Covid 1 pandemic

library(COVID19)

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library(covid19.analytics)

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library(coronavirus)

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library("RTutorEconomicImpactsofCOVID19")

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 #obtain all the records combined for "confirmed", "deaths" and "recovered" cases  *aggregated* data
 covid19.data.ALLcases <- covid19.data()

## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

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## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

# obtain time series data for "confirmed" cases
 covid19.confirmed.cases <- covid19.data("ts-confirmed")

## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:26:26 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

# reads all possible datasets, returning a list
 covid19.all.datasets <- covid19.data("ALL")

## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:26:29 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

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## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:26:31 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

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## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:26:33 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

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## Data being read from *local* repo in the 'covid19.analytics' package

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## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/time_series_19-covid-Confirmed.csv.RDS

## Data retrieved on 2022-02-25 06:26:33 || Range of dates on data: 2020-01-22--2020-03-23 | Nbr of records: 501

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## Data being read from *local* repo in the 'covid19.analytics' package

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## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/time_series_19-covid-Deaths.csv.RDS
## Data retrieved on 2022-02-25 06:26:33 || Range of dates on data: 2020-01-22--2020-03-23 | Nbr of records: 501

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## Data being read from *local* repo in the 'covid19.analytics' package

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## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/time_series_19-covid-Recovered.csv.RDS
## Data retrieved on 2022-02-25 06:26:33 || Range of dates on data: 2020-01-22--2020-03-23 | Nbr of records: 501

## --------------------------------------------------------------------------------

# reads the latest aggregated data
 covid19.ALL.agg.cases <- covid19.data("aggregated")

## Data being read from JHU/CCSE repository

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## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

# reads time series data for casualties
 covid19.TS.deaths <- covid19.data("ts-deaths")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:26:35 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

# reads testing data
 testing.data <- covid19.testing.data()

## Data obtained from OWID repo:
## https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/testing/covid-testing-all-observations.csv

 # obtain covid19's genomic data
 covid19.gen.seq <- covid19.genomic.data()

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## Retrieving data from NCBI...

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# display the actual RNA seq
 covid19.gen.seq$NC_045512.2

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##  [1135] "g" "a" "a" "a" "a" "a" "g" "c" "t" "t" "g" "a" "t" "g" "g" "c" "t" "t"
##  [1153] "t" "a" "t" "g" "g" "g" "t" "a" "g" "a" "a" "t" "t" "c" "g" "a" "t" "c"
##  [1171] "t" "g" "t" "c" "t" "a" "t" "c" "c" "a" "g" "t" "t" "g" "c" "g" "t" "c"
##  [1189] "a" "c" "c" "a" "a" "a" "t" "g" "a" "a" "t" "g" "c" "a" "a" "c" "c" "a"
##  [1207] "a" "a" "t" "g" "t" "g" "c" "c" "t" "t" "t" "c" "a" "a" "c" "t" "c" "t"
##  [1225] "c" "a" "t" "g" "a" "a" "g" "t" "g" "t" "g" "a" "t" "c" "a" "t" "t" "g"
##  [1243] "t" "g" "g" "t" "g" "a" "a" "a" "c" "t" "t" "c" "a" "t" "g" "g" "c" "a"
##  [1261] "g" "a" "c" "g" "g" "g" "c" "g" "a" "t" "t" "t" "t" "g" "t" "t" "a" "a"
##  [1279] "a" "g" "c" "c" "a" "c" "t" "t" "g" "c" "g" "a" "a" "t" "t" "t" "t" "g"
##  [1297] "t" "g" "g" "c" "a" "c" "t" "g" "a" "g" "a" "a" "t" "t" "t" "g" "a" "c"
##  [1315] "t" "a" "a" "a" "g" "a" "a" "g" "g" "t" "g" "c" "c" "a" "c" "t" "a" "c"
##  [1333] "t" "t" "g" "t" "g" "g" "t" "t" "a" "c" "t" "t" "a" "c" "c" "c" "c" "a"
##  [1351] "a" "a" "a" "t" "g" "c" "t" "g" "t" "t" "g" "t" "t" "a" "a" "a" "a" "t"
##  [1369] "t" "t" "a" "t" "t" "g" "t" "c" "c" "a" "g" "c" "a" "t" "g" "t" "c" "a"
##  [1387] "c" "a" "a" "t" "t" "c" "a" "g" "a" "a" "g" "t" "a" "g" "g" "a" "c" "c"
##  [1405] "t" "g" "a" "g" "c" "a" "t" "a" "g" "t" "c" "t" "t" "g" "c" "c" "g" "a"
##  [1423] "a" "t" "a" "c" "c" "a" "t" "a" "a" "t" "g" "a" "a" "t" "c" "t" "g" "g"
##  [1441] "c" "t" "t" "g" "a" "a" "a" "a" "c" "c" "a" "t" "t" "c" "t" "t" "c" "g"
##  [1459] "t" "a" "a" "g" "g" "g" "t" "g" "g" "t" "c" "g" "c" "a" "c" "t" "a" "t"
##  [1477] "t" "g" "c" "c" "t" "t" "t" "g" "g" "a" "g" "g" "c" "t" "g" "t" "g" "t"
##  [1495] "g" "t" "t" "c" "t" "c" "t" "t" "a" "t" "g" "t" "t" "g" "g" "t" "t" "g"
##  [1513] "c" "c" "a" "t" "a" "a" "c" "a" "a" "g" "t" "g" "t" "g" "c" "c" "t" "a"
##  [1531] "t" "t" "g" "g" "g" "t" "t" "c" "c" "a" "c" "g" "t" "g" "c" "t" "a" "g"
##  [1549] "c" "g" "c" "t" "a" "a" "c" "a" "t" "a" "g" "g" "t" "t" "g" "t" "a" "a"
##  [1567] "c" "c" "a" "t" "a" "c" "a" "g" "g" "t" "g" "t" "t" "g" "t" "t" "g" "g"
##  [1585] "a" "g" "a" "a" "g" "g" "t" "t" "c" "c" "g" "a" "a" "g" "g" "t" "c" "t"
##  [1603] "t" "a" "a" "t" "g" "a" "c" "a" "a" "c" "c" "t" "t" "c" "t" "t" "g" "a"
##  [1621] "a" "a" "t" "a" "c" "t" "c" "c" "a" "a" "a" "a" "a" "g" "a" "g" "a" "a"
##  [1639] "a" "g" "t" "c" "a" "a" "c" "a" "t" "c" "a" "a" "t" "a" "t" "t" "g" "t"
##  [1657] "t" "g" "g" "t" "g" "a" "c" "t" "t" "t" "a" "a" "a" "c" "t" "t" "a" "a"
##  [1675] "t" "g" "a" "a" "g" "a" "g" "a" "t" "c" "g" "c" "c" "a" "t" "t" "a" "t"
##  [1693] "t" "t" "t" "g" "g" "c" "a" "t" "c" "t" "t" "t" "t" "t" "c" "t" "g" "c"
##  [1711] "t" "t" "c" "c" "a" "c" "a" "a" "g" "t" "g" "c" "t" "t" "t" "t" "g" "t"
##  [1729] "g" "g" "a" "a" "a" "c" "t" "g" "t" "g" "a" "a" "a" "g" "g" "t" "t" "t"
##  [1747] "g" "g" "a" "t" "t" "a" "t" "a" "a" "a" "g" "c" "a" "t" "t" "c" "a" "a"
##  [1765] "a" "c" "a" "a" "a" "t" "t" "g" "t" "t" "g" "a" "a" "t" "c" "c" "t" "g"
##  [1783] "t" "g" "g" "t" "a" "a" "t" "t" "t" "t" "a" "a" "a" "g" "t" "t" "a" "c"
##  [1801] "a" "a" "a" "a" "g" "g" "a" "a" "a" "a" "g" "c" "t" "a" "a" "a" "a" "a"
##  [1819] "a" "g" "g" "t" "g" "c" "c" "t" "g" "g" "a" "a" "t" "a" "t" "t" "g" "g"
##  [1837] "t" "g" "a" "a" "c" "a" "g" "a" "a" "a" "t" "c" "a" "a" "t" "a" "c" "t"
##  [1855] "g" "a" "g" "t" "c" "c" "t" "c" "t" "t" "t" "a" "t" "g" "c" "a" "t" "t"
##  [1873] "t" "g" "c" "a" "t" "c" "a" "g" "a" "g" "g" "c" "t" "g" "c" "t" "c" "g"
##  [1891] "t" "g" "t" "t" "g" "t" "a" "c" "g" "a" "t" "c" "a" "a" "t" "t" "t" "t"
##  [1909] "c" "t" "c" "c" "c" "g" "c" "a" "c" "t" "c" "t" "t" "g" "a" "a" "a" "c"
##  [1927] "t" "g" "c" "t" "c" "a" "a" "a" "a" "t" "t" "c" "t" "g" "t" "g" "c" "g"
##  [1945] "t" "g" "t" "t" "t" "t" "a" "c" "a" "g" "a" "a" "g" "g" "c" "c" "g" "c"
##  [1963] "t" "a" "t" "a" "a" "c" "a" "a" "t" "a" "c" "t" "a" "g" "a" "t" "g" "g"
##  [1981] "a" "a" "t" "t" "t" "c" "a" "c" "a" "g" "t" "a" "t" "t" "c" "a" "c" "t"
##  [1999] "g" "a" "g" "a" "c" "t" "c" "a" "t" "t" "g" "a" "t" "g" "c" "t" "a" "t"
##  [2017] "g" "a" "t" "g" "t" "t" "c" "a" "c" "a" "t" "c" "t" "g" "a" "t" "t" "t"
##  [2035] "g" "g" "c" "t" "a" "c" "t" "a" "a" "c" "a" "a" "t" "c" "t" "a" "g" "t"
##  [2053] "t" "g" "t" "a" "a" "t" "g" "g" "c" "c" "t" "a" "c" "a" "t" "t" "a" "c"
##  [2071] "a" "g" "g" "t" "g" "g" "t" "g" "t" "t" "g" "t" "t" "c" "a" "g" "t" "t"
##  [2089] "g" "a" "c" "t" "t" "c" "g" "c" "a" "g" "t" "g" "g" "c" "t" "a" "a" "c"
##  [2107] "t" "a" "a" "c" "a" "t" "c" "t" "t" "t" "g" "g" "c" "a" "c" "t" "g" "t"
##  [2125] "t" "t" "a" "t" "g" "a" "a" "a" "a" "a" "c" "t" "c" "a" "a" "a" "c" "c"
##  [2143] "c" "g" "t" "c" "c" "t" "t" "g" "a" "t" "t" "g" "g" "c" "t" "t" "g" "a"
##  [2161] "a" "g" "a" "g" "a" "a" "g" "t" "t" "t" "a" "a" "g" "g" "a" "a" "g" "g"
##  [2179] "t" "g" "t" "a" "g" "a" "g" "t" "t" "t" "c" "t" "t" "a" "g" "a" "g" "a"
##  [2197] "c" "g" "g" "t" "t" "g" "g" "g" "a" "a" "a" "t" "t" "g" "t" "t" "a" "a"
##  [2215] "a" "t" "t" "t" "a" "t" "c" "t" "c" "a" "a" "c" "c" "t" "g" "t" "g" "c"
##  [2233] "t" "t" "g" "t" "g" "a" "a" "a" "t" "t" "g" "t" "c" "g" "g" "t" "g" "g"
##  [2251] "a" "c" "a" "a" "a" "t" "t" "g" "t" "c" "a" "c" "c" "t" "g" "t" "g" "c"
##  [2269] "a" "a" "a" "g" "g" "a" "a" "a" "t" "t" "a" "a" "g" "g" "a" "g" "a" "g"
##  [2287] "t" "g" "t" "t" "c" "a" "g" "a" "c" "a" "t" "t" "c" "t" "t" "t" "a" "a"
##  [2305] "g" "c" "t" "t" "g" "t" "a" "a" "a" "t" "a" "a" "a" "t" "t" "t" "t" "t"
##  [2323] "g" "g" "c" "t" "t" "t" "g" "t" "g" "t" "g" "c" "t" "g" "a" "c" "t" "c"
##  [2341] "t" "a" "t" "c" "a" "t" "t" "a" "t" "t" "g" "g" "t" "g" "g" "a" "g" "c"
##  [2359] "t" "a" "a" "a" "c" "t" "t" "a" "a" "a" "g" "c" "c" "t" "t" "g" "a" "a"
##  [2377] "t" "t" "t" "a" "g" "g" "t" "g" "a" "a" "a" "c" "a" "t" "t" "t" "g" "t"
##  [2395] "c" "a" "c" "g" "c" "a" "c" "t" "c" "a" "a" "a" "g" "g" "g" "a" "t" "t"
##  [2413] "g" "t" "a" "c" "a" "g" "a" "a" "a" "g" "t" "g" "t" "g" "t" "t" "a" "a"
##  [2431] "a" "t" "c" "c" "a" "g" "a" "g" "a" "a" "g" "a" "a" "a" "c" "t" "g" "g"
##  [2449] "c" "c" "t" "a" "c" "t" "c" "a" "t" "g" "c" "c" "t" "c" "t" "a" "a" "a"
##  [2467] "a" "g" "c" "c" "c" "c" "a" "a" "a" "a" "g" "a" "a" "a" "t" "t" "a" "t"
##  [2485] "c" "t" "t" "c" "t" "t" "a" "g" "a" "g" "g" "g" "a" "g" "a" "a" "a" "c"
##  [2503] "a" "c" "t" "t" "c" "c" "c" "a" "c" "a" "g" "a" "a" "g" "t" "g" "t" "t"
##  [2521] "a" "a" "c" "a" "g" "a" "g" "g" "a" "a" "g" "t" "t" "g" "t" "c" "t" "t"
##  [2539] "g" "a" "a" "a" "a" "c" "t" "g" "g" "t" "g" "a" "t" "t" "t" "a" "c" "a"
##  [2557] "a" "c" "c" "a" "t" "t" "a" "g" "a" "a" "c" "a" "a" "c" "c" "t" "a" "c"
##  [2575] "t" "a" "g" "t" "g" "a" "a" "g" "c" "t" "g" "t" "t" "g" "a" "a" "g" "c"
##  [2593] "t" "c" "c" "a" "t" "t" "g" "g" "t" "t" "g" "g" "t" "a" "c" "a" "c" "c"
##  [2611] "a" "g" "t" "t" "t" "g" "t" "a" "t" "t" "a" "a" "c" "g" "g" "g" "c" "t"
##  [2629] "t" "a" "t" "g" "t" "t" "g" "c" "t" "c" "g" "a" "a" "a" "t" "c" "a" "a"
##  [2647] "a" "g" "a" "c" "a" "c" "a" "g" "a" "a" "a" "a" "g" "t" "a" "c" "t" "g"
##  [2665] "t" "g" "c" "c" "c" "t" "t" "g" "c" "a" "c" "c" "t" "a" "a" "t" "a" "t"
##  [2683] "g" "a" "t" "g" "g" "t" "a" "a" "c" "a" "a" "a" "c" "a" "a" "t" "a" "c"
##  [2701] "c" "t" "t" "c" "a" "c" "a" "c" "t" "c" "a" "a" "a" "g" "g" "c" "g" "g"
##  [2719] "t" "g" "c" "a" "c" "c" "a" "a" "c" "a" "a" "a" "g" "g" "t" "t" "a" "c"
##  [2737] "t" "t" "t" "t" "g" "g" "t" "g" "a" "t" "g" "a" "c" "a" "c" "t" "g" "t"
##  [2755] "g" "a" "t" "a" "g" "a" "a" "g" "t" "g" "c" "a" "a" "g" "g" "t" "t" "a"
##  [2773] "c" "a" "a" "g" "a" "g" "t" "g" "t" "g" "a" "a" "t" "a" "t" "c" "a" "c"
##  [2791] "t" "t" "t" "t" "g" "a" "a" "c" "t" "t" "g" "a" "t" "g" "a" "a" "a" "g"
##  [2809] "g" "a" "t" "t" "g" "a" "t" "a" "a" "a" "g" "t" "a" "c" "t" "t" "a" "a"
##  [2827] "t" "g" "a" "g" "a" "a" "g" "t" "g" "c" "t" "c" "t" "g" "c" "c" "t" "a"
##  [2845] "t" "a" "c" "a" "g" "t" "t" "g" "a" "a" "c" "t" "c" "g" "g" "t" "a" "c"
##  [2863] "a" "g" "a" "a" "g" "t" "a" "a" "a" "t" "g" "a" "g" "t" "t" "c" "g" "c"
##  [2881] "c" "t" "g" "t" "g" "t" "t" "g" "t" "g" "g" "c" "a" "g" "a" "t" "g" "c"
##  [2899] "t" "g" "t" "c" "a" "t" "a" "a" "a" "a" "a" "c" "t" "t" "t" "g" "c" "a"
##  [2917] "a" "c" "c" "a" "g" "t" "a" "t" "c" "t" "g" "a" "a" "t" "t" "a" "c" "t"
##  [2935] "t" "a" "c" "a" "c" "c" "a" "c" "t" "g" "g" "g" "c" "a" "t" "t" "g" "a"
##  [2953] "t" "t" "t" "a" "g" "a" "t" "g" "a" "g" "t" "g" "g" "a" "g" "t" "a" "t"
##  [2971] "g" "g" "c" "t" "a" "c" "a" "t" "a" "c" "t" "a" "c" "t" "t" "a" "t" "t"
##  [2989] "t" "g" "a" "t" "g" "a" "g" "t" "c" "t" "g" "g" "t" "g" "a" "g" "t" "t"
##  [3007] "t" "a" "a" "a" "t" "t" "g" "g" "c" "t" "t" "c" "a" "c" "a" "t" "a" "t"
##  [3025] "g" "t" "a" "t" "t" "g" "t" "t" "c" "t" "t" "t" "c" "t" "a" "c" "c" "c"
##  [3043] "t" "c" "c" "a" "g" "a" "t" "g" "a" "g" "g" "a" "t" "g" "a" "a" "g" "a"
##  [3061] "a" "g" "a" "a" "g" "g" "t" "g" "a" "t" "t" "g" "t" "g" "a" "a" "g" "a"
##  [3079] "a" "g" "a" "a" "g" "a" "g" "t" "t" "t" "g" "a" "g" "c" "c" "a" "t" "c"
##  [3097] "a" "a" "c" "t" "c" "a" "a" "t" "a" "t" "g" "a" "g" "t" "a" "t" "g" "g"
##  [3115] "t" "a" "c" "t" "g" "a" "a" "g" "a" "t" "g" "a" "t" "t" "a" "c" "c" "a"
##  [3133] "a" "g" "g" "t" "a" "a" "a" "c" "c" "t" "t" "t" "g" "g" "a" "a" "t" "t"
##  [3151] "t" "g" "g" "t" "g" "c" "c" "a" "c" "t" "t" "c" "t" "g" "c" "t" "g" "c"
##  [3169] "t" "c" "t" "t" "c" "a" "a" "c" "c" "t" "g" "a" "a" "g" "a" "a" "g" "a"
##  [3187] "g" "c" "a" "a" "g" "a" "a" "g" "a" "a" "g" "a" "t" "t" "g" "g" "t" "t"
##  [3205] "a" "g" "a" "t" "g" "a" "t" "g" "a" "t" "a" "g" "t" "c" "a" "a" "c" "a"
##  [3223] "a" "a" "c" "t" "g" "t" "t" "g" "g" "t" "c" "a" "a" "c" "a" "a" "g" "a"
##  [3241] "c" "g" "g" "c" "a" "g" "t" "g" "a" "g" "g" "a" "c" "a" "a" "t" "c" "a"
##  [3259] "g" "a" "c" "a" "a" "c" "t" "a" "c" "t" "a" "t" "t" "c" "a" "a" "a" "c"
##  [3277] "a" "a" "t" "t" "g" "t" "t" "g" "a" "g" "g" "t" "t" "c" "a" "a" "c" "c"
##  [3295] "t" "c" "a" "a" "t" "t" "a" "g" "a" "g" "a" "t" "g" "g" "a" "a" "c" "t"
##  [3313] "t" "a" "c" "a" "c" "c" "a" "g" "t" "t" "g" "t" "t" "c" "a" "g" "a" "c"
##  [3331] "t" "a" "t" "t" "g" "a" "a" "g" "t" "g" "a" "a" "t" "a" "g" "t" "t" "t"
##  [3349] "t" "a" "g" "t" "g" "g" "t" "t" "a" "t" "t" "t" "a" "a" "a" "a" "c" "t"
##  [3367] "t" "a" "c" "t" "g" "a" "c" "a" "a" "t" "g" "t" "a" "t" "a" "c" "a" "t"
##  [3385] "t" "a" "a" "a" "a" "a" "t" "g" "c" "a" "g" "a" "c" "a" "t" "t" "g" "t"
##  [3403] "g" "g" "a" "a" "g" "a" "a" "g" "c" "t" "a" "a" "a" "a" "a" "g" "g" "t"
##  [3421] "a" "a" "a" "a" "c" "c" "a" "a" "c" "a" "g" "t" "g" "g" "t" "t" "g" "t"
##  [3439] "t" "a" "a" "t" "g" "c" "a" "g" "c" "c" "a" "a" "t" "g" "t" "t" "t" "a"
##  [3457] "c" "c" "t" "t" "a" "a" "a" "c" "a" "t" "g" "g" "a" "g" "g" "a" "g" "g"
##  [3475] "t" "g" "t" "t" "g" "c" "a" "g" "g" "a" "g" "c" "c" "t" "t" "a" "a" "a"
##  [3493] "t" "a" "a" "g" "g" "c" "t" "a" "c" "t" "a" "a" "c" "a" "a" "t" "g" "c"
##  [3511] "c" "a" "t" "g" "c" "a" "a" "g" "t" "t" "g" "a" "a" "t" "c" "t" "g" "a"
##  [3529] "t" "g" "a" "t" "t" "a" "c" "a" "t" "a" "g" "c" "t" "a" "c" "t" "a" "a"
##  [3547] "t" "g" "g" "a" "c" "c" "a" "c" "t" "t" "a" "a" "a" "g" "t" "g" "g" "g"
##  [3565] "t" "g" "g" "t" "a" "g" "t" "t" "g" "t" "g" "t" "t" "t" "t" "a" "a" "g"
##  [3583] "c" "g" "g" "a" "c" "a" "c" "a" "a" "t" "c" "t" "t" "g" "c" "t" "a" "a"
##  [3601] "a" "c" "a" "c" "t" "g" "t" "c" "t" "t" "c" "a" "t" "g" "t" "t" "g" "t"
##  [3619] "c" "g" "g" "c" "c" "c" "a" "a" "a" "t" "g" "t" "t" "a" "a" "c" "a" "a"
##  [3637] "a" "g" "g" "t" "g" "a" "a" "g" "a" "c" "a" "t" "t" "c" "a" "a" "c" "t"
##  [3655] "t" "c" "t" "t" "a" "a" "g" "a" "g" "t" "g" "c" "t" "t" "a" "t" "g" "a"
##  [3673] "a" "a" "a" "t" "t" "t" "t" "a" "a" "t" "c" "a" "g" "c" "a" "c" "g" "a"
##  [3691] "a" "g" "t" "t" "c" "t" "a" "c" "t" "t" "g" "c" "a" "c" "c" "a" "t" "t"
##  [3709] "a" "t" "t" "a" "t" "c" "a" "g" "c" "t" "g" "g" "t" "a" "t" "t" "t" "t"
##  [3727] "t" "g" "g" "t" "g" "c" "t" "g" "a" "c" "c" "c" "t" "a" "t" "a" "c" "a"
##  [3745] "t" "t" "c" "t" "t" "t" "a" "a" "g" "a" "g" "t" "t" "t" "g" "t" "g" "t"
##  [3763] "a" "g" "a" "t" "a" "c" "t" "g" "t" "t" "c" "g" "c" "a" "c" "a" "a" "a"
##  [3781] "t" "g" "t" "c" "t" "a" "c" "t" "t" "a" "g" "c" "t" "g" "t" "c" "t" "t"
##  [3799] "t" "g" "a" "t" "a" "a" "a" "a" "a" "t" "c" "t" "c" "t" "a" "t" "g" "a"
##  [3817] "c" "a" "a" "a" "c" "t" "t" "g" "t" "t" "t" "c" "a" "a" "g" "c" "t" "t"
##  [3835] "t" "t" "t" "g" "g" "a" "a" "a" "t" "g" "a" "a" "g" "a" "g" "t" "g" "a"
##  [3853] "a" "a" "a" "g" "c" "a" "a" "g" "t" "t" "g" "a" "a" "c" "a" "a" "a" "a"
##  [3871] "g" "a" "t" "c" "g" "c" "t" "g" "a" "g" "a" "t" "t" "c" "c" "t" "a" "a"
##  [3889] "a" "g" "a" "g" "g" "a" "a" "g" "t" "t" "a" "a" "g" "c" "c" "a" "t" "t"
##  [3907] "t" "a" "t" "a" "a" "c" "t" "g" "a" "a" "a" "g" "t" "a" "a" "a" "c" "c"
##  [3925] "t" "t" "c" "a" "g" "t" "t" "g" "a" "a" "c" "a" "g" "a" "g" "a" "a" "a"
##  [3943] "a" "c" "a" "a" "g" "a" "t" "g" "a" "t" "a" "a" "g" "a" "a" "a" "a" "t"
##  [3961] "c" "a" "a" "a" "g" "c" "t" "t" "g" "t" "g" "t" "t" "g" "a" "a" "g" "a"
##  [3979] "a" "g" "t" "t" "a" "c" "a" "a" "c" "a" "a" "c" "t" "c" "t" "g" "g" "a"
##  [3997] "a" "g" "a" "a" "a" "c" "t" "a" "a" "g" "t" "t" "c" "c" "t" "c" "a" "c"
##  [4015] "a" "g" "a" "a" "a" "a" "c" "t" "t" "g" "t" "t" "a" "c" "t" "t" "t" "a"
##  [4033] "t" "a" "t" "t" "g" "a" "c" "a" "t" "t" "a" "a" "t" "g" "g" "c" "a" "a"
##  [4051] "t" "c" "t" "t" "c" "a" "t" "c" "c" "a" "g" "a" "t" "t" "c" "t" "g" "c"
##  [4069] "c" "a" "c" "t" "c" "t" "t" "g" "t" "t" "a" "g" "t" "g" "a" "c" "a" "t"
##  [4087] "t" "g" "a" "c" "a" "t" "c" "a" "c" "t" "t" "t" "c" "t" "t" "a" "a" "a"
##  [4105] "g" "a" "a" "a" "g" "a" "t" "g" "c" "t" "c" "c" "a" "t" "a" "t" "a" "t"
##  [4123] "a" "g" "t" "g" "g" "g" "t" "g" "a" "t" "g" "t" "t" "g" "t" "t" "c" "a"
##  [4141] "a" "g" "a" "g" "g" "g" "t" "g" "t" "t" "t" "t" "a" "a" "c" "t" "g" "c"
##  [4159] "t" "g" "t" "g" "g" "t" "t" "a" "t" "a" "c" "c" "t" "a" "c" "t" "a" "a"
##  [4177] "a" "a" "a" "g" "g" "c" "t" "g" "g" "t" "g" "g" "c" "a" "c" "t" "a" "c"
##  [4195] "t" "g" "a" "a" "a" "t" "g" "c" "t" "a" "g" "c" "g" "a" "a" "a" "g" "c"
##  [4213] "t" "t" "t" "g" "a" "g" "a" "a" "a" "a" "g" "t" "g" "c" "c" "a" "a" "c"
##  [4231] "a" "g" "a" "c" "a" "a" "t" "t" "a" "t" "a" "t" "a" "a" "c" "c" "a" "c"
##  [4249] "t" "t" "a" "c" "c" "c" "g" "g" "g" "t" "c" "a" "g" "g" "g" "t" "t" "t"
##  [4267] "a" "a" "a" "t" "g" "g" "t" "t" "a" "c" "a" "c" "t" "g" "t" "a" "g" "a"
##  [4285] "g" "g" "a" "g" "g" "c" "a" "a" "a" "g" "a" "c" "a" "g" "t" "g" "c" "t"
##  [4303] "t" "a" "a" "a" "a" "a" "g" "t" "g" "t" "a" "a" "a" "a" "g" "t" "g" "c"
##  [4321] "c" "t" "t" "t" "t" "a" "c" "a" "t" "t" "c" "t" "a" "c" "c" "a" "t" "c"
##  [4339] "t" "a" "t" "t" "a" "t" "c" "t" "c" "t" "a" "a" "t" "g" "a" "g" "a" "a"
##  [4357] "g" "c" "a" "a" "g" "a" "a" "a" "t" "t" "c" "t" "t" "g" "g" "a" "a" "c"
##  [4375] "t" "g" "t" "t" "t" "c" "t" "t" "g" "g" "a" "a" "t" "t" "t" "g" "c" "g"
##  [4393] "a" "g" "a" "a" "a" "t" "g" "c" "t" "t" "g" "c" "a" "c" "a" "t" "g" "c"
##  [4411] "a" "g" "a" "a" "g" "a" "a" "a" "c" "a" "c" "g" "c" "a" "a" "a" "t" "t"
##  [4429] "a" "a" "t" "g" "c" "c" "t" "g" "t" "c" "t" "g" "t" "g" "t" "g" "g" "a"
##  [4447] "a" "a" "c" "t" "a" "a" "a" "g" "c" "c" "a" "t" "a" "g" "t" "t" "t" "c"
##  [4465] "a" "a" "c" "t" "a" "t" "a" "c" "a" "g" "c" "g" "t" "a" "a" "a" "t" "a"
##  [4483] "t" "a" "a" "g" "g" "g" "t" "a" "t" "t" "a" "a" "a" "a" "t" "a" "c" "a"
##  [4501] "a" "g" "a" "g" "g" "g" "t" "g" "t" "g" "g" "t" "t" "g" "a" "t" "t" "a"
##  [4519] "t" "g" "g" "t" "g" "c" "t" "a" "g" "a" "t" "t" "t" "t" "a" "c" "t" "t"
##  [4537] "t" "t" "a" "c" "a" "c" "c" "a" "g" "t" "a" "a" "a" "a" "c" "a" "a" "c"
##  [4555] "t" "g" "t" "a" "g" "c" "g" "t" "c" "a" "c" "t" "t" "a" "t" "c" "a" "a"
##  [4573] "c" "a" "c" "a" "c" "t" "t" "a" "a" "c" "g" "a" "t" "c" "t" "a" "a" "a"
##  [4591] "t" "g" "a" "a" "a" "c" "t" "c" "t" "t" "g" "t" "t" "a" "c" "a" "a" "t"
##  [4609] "g" "c" "c" "a" "c" "t" "t" "g" "g" "c" "t" "a" "t" "g" "t" "a" "a" "c"
##  [4627] "a" "c" "a" "t" "g" "g" "c" "t" "t" "a" "a" "a" "t" "t" "t" "g" "g" "a"
##  [4645] "a" "g" "a" "a" "g" "c" "t" "g" "c" "t" "c" "g" "g" "t" "a" "t" "a" "t"
##  [4663] "g" "a" "g" "a" "t" "c" "t" "c" "t" "c" "a" "a" "a" "g" "t" "g" "c" "c"
##  [4681] "a" "g" "c" "t" "a" "c" "a" "g" "t" "t" "t" "c" "t" "g" "t" "t" "t" "c"
##  [4699] "t" "t" "c" "a" "c" "c" "t" "g" "a" "t" "g" "c" "t" "g" "t" "t" "a" "c"
##  [4717] "a" "g" "c" "g" "t" "a" "t" "a" "a" "t" "g" "g" "t" "t" "a" "t" "c" "t"
##  [4735] "t" "a" "c" "t" "t" "c" "t" "t" "c" "t" "t" "c" "t" "a" "a" "a" "a" "c"
##  [4753] "a" "c" "c" "t" "g" "a" "a" "g" "a" "a" "c" "a" "t" "t" "t" "t" "a" "t"
##  [4771] "t" "g" "a" "a" "a" "c" "c" "a" "t" "c" "t" "c" "a" "c" "t" "t" "g" "c"
##  [4789] "t" "g" "g" "t" "t" "c" "c" "t" "a" "t" "a" "a" "a" "g" "a" "t" "t" "g"
##  [4807] "g" "t" "c" "c" "t" "a" "t" "t" "c" "t" "g" "g" "a" "c" "a" "a" "t" "c"
##  [4825] "t" "a" "c" "a" "c" "a" "a" "c" "t" "a" "g" "g" "t" "a" "t" "a" "g" "a"
##  [4843] "a" "t" "t" "t" "c" "t" "t" "a" "a" "g" "a" "g" "a" "g" "g" "t" "g" "a"
##  [4861] "t" "a" "a" "a" "a" "g" "t" "g" "t" "a" "t" "a" "t" "t" "a" "c" "a" "c"
##  [4879] "t" "a" "g" "t" "a" "a" "t" "c" "c" "t" "a" "c" "c" "a" "c" "a" "t" "t"
##  [4897] "c" "c" "a" "c" "c" "t" "a" "g" "a" "t" "g" "g" "t" "g" "a" "a" "g" "t"
##  [4915] "t" "a" "t" "c" "a" "c" "c" "t" "t" "t" "g" "a" "c" "a" "a" "t" "c" "t"
##  [4933] "t" "a" "a" "g" "a" "c" "a" "c" "t" "t" "c" "t" "t" "t" "c" "t" "t" "t"
##  [4951] "g" "a" "g" "a" "g" "a" "a" "g" "t" "g" "a" "g" "g" "a" "c" "t" "a" "t"
##  [4969] "t" "a" "a" "g" "g" "t" "g" "t" "t" "t" "a" "c" "a" "a" "c" "a" "g" "t"
##  [4987] "a" "g" "a" "c" "a" "a" "c" "a" "t" "t" "a" "a" "c" "c" "t" "c" "c" "a"
##  [5005] "c" "a" "c" "g" "c" "a" "a" "g" "t" "t" "g" "t" "g" "g" "a" "c" "a" "t"
##  [5023] "g" "t" "c" "a" "a" "t" "g" "a" "c" "a" "t" "a" "t" "g" "g" "a" "c" "a"
##  [5041] "a" "c" "a" "g" "t" "t" "t" "g" "g" "t" "c" "c" "a" "a" "c" "t" "t" "a"
##  [5059] "t" "t" "t" "g" "g" "a" "t" "g" "g" "a" "g" "c" "t" "g" "a" "t" "g" "t"
##  [5077] "t" "a" "c" "t" "a" "a" "a" "a" "t" "a" "a" "a" "a" "c" "c" "t" "c" "a"
##  [5095] "t" "a" "a" "t" "t" "c" "a" "c" "a" "t" "g" "a" "a" "g" "g" "t" "a" "a"
##  [5113] "a" "a" "c" "a" "t" "t" "t" "t" "a" "t" "g" "t" "t" "t" "t" "a" "c" "c"
##  [5131] "t" "a" "a" "t" "g" "a" "t" "g" "a" "c" "a" "c" "t" "c" "t" "a" "c" "g"
##  [5149] "t" "g" "t" "t" "g" "a" "g" "g" "c" "t" "t" "t" "t" "g" "a" "g" "t" "a"
##  [5167] "c" "t" "a" "c" "c" "a" "c" "a" "c" "a" "a" "c" "t" "g" "a" "t" "c" "c"
##  [5185] "t" "a" "g" "t" "t" "t" "t" "c" "t" "g" "g" "g" "t" "a" "g" "g" "t" "a"
##  [5203] "c" "a" "t" "g" "t" "c" "a" "g" "c" "a" "t" "t" "a" "a" "a" "t" "c" "a"
##  [5221] "c" "a" "c" "t" "a" "a" "a" "a" "a" "g" "t" "g" "g" "a" "a" "a" "t" "a"
##  [5239] "c" "c" "c" "a" "c" "a" "a" "g" "t" "t" "a" "a" "t" "g" "g" "t" "t" "t"
##  [5257] "a" "a" "c" "t" "t" "c" "t" "a" "t" "t" "a" "a" "a" "t" "g" "g" "g" "c"
##  [5275] "a" "g" "a" "t" "a" "a" "c" "a" "a" "c" "t" "g" "t" "t" "a" "t" "c" "t"
##  [5293] "t" "g" "c" "c" "a" "c" "t" "g" "c" "a" "t" "t" "g" "t" "t" "a" "a" "c"
##  [5311] "a" "c" "t" "c" "c" "a" "a" "c" "a" "a" "a" "t" "a" "g" "a" "g" "t" "t"
##  [5329] "g" "a" "a" "g" "t" "t" "t" "a" "a" "t" "c" "c" "a" "c" "c" "t" "g" "c"
##  [5347] "t" "c" "t" "a" "c" "a" "a" "g" "a" "t" "g" "c" "t" "t" "a" "t" "t" "a"
##  [5365] "c" "a" "g" "a" "g" "c" "a" "a" "g" "g" "g" "c" "t" "g" "g" "t" "g" "a"
##  [5383] "a" "g" "c" "t" "g" "c" "t" "a" "a" "c" "t" "t" "t" "t" "g" "t" "g" "c"
##  [5401] "a" "c" "t" "t" "a" "t" "c" "t" "t" "a" "g" "c" "c" "t" "a" "c" "t" "g"
##  [5419] "t" "a" "a" "t" "a" "a" "g" "a" "c" "a" "g" "t" "a" "g" "g" "t" "g" "a"
##  [5437] "g" "t" "t" "a" "g" "g" "t" "g" "a" "t" "g" "t" "t" "a" "g" "a" "g" "a"
##  [5455] "a" "a" "c" "a" "a" "t" "g" "a" "g" "t" "t" "a" "c" "t" "t" "g" "t" "t"
##  [5473] "t" "c" "a" "a" "c" "a" "t" "g" "c" "c" "a" "a" "t" "t" "t" "a" "g" "a"
##  [5491] "t" "t" "c" "t" "t" "g" "c" "a" "a" "a" "a" "g" "a" "g" "t" "c" "t" "t"
##  [5509] "g" "a" "a" "c" "g" "t" "g" "g" "t" "g" "t" "g" "t" "a" "a" "a" "a" "c"
##  [5527] "t" "t" "g" "t" "g" "g" "a" "c" "a" "a" "c" "a" "g" "c" "a" "g" "a" "c"
##  [5545] "a" "a" "c" "c" "c" "t" "t" "a" "a" "g" "g" "g" "t" "g" "t" "a" "g" "a"
##  [5563] "a" "g" "c" "t" "g" "t" "t" "a" "t" "g" "t" "a" "c" "a" "t" "g" "g" "g"
##  [5581] "c" "a" "c" "a" "c" "t" "t" "t" "c" "t" "t" "a" "t" "g" "a" "a" "c" "a"
##  [5599] "a" "t" "t" "t" "a" "a" "g" "a" "a" "a" "g" "g" "t" "g" "t" "t" "c" "a"
##  [5617] "g" "a" "t" "a" "c" "c" "t" "t" "g" "t" "a" "c" "g" "t" "g" "t" "g" "g"
##  [5635] "t" "a" "a" "a" "c" "a" "a" "g" "c" "t" "a" "c" "a" "a" "a" "a" "t" "a"
##  [5653] "t" "c" "t" "a" "g" "t" "a" "c" "a" "a" "c" "a" "g" "g" "a" "g" "t" "c"
##  [5671] "a" "c" "c" "t" "t" "t" "t" "g" "t" "t" "a" "t" "g" "a" "t" "g" "t" "c"
##  [5689] "a" "g" "c" "a" "c" "c" "a" "c" "c" "t" "g" "c" "t" "c" "a" "g" "t" "a"
##  [5707] "t" "g" "a" "a" "c" "t" "t" "a" "a" "g" "c" "a" "t" "g" "g" "t" "a" "c"
##  [5725] "a" "t" "t" "t" "a" "c" "t" "t" "g" "t" "g" "c" "t" "a" "g" "t" "g" "a"
##  [5743] "g" "t" "a" "c" "a" "c" "t" "g" "g" "t" "a" "a" "t" "t" "a" "c" "c" "a"
##  [5761] "g" "t" "g" "t" "g" "g" "t" "c" "a" "c" "t" "a" "t" "a" "a" "a" "c" "a"
##  [5779] "t" "a" "t" "a" "a" "c" "t" "t" "c" "t" "a" "a" "a" "g" "a" "a" "a" "c"
##  [5797] "t" "t" "t" "g" "t" "a" "t" "t" "g" "c" "a" "t" "a" "g" "a" "c" "g" "g"
##  [5815] "t" "g" "c" "t" "t" "t" "a" "c" "t" "t" "a" "c" "a" "a" "a" "g" "t" "c"
##  [5833] "c" "t" "c" "a" "g" "a" "a" "t" "a" "c" "a" "a" "a" "g" "g" "t" "c" "c"
##  [5851] "t" "a" "t" "t" "a" "c" "g" "g" "a" "t" "g" "t" "t" "t" "t" "c" "t" "a"
##  [5869] "c" "a" "a" "a" "g" "a" "a" "a" "a" "c" "a" "g" "t" "t" "a" "c" "a" "c"
##  [5887] "a" "a" "c" "a" "a" "c" "c" "a" "t" "a" "a" "a" "a" "c" "c" "a" "g" "t"
##  [5905] "t" "a" "c" "t" "t" "a" "t" "a" "a" "a" "t" "t" "g" "g" "a" "t" "g" "g"
##  [5923] "t" "g" "t" "t" "g" "t" "t" "t" "g" "t" "a" "c" "a" "g" "a" "a" "a" "t"
##  [5941] "t" "g" "a" "c" "c" "c" "t" "a" "a" "g" "t" "t" "g" "g" "a" "c" "a" "a"
##  [5959] "t" "t" "a" "t" "t" "a" "t" "a" "a" "g" "a" "a" "a" "g" "a" "c" "a" "a"
##  [5977] "t" "t" "c" "t" "t" "a" "t" "t" "t" "c" "a" "c" "a" "g" "a" "g" "c" "a"
##  [5995] "a" "c" "c" "a" "a" "t" "t" "g" "a" "t" "c" "t" "t" "g" "t" "a" "c" "c"
##  [6013] "a" "a" "a" "c" "c" "a" "a" "c" "c" "a" "t" "a" "t" "c" "c" "a" "a" "a"
##  [6031] "c" "g" "c" "a" "a" "g" "c" "t" "t" "c" "g" "a" "t" "a" "a" "t" "t" "t"
##  [6049] "t" "a" "a" "g" "t" "t" "t" "g" "t" "a" "t" "g" "t" "g" "a" "t" "a" "a"
##  [6067] "t" "a" "t" "c" "a" "a" "a" "t" "t" "t" "g" "c" "t" "g" "a" "t" "g" "a"
##  [6085] "t" "t" "t" "a" "a" "a" "c" "c" "a" "g" "t" "t" "a" "a" "c" "t" "g" "g"
##  [6103] "t" "t" "a" "t" "a" "a" "g" "a" "a" "a" "c" "c" "t" "g" "c" "t" "t" "c"
##  [6121] "a" "a" "g" "a" "g" "a" "g" "c" "t" "t" "a" "a" "a" "g" "t" "t" "a" "c"
##  [6139] "a" "t" "t" "t" "t" "t" "c" "c" "c" "t" "g" "a" "c" "t" "t" "a" "a" "a"
##  [6157] "t" "g" "g" "t" "g" "a" "t" "g" "t" "g" "g" "t" "g" "g" "c" "t" "a" "t"
##  [6175] "t" "g" "a" "t" "t" "a" "t" "a" "a" "a" "c" "a" "c" "t" "a" "c" "a" "c"
##  [6193] "a" "c" "c" "c" "t" "c" "t" "t" "t" "t" "a" "a" "g" "a" "a" "a" "g" "g"
##  [6211] "a" "g" "c" "t" "a" "a" "a" "t" "t" "g" "t" "t" "a" "c" "a" "t" "a" "a"
##  [6229] "a" "c" "c" "t" "a" "t" "t" "g" "t" "t" "t" "g" "g" "c" "a" "t" "g" "t"
##  [6247] "t" "a" "a" "c" "a" "a" "t" "g" "c" "a" "a" "c" "t" "a" "a" "t" "a" "a"
##  [6265] "a" "g" "c" "c" "a" "c" "g" "t" "a" "t" "a" "a" "a" "c" "c" "a" "a" "a"
##  [6283] "t" "a" "c" "c" "t" "g" "g" "t" "g" "t" "a" "t" "a" "c" "g" "t" "t" "g"
##  [6301] "t" "c" "t" "t" "t" "g" "g" "a" "g" "c" "a" "c" "a" "a" "a" "a" "c" "c"
##  [6319] "a" "g" "t" "t" "g" "a" "a" "a" "c" "a" "t" "c" "a" "a" "a" "t" "t" "c"
##  [6337] "g" "t" "t" "t" "g" "a" "t" "g" "t" "a" "c" "t" "g" "a" "a" "g" "t" "c"
##  [6355] "a" "g" "a" "g" "g" "a" "c" "g" "c" "g" "c" "a" "g" "g" "g" "a" "a" "t"
##  [6373] "g" "g" "a" "t" "a" "a" "t" "c" "t" "t" "g" "c" "c" "t" "g" "c" "g" "a"
##  [6391] "a" "g" "a" "t" "c" "t" "a" "a" "a" "a" "c" "c" "a" "g" "t" "c" "t" "c"
##  [6409] "t" "g" "a" "a" "g" "a" "a" "g" "t" "a" "g" "t" "g" "g" "a" "a" "a" "a"
##  [6427] "t" "c" "c" "t" "a" "c" "c" "a" "t" "a" "c" "a" "g" "a" "a" "a" "g" "a"
##  [6445] "c" "g" "t" "t" "c" "t" "t" "g" "a" "g" "t" "g" "t" "a" "a" "t" "g" "t"
##  [6463] "g" "a" "a" "a" "a" "c" "t" "a" "c" "c" "g" "a" "a" "g" "t" "t" "g" "t"
##  [6481] "a" "g" "g" "a" "g" "a" "c" "a" "t" "t" "a" "t" "a" "c" "t" "t" "a" "a"
##  [6499] "a" "c" "c" "a" "g" "c" "a" "a" "a" "t" "a" "a" "t" "a" "g" "t" "t" "t"
##  [6517] "a" "a" "a" "a" "a" "t" "t" "a" "c" "a" "g" "a" "a" "g" "a" "g" "g" "t"
##  [6535] "t" "g" "g" "c" "c" "a" "c" "a" "c" "a" "g" "a" "t" "c" "t" "a" "a" "t"
##  [6553] "g" "g" "c" "t" "g" "c" "t" "t" "a" "t" "g" "t" "a" "g" "a" "c" "a" "a"
##  [6571] "t" "t" "c" "t" "a" "g" "t" "c" "t" "t" "a" "c" "t" "a" "t" "t" "a" "a"
##  [6589] "g" "a" "a" "a" "c" "c" "t" "a" "a" "t" "g" "a" "a" "t" "t" "a" "t" "c"
##  [6607] "t" "a" "g" "a" "g" "t" "a" "t" "t" "a" "g" "g" "t" "t" "t" "g" "a" "a"
##  [6625] "a" "a" "c" "c" "c" "t" "t" "g" "c" "t" "a" "c" "t" "c" "a" "t" "g" "g"
##  [6643] "t" "t" "t" "a" "g" "c" "t" "g" "c" "t" "g" "t" "t" "a" "a" "t" "a" "g"
##  [6661] "t" "g" "t" "c" "c" "c" "t" "t" "g" "g" "g" "a" "t" "a" "c" "t" "a" "t"
##  [6679] "a" "g" "c" "t" "a" "a" "t" "t" "a" "t" "g" "c" "t" "a" "a" "g" "c" "c"
##  [6697] "t" "t" "t" "t" "c" "t" "t" "a" "a" "c" "a" "a" "a" "g" "t" "t" "g" "t"
##  [6715] "t" "a" "g" "t" "a" "c" "a" "a" "c" "t" "a" "c" "t" "a" "a" "c" "a" "t"
##  [6733] "a" "g" "t" "t" "a" "c" "a" "c" "g" "g" "t" "g" "t" "t" "t" "a" "a" "a"
##  [6751] "c" "c" "g" "t" "g" "t" "t" "t" "g" "t" "a" "c" "t" "a" "a" "t" "t" "a"
##  [6769] "t" "a" "t" "g" "c" "c" "t" "t" "a" "t" "t" "t" "c" "t" "t" "t" "a" "c"
##  [6787] "t" "t" "t" "a" "t" "t" "g" "c" "t" "a" "c" "a" "a" "t" "t" "g" "t" "g"
##  [6805] "t" "a" "c" "t" "t" "t" "t" "a" "c" "t" "a" "g" "a" "a" "g" "t" "a" "c"
##  [6823] "a" "a" "a" "t" "t" "c" "t" "a" "g" "a" "a" "t" "t" "a" "a" "a" "g" "c"
##  [6841] "a" "t" "c" "t" "a" "t" "g" "c" "c" "g" "a" "c" "t" "a" "c" "t" "a" "t"
##  [6859] "a" "g" "c" "a" "a" "a" "g" "a" "a" "t" "a" "c" "t" "g" "t" "t" "a" "a"
##  [6877] "g" "a" "g" "t" "g" "t" "c" "g" "g" "t" "a" "a" "a" "t" "t" "t" "t" "g"
##  [6895] "t" "c" "t" "a" "g" "a" "g" "g" "c" "t" "t" "c" "a" "t" "t" "t" "a" "a"
##  [6913] "t" "t" "a" "t" "t" "t" "g" "a" "a" "g" "t" "c" "a" "c" "c" "t" "a" "a"
##  [6931] "t" "t" "t" "t" "t" "c" "t" "a" "a" "a" "c" "t" "g" "a" "t" "a" "a" "a"
##  [6949] "t" "a" "t" "t" "a" "t" "a" "a" "t" "t" "t" "g" "g" "t" "t" "t" "t" "t"
##  [6967] "a" "c" "t" "a" "t" "t" "a" "a" "g" "t" "g" "t" "t" "t" "g" "c" "c" "t"
##  [6985] "a" "g" "g" "t" "t" "c" "t" "t" "t" "a" "a" "t" "c" "t" "a" "c" "t" "c"
##  [7003] "a" "a" "c" "c" "g" "c" "t" "g" "c" "t" "t" "t" "a" "g" "g" "t" "g" "t"
##  [7021] "t" "t" "t" "a" "a" "t" "g" "t" "c" "t" "a" "a" "t" "t" "t" "a" "g" "g"
##  [7039] "c" "a" "t" "g" "c" "c" "t" "t" "c" "t" "t" "a" "c" "t" "g" "t" "a" "c"
##  [7057] "t" "g" "g" "t" "t" "a" "c" "a" "g" "a" "g" "a" "a" "g" "g" "c" "t" "a"
##  [7075] "t" "t" "t" "g" "a" "a" "c" "t" "c" "t" "a" "c" "t" "a" "a" "t" "g" "t"
##  [7093] "c" "a" "c" "t" "a" "t" "t" "g" "c" "a" "a" "c" "c" "t" "a" "c" "t" "g"
##  [7111] "t" "a" "c" "t" "g" "g" "t" "t" "c" "t" "a" "t" "a" "c" "c" "t" "t" "g"
##  [7129] "t" "a" "g" "t" "g" "t" "t" "t" "g" "t" "c" "t" "t" "a" "g" "t" "g" "g"
##  [7147] "t" "t" "t" "a" "g" "a" "t" "t" "c" "t" "t" "t" "a" "g" "a" "c" "a" "c"
##  [7165] "c" "t" "a" "t" "c" "c" "t" "t" "c" "t" "t" "t" "a" "g" "a" "a" "a" "c"
##  [7183] "t" "a" "t" "a" "c" "a" "a" "a" "t" "t" "a" "c" "c" "a" "t" "t" "t" "c"
##  [7201] "a" "t" "c" "t" "t" "t" "t" "a" "a" "a" "t" "g" "g" "g" "a" "t" "t" "t"
##  [7219] "a" "a" "c" "t" "g" "c" "t" "t" "t" "t" "g" "g" "c" "t" "t" "a" "g" "t"
##  [7237] "t" "g" "c" "a" "g" "a" "g" "t" "g" "g" "t" "t" "t" "t" "t" "g" "g" "c"
##  [7255] "a" "t" "a" "t" "a" "t" "t" "c" "t" "t" "t" "t" "c" "a" "c" "t" "a" "g"
##  [7273] "g" "t" "t" "t" "t" "t" "c" "t" "a" "t" "g" "t" "a" "c" "t" "t" "g" "g"
##  [7291] "a" "t" "t" "g" "g" "c" "t" "g" "c" "a" "a" "t" "c" "a" "t" "g" "c" "a"
##  [7309] "a" "t" "t" "g" "t" "t" "t" "t" "t" "c" "a" "g" "c" "t" "a" "t" "t" "t"
##  [7327] "t" "g" "c" "a" "g" "t" "a" "c" "a" "t" "t" "t" "t" "a" "t" "t" "a" "g"
##  [7345] "t" "a" "a" "t" "t" "c" "t" "t" "g" "g" "c" "t" "t" "a" "t" "g" "t" "g"
##  [7363] "g" "t" "t" "a" "a" "t" "a" "a" "t" "t" "a" "a" "t" "c" "t" "t" "g" "t"
##  [7381] "a" "c" "a" "a" "a" "t" "g" "g" "c" "c" "c" "c" "g" "a" "t" "t" "t" "c"
##  [7399] "a" "g" "c" "t" "a" "t" "g" "g" "t" "t" "a" "g" "a" "a" "t" "g" "t" "a"
##  [7417] "c" "a" "t" "c" "t" "t" "c" "t" "t" "t" "g" "c" "a" "t" "c" "a" "t" "t"
##  [7435] "t" "t" "a" "t" "t" "a" "t" "g" "t" "a" "t" "g" "g" "a" "a" "a" "a" "g"
##  [7453] "t" "t" "a" "t" "g" "t" "g" "c" "a" "t" "g" "t" "t" "g" "t" "a" "g" "a"
##  [7471] "c" "g" "g" "t" "t" "g" "t" "a" "a" "t" "t" "c" "a" "t" "c" "a" "a" "c"
##  [7489] "t" "t" "g" "t" "a" "t" "g" "a" "t" "g" "t" "g" "t" "t" "a" "c" "a" "a"
##  [7507] "a" "c" "g" "t" "a" "a" "t" "a" "g" "a" "g" "c" "a" "a" "c" "a" "a" "g"
##  [7525] "a" "g" "t" "c" "g" "a" "a" "t" "g" "t" "a" "c" "a" "a" "c" "t" "a" "t"
##  [7543] "t" "g" "t" "t" "a" "a" "t" "g" "g" "t" "g" "t" "t" "a" "g" "a" "a" "g"
##  [7561] "g" "t" "c" "c" "t" "t" "t" "t" "a" "t" "g" "t" "c" "t" "a" "t" "g" "c"
##  [7579] "t" "a" "a" "t" "g" "g" "a" "g" "g" "t" "a" "a" "a" "g" "g" "c" "t" "t"
##  [7597] "t" "t" "g" "c" "a" "a" "a" "c" "t" "a" "c" "a" "c" "a" "a" "t" "t" "g"
##  [7615] "g" "a" "a" "t" "t" "g" "t" "g" "t" "t" "a" "a" "t" "t" "g" "t" "g" "a"
##  [7633] "t" "a" "c" "a" "t" "t" "c" "t" "g" "t" "g" "c" "t" "g" "g" "t" "a" "g"
##  [7651] "t" "a" "c" "a" "t" "t" "t" "a" "t" "t" "a" "g" "t" "g" "a" "t" "g" "a"
##  [7669] "a" "g" "t" "t" "g" "c" "g" "a" "g" "a" "g" "a" "c" "t" "t" "g" "t" "c"
##  [7687] "a" "c" "t" "a" "c" "a" "g" "t" "t" "t" "a" "a" "a" "a" "g" "a" "c" "c"
##  [7705] "a" "a" "t" "a" "a" "a" "t" "c" "c" "t" "a" "c" "t" "g" "a" "c" "c" "a"
##  [7723] "g" "t" "c" "t" "t" "c" "t" "t" "a" "c" "a" "t" "c" "g" "t" "t" "g" "a"
##  [7741] "t" "a" "g" "t" "g" "t" "t" "a" "c" "a" "g" "t" "g" "a" "a" "g" "a" "a"
##  [7759] "t" "g" "g" "t" "t" "c" "c" "a" "t" "c" "c" "a" "t" "c" "t" "t" "t" "a"
##  [7777] "c" "t" "t" "t" "g" "a" "t" "a" "a" "a" "g" "c" "t" "g" "g" "t" "c" "a"
##  [7795] "a" "a" "a" "g" "a" "c" "t" "t" "a" "t" "g" "a" "a" "a" "g" "a" "c" "a"
##  [7813] "t" "t" "c" "t" "c" "t" "c" "t" "c" "t" "c" "a" "t" "t" "t" "t" "g" "t"
##  [7831] "t" "a" "a" "c" "t" "t" "a" "g" "a" "c" "a" "a" "c" "c" "t" "g" "a" "g"
##  [7849] "a" "g" "c" "t" "a" "a" "t" "a" "a" "c" "a" "c" "t" "a" "a" "a" "g" "g"
##  [7867] "t" "t" "c" "a" "t" "t" "g" "c" "c" "t" "a" "t" "t" "a" "a" "t" "g" "t"
##  [7885] "t" "a" "t" "a" "g" "t" "t" "t" "t" "t" "g" "a" "t" "g" "g" "t" "a" "a"
##  [7903] "a" "t" "c" "a" "a" "a" "a" "t" "g" "t" "g" "a" "a" "g" "a" "a" "t" "c"
##  [7921] "a" "t" "c" "t" "g" "c" "a" "a" "a" "a" "t" "c" "a" "g" "c" "g" "t" "c"
##  [7939] "t" "g" "t" "t" "t" "a" "c" "t" "a" "c" "a" "g" "t" "c" "a" "g" "c" "t"
##  [7957] "t" "a" "t" "g" "t" "g" "t" "c" "a" "a" "c" "c" "t" "a" "t" "a" "c" "t"
##  [7975] "g" "t" "t" "a" "c" "t" "a" "g" "a" "t" "c" "a" "g" "g" "c" "a" "t" "t"
##  [7993] "a" "g" "t" "g" "t" "c" "t" "g" "a" "t" "g" "t" "t" "g" "g" "t" "g" "a"
##  [8011] "t" "a" "g" "t" "g" "c" "g" "g" "a" "a" "g" "t" "t" "g" "c" "a" "g" "t"
##  [8029] "t" "a" "a" "a" "a" "t" "g" "t" "t" "t" "g" "a" "t" "g" "c" "t" "t" "a"
##  [8047] "c" "g" "t" "t" "a" "a" "t" "a" "c" "g" "t" "t" "t" "t" "c" "a" "t" "c"
##  [8065] "a" "a" "c" "t" "t" "t" "t" "a" "a" "c" "g" "t" "a" "c" "c" "a" "a" "t"
##  [8083] "g" "g" "a" "a" "a" "a" "a" "c" "t" "c" "a" "a" "a" "a" "c" "a" "c" "t"
##  [8101] "a" "g" "t" "t" "g" "c" "a" "a" "c" "t" "g" "c" "a" "g" "a" "a" "g" "c"
##  [8119] "t" "g" "a" "a" "c" "t" "t" "g" "c" "a" "a" "a" "g" "a" "a" "t" "g" "t"
##  [8137] "g" "t" "c" "c" "t" "t" "a" "g" "a" "c" "a" "a" "t" "g" "t" "c" "t" "t"
##  [8155] "a" "t" "c" "t" "a" "c" "t" "t" "t" "t" "a" "t" "t" "t" "c" "a" "g" "c"
##  [8173] "a" "g" "c" "t" "c" "g" "g" "c" "a" "a" "g" "g" "g" "t" "t" "t" "g" "t"
##  [8191] "t" "g" "a" "t" "t" "c" "a" "g" "a" "t" "g" "t" "a" "g" "a" "a" "a" "c"
##  [8209] "t" "a" "a" "a" "g" "a" "t" "g" "t" "t" "g" "t" "t" "g" "a" "a" "t" "g"
##  [8227] "t" "c" "t" "t" "a" "a" "a" "t" "t" "g" "t" "c" "a" "c" "a" "t" "c" "a"
##  [8245] "a" "t" "c" "t" "g" "a" "c" "a" "t" "a" "g" "a" "a" "g" "t" "t" "a" "c"
##  [8263] "t" "g" "g" "c" "g" "a" "t" "a" "g" "t" "t" "g" "t" "a" "a" "t" "a" "a"
##  [8281] "c" "t" "a" "t" "a" "t" "g" "c" "t" "c" "a" "c" "c" "t" "a" "t" "a" "a"
##  [8299] "c" "a" "a" "a" "g" "t" "t" "g" "a" "a" "a" "a" "c" "a" "t" "g" "a" "c"
##  [8317] "a" "c" "c" "c" "c" "g" "t" "g" "a" "c" "c" "t" "t" "g" "g" "t" "g" "c"
##  [8335] "t" "t" "g" "t" "a" "t" "t" "g" "a" "c" "t" "g" "t" "a" "g" "t" "g" "c"
##  [8353] "g" "c" "g" "t" "c" "a" "t" "a" "t" "t" "a" "a" "t" "g" "c" "g" "c" "a"
##  [8371] "g" "g" "t" "a" "g" "c" "a" "a" "a" "a" "a" "g" "t" "c" "a" "c" "a" "a"
##  [8389] "c" "a" "t" "t" "g" "c" "t" "t" "t" "g" "a" "t" "a" "t" "g" "g" "a" "a"
##  [8407] "c" "g" "t" "t" "a" "a" "a" "g" "a" "t" "t" "t" "c" "a" "t" "g" "t" "c"
##  [8425] "a" "t" "t" "g" "t" "c" "t" "g" "a" "a" "c" "a" "a" "c" "t" "a" "c" "g"
##  [8443] "a" "a" "a" "a" "c" "a" "a" "a" "t" "a" "c" "g" "t" "a" "g" "t" "g" "c"
##  [8461] "t" "g" "c" "t" "a" "a" "a" "a" "a" "g" "a" "a" "t" "a" "a" "c" "t" "t"
##  [8479] "a" "c" "c" "t" "t" "t" "t" "a" "a" "g" "t" "t" "g" "a" "c" "a" "t" "g"
##  [8497] "t" "g" "c" "a" "a" "c" "t" "a" "c" "t" "a" "g" "a" "c" "a" "a" "g" "t"
##  [8515] "t" "g" "t" "t" "a" "a" "t" "g" "t" "t" "g" "t" "a" "a" "c" "a" "a" "c"
##  [8533] "a" "a" "a" "g" "a" "t" "a" "g" "c" "a" "c" "t" "t" "a" "a" "g" "g" "g"
##  [8551] "t" "g" "g" "t" "a" "a" "a" "a" "t" "t" "g" "t" "t" "a" "a" "t" "a" "a"
##  [8569] "t" "t" "g" "g" "t" "t" "g" "a" "a" "g" "c" "a" "g" "t" "t" "a" "a" "t"
##  [8587] "t" "a" "a" "a" "g" "t" "t" "a" "c" "a" "c" "t" "t" "g" "t" "g" "t" "t"
##  [8605] "c" "c" "t" "t" "t" "t" "t" "g" "t" "t" "g" "c" "t" "g" "c" "t" "a" "t"
##  [8623] "t" "t" "t" "c" "t" "a" "t" "t" "t" "a" "a" "t" "a" "a" "c" "a" "c" "c"
##  [8641] "t" "g" "t" "t" "c" "a" "t" "g" "t" "c" "a" "t" "g" "t" "c" "t" "a" "a"
##  [8659] "a" "c" "a" "t" "a" "c" "t" "g" "a" "c" "t" "t" "t" "t" "c" "a" "a" "g"
##  [8677] "t" "g" "a" "a" "a" "t" "c" "a" "t" "a" "g" "g" "a" "t" "a" "c" "a" "a"
##  [8695] "g" "g" "c" "t" "a" "t" "t" "g" "a" "t" "g" "g" "t" "g" "g" "t" "g" "t"
##  [8713] "c" "a" "c" "t" "c" "g" "t" "g" "a" "c" "a" "t" "a" "g" "c" "a" "t" "c"
##  [8731] "t" "a" "c" "a" "g" "a" "t" "a" "c" "t" "t" "g" "t" "t" "t" "t" "g" "c"
##  [8749] "t" "a" "a" "c" "a" "a" "a" "c" "a" "t" "g" "c" "t" "g" "a" "t" "t" "t"
##  [8767] "t" "g" "a" "c" "a" "c" "a" "t" "g" "g" "t" "t" "t" "a" "g" "c" "c" "a"
##  [8785] "g" "c" "g" "t" "g" "g" "t" "g" "g" "t" "a" "g" "t" "t" "a" "t" "a" "c"
##  [8803] "t" "a" "a" "t" "g" "a" "c" "a" "a" "a" "g" "c" "t" "t" "g" "c" "c" "c"
##  [8821] "a" "t" "t" "g" "a" "t" "t" "g" "c" "t" "g" "c" "a" "g" "t" "c" "a" "t"
##  [8839] "a" "a" "c" "a" "a" "g" "a" "g" "a" "a" "g" "t" "g" "g" "g" "t" "t" "t"
##  [8857] "t" "g" "t" "c" "g" "t" "g" "c" "c" "t" "g" "g" "t" "t" "t" "g" "c" "c"
##  [8875] "t" "g" "g" "c" "a" "c" "g" "a" "t" "a" "t" "t" "a" "c" "g" "c" "a" "c"
##  [8893] "a" "a" "c" "t" "a" "a" "t" "g" "g" "t" "g" "a" "c" "t" "t" "t" "t" "t"
##  [8911] "g" "c" "a" "t" "t" "t" "c" "t" "t" "a" "c" "c" "t" "a" "g" "a" "g" "t"
##  [8929] "t" "t" "t" "t" "a" "g" "t" "g" "c" "a" "g" "t" "t" "g" "g" "t" "a" "a"
##  [8947] "c" "a" "t" "c" "t" "g" "t" "t" "a" "c" "a" "c" "a" "c" "c" "a" "t" "c"
##  [8965] "a" "a" "a" "a" "c" "t" "t" "a" "t" "a" "g" "a" "g" "t" "a" "c" "a" "c"
##  [8983] "t" "g" "a" "c" "t" "t" "t" "g" "c" "a" "a" "c" "a" "t" "c" "a" "g" "c"
##  [9001] "t" "t" "g" "t" "g" "t" "t" "t" "t" "g" "g" "c" "t" "g" "c" "t" "g" "a"
##  [9019] "a" "t" "g" "t" "a" "c" "a" "a" "t" "t" "t" "t" "t" "a" "a" "a" "g" "a"
##  [9037] "t" "g" "c" "t" "t" "c" "t" "g" "g" "t" "a" "a" "g" "c" "c" "a" "g" "t"
##  [9055] "a" "c" "c" "a" "t" "a" "t" "t" "g" "t" "t" "a" "t" "g" "a" "t" "a" "c"
##  [9073] "c" "a" "a" "t" "g" "t" "a" "c" "t" "a" "g" "a" "a" "g" "g" "t" "t" "c"
##  [9091] "t" "g" "t" "t" "g" "c" "t" "t" "a" "t" "g" "a" "a" "a" "g" "t" "t" "t"
##  [9109] "a" "c" "g" "c" "c" "c" "t" "g" "a" "c" "a" "c" "a" "c" "g" "t" "t" "a"
##  [9127] "t" "g" "t" "g" "c" "t" "c" "a" "t" "g" "g" "a" "t" "g" "g" "c" "t" "c"
##  [9145] "t" "a" "t" "t" "a" "t" "t" "c" "a" "a" "t" "t" "t" "c" "c" "t" "a" "a"
##  [9163] "c" "a" "c" "c" "t" "a" "c" "c" "t" "t" "g" "a" "a" "g" "g" "t" "t" "c"
##  [9181] "t" "g" "t" "t" "a" "g" "a" "g" "t" "g" "g" "t" "a" "a" "c" "a" "a" "c"
##  [9199] "t" "t" "t" "t" "g" "a" "t" "t" "c" "t" "g" "a" "g" "t" "a" "c" "t" "g"
##  [9217] "t" "a" "g" "g" "c" "a" "c" "g" "g" "c" "a" "c" "t" "t" "g" "t" "g" "a"
##  [9235] "a" "a" "g" "a" "t" "c" "a" "g" "a" "a" "g" "c" "t" "g" "g" "t" "g" "t"
##  [9253] "t" "t" "g" "t" "g" "t" "a" "t" "c" "t" "a" "c" "t" "a" "g" "t" "g" "g"
##  [9271] "t" "a" "g" "a" "t" "g" "g" "g" "t" "a" "c" "t" "t" "a" "a" "c" "a" "a"
##  [9289] "t" "g" "a" "t" "t" "a" "t" "t" "a" "c" "a" "g" "a" "t" "c" "t" "t" "t"
##  [9307] "a" "c" "c" "a" "g" "g" "a" "g" "t" "t" "t" "t" "c" "t" "g" "t" "g" "g"
##  [9325] "t" "g" "t" "a" "g" "a" "t" "g" "c" "t" "g" "t" "a" "a" "a" "t" "t" "t"
##  [9343] "a" "c" "t" "t" "a" "c" "t" "a" "a" "t" "a" "t" "g" "t" "t" "t" "a" "c"
##  [9361] "a" "c" "c" "a" "c" "t" "a" "a" "t" "t" "c" "a" "a" "c" "c" "t" "a" "t"
##  [9379] "t" "g" "g" "t" "g" "c" "t" "t" "t" "g" "g" "a" "c" "a" "t" "a" "t" "c"
##  [9397] "a" "g" "c" "a" "t" "c" "t" "a" "t" "a" "g" "t" "a" "g" "c" "t" "g" "g"
##  [9415] "t" "g" "g" "t" "a" "t" "t" "g" "t" "a" "g" "c" "t" "a" "t" "c" "g" "t"
##  [9433] "a" "g" "t" "a" "a" "c" "a" "t" "g" "c" "c" "t" "t" "g" "c" "c" "t" "a"
##  [9451] "c" "t" "a" "t" "t" "t" "t" "a" "t" "g" "a" "g" "g" "t" "t" "t" "a" "g"
##  [9469] "a" "a" "g" "a" "g" "c" "t" "t" "t" "t" "g" "g" "t" "g" "a" "a" "t" "a"
##  [9487] "c" "a" "g" "t" "c" "a" "t" "g" "t" "a" "g" "t" "t" "g" "c" "c" "t" "t"
##  [9505] "t" "a" "a" "t" "a" "c" "t" "t" "t" "a" "c" "t" "a" "t" "t" "c" "c" "t"
##  [9523] "t" "a" "t" "g" "t" "c" "a" "t" "t" "c" "a" "c" "t" "g" "t" "a" "c" "t"
##  [9541] "c" "t" "g" "t" "t" "t" "a" "a" "c" "a" "c" "c" "a" "g" "t" "t" "t" "a"
##  [9559] "c" "t" "c" "a" "t" "t" "c" "t" "t" "a" "c" "c" "t" "g" "g" "t" "g" "t"
##  [9577] "t" "t" "a" "t" "t" "c" "t" "g" "t" "t" "a" "t" "t" "t" "a" "c" "t" "t"
##  [9595] "g" "t" "a" "c" "t" "t" "g" "a" "c" "a" "t" "t" "t" "t" "a" "t" "c" "t"
##  [9613] "t" "a" "c" "t" "a" "a" "t" "g" "a" "t" "g" "t" "t" "t" "c" "t" "t" "t"
##  [9631] "t" "t" "t" "a" "g" "c" "a" "c" "a" "t" "a" "t" "t" "c" "a" "g" "t" "g"
##  [9649] "g" "a" "t" "g" "g" "t" "t" "a" "t" "g" "t" "t" "c" "a" "c" "a" "c" "c"
##  [9667] "t" "t" "t" "a" "g" "t" "a" "c" "c" "t" "t" "t" "c" "t" "g" "g" "a" "t"
##  [9685] "a" "a" "c" "a" "a" "t" "t" "g" "c" "t" "t" "a" "t" "a" "t" "c" "a" "t"
##  [9703] "t" "t" "g" "t" "a" "t" "t" "t" "c" "c" "a" "c" "a" "a" "a" "g" "c" "a"
##  [9721] "t" "t" "t" "c" "t" "a" "t" "t" "g" "g" "t" "t" "c" "t" "t" "t" "a" "g"
##  [9739] "t" "a" "a" "t" "t" "a" "c" "c" "t" "a" "a" "a" "g" "a" "g" "a" "c" "g"
##  [9757] "t" "g" "t" "a" "g" "t" "c" "t" "t" "t" "a" "a" "t" "g" "g" "t" "g" "t"
##  [9775] "t" "t" "c" "c" "t" "t" "t" "a" "g" "t" "a" "c" "t" "t" "t" "t" "g" "a"
##  [9793] "a" "g" "a" "a" "g" "c" "t" "g" "c" "g" "c" "t" "g" "t" "g" "c" "a" "c"
##  [9811] "c" "t" "t" "t" "t" "t" "g" "t" "t" "a" "a" "a" "t" "a" "a" "a" "g" "a"
##  [9829] "a" "a" "t" "g" "t" "a" "t" "c" "t" "a" "a" "a" "g" "t" "t" "g" "c" "g"
##  [9847] "t" "a" "g" "t" "g" "a" "t" "g" "t" "g" "c" "t" "a" "t" "t" "a" "c" "c"
##  [9865] "t" "c" "t" "t" "a" "c" "g" "c" "a" "a" "t" "a" "t" "a" "a" "t" "a" "g"
##  [9883] "a" "t" "a" "c" "t" "t" "a" "g" "c" "t" "c" "t" "t" "t" "a" "t" "a" "a"
##  [9901] "t" "a" "a" "g" "t" "a" "c" "a" "a" "g" "t" "a" "t" "t" "t" "t" "a" "g"
##  [9919] "t" "g" "g" "a" "g" "c" "a" "a" "t" "g" "g" "a" "t" "a" "c" "a" "a" "c"
##  [9937] "t" "a" "g" "c" "t" "a" "c" "a" "g" "a" "g" "a" "a" "g" "c" "t" "g" "c"
##  [9955] "t" "t" "g" "t" "t" "g" "t" "c" "a" "t" "c" "t" "c" "g" "c" "a" "a" "a"
##  [9973] "g" "g" "c" "t" "c" "t" "c" "a" "a" "t" "g" "a" "c" "t" "t" "c" "a" "g"
##  [9991] "t" "a" "a" "c" "t" "c" "a" "g" "g" "t" "t" "c" "t" "g" "a" "t" "g" "t"
## [10009] "t" "c" "t" "t" "t" "a" "c" "c" "a" "a" "c" "c" "a" "c" "c" "a" "c" "a"
## [10027] "a" "a" "c" "c" "t" "c" "t" "a" "t" "c" "a" "c" "c" "t" "c" "a" "g" "c"
## [10045] "t" "g" "t" "t" "t" "t" "g" "c" "a" "g" "a" "g" "t" "g" "g" "t" "t" "t"
## [10063] "t" "a" "g" "a" "a" "a" "a" "a" "t" "g" "g" "c" "a" "t" "t" "c" "c" "c"
## [10081] "a" "t" "c" "t" "g" "g" "t" "a" "a" "a" "g" "t" "t" "g" "a" "g" "g" "g"
## [10099] "t" "t" "g" "t" "a" "t" "g" "g" "t" "a" "c" "a" "a" "g" "t" "a" "a" "c"
## [10117] "t" "t" "g" "t" "g" "g" "t" "a" "c" "a" "a" "c" "t" "a" "c" "a" "c" "t"
## [10135] "t" "a" "a" "c" "g" "g" "t" "c" "t" "t" "t" "g" "g" "c" "t" "t" "g" "a"
## [10153] "t" "g" "a" "c" "g" "t" "a" "g" "t" "t" "t" "a" "c" "t" "g" "t" "c" "c"
## [10171] "a" "a" "g" "a" "c" "a" "t" "g" "t" "g" "a" "t" "c" "t" "g" "c" "a" "c"
## [10189] "c" "t" "c" "t" "g" "a" "a" "g" "a" "c" "a" "t" "g" "c" "t" "t" "a" "a"
## [10207] "c" "c" "c" "t" "a" "a" "t" "t" "a" "t" "g" "a" "a" "g" "a" "t" "t" "t"
## [10225] "a" "c" "t" "c" "a" "t" "t" "c" "g" "t" "a" "a" "g" "t" "c" "t" "a" "a"
## [10243] "t" "c" "a" "t" "a" "a" "t" "t" "t" "c" "t" "t" "g" "g" "t" "a" "c" "a"
## [10261] "g" "g" "c" "t" "g" "g" "t" "a" "a" "t" "g" "t" "t" "c" "a" "a" "c" "t"
## [10279] "c" "a" "g" "g" "g" "t" "t" "a" "t" "t" "g" "g" "a" "c" "a" "t" "t" "c"
## [10297] "t" "a" "t" "g" "c" "a" "a" "a" "a" "t" "t" "g" "t" "g" "t" "a" "c" "t"
## [10315] "t" "a" "a" "g" "c" "t" "t" "a" "a" "g" "g" "t" "t" "g" "a" "t" "a" "c"
## [10333] "a" "g" "c" "c" "a" "a" "t" "c" "c" "t" "a" "a" "g" "a" "c" "a" "c" "c"
## [10351] "t" "a" "a" "g" "t" "a" "t" "a" "a" "g" "t" "t" "t" "g" "t" "t" "c" "g"
## [10369] "c" "a" "t" "t" "c" "a" "a" "c" "c" "a" "g" "g" "a" "c" "a" "g" "a" "c"
## [10387] "t" "t" "t" "t" "t" "c" "a" "g" "t" "g" "t" "t" "a" "g" "c" "t" "t" "g"
## [10405] "t" "t" "a" "c" "a" "a" "t" "g" "g" "t" "t" "c" "a" "c" "c" "a" "t" "c"
## [10423] "t" "g" "g" "t" "g" "t" "t" "t" "a" "c" "c" "a" "a" "t" "g" "t" "g" "c"
## [10441] "t" "a" "t" "g" "a" "g" "g" "c" "c" "c" "a" "a" "t" "t" "t" "c" "a" "c"
## [10459] "t" "a" "t" "t" "a" "a" "g" "g" "g" "t" "t" "c" "a" "t" "t" "c" "c" "t"
## [10477] "t" "a" "a" "t" "g" "g" "t" "t" "c" "a" "t" "g" "t" "g" "g" "t" "a" "g"
## [10495] "t" "g" "t" "t" "g" "g" "t" "t" "t" "t" "a" "a" "c" "a" "t" "a" "g" "a"
## [10513] "t" "t" "a" "t" "g" "a" "c" "t" "g" "t" "g" "t" "c" "t" "c" "t" "t" "t"
## [10531] "t" "t" "g" "t" "t" "a" "c" "a" "t" "g" "c" "a" "c" "c" "a" "t" "a" "t"
## [10549] "g" "g" "a" "a" "t" "t" "a" "c" "c" "a" "a" "c" "t" "g" "g" "a" "g" "t"
## [10567] "t" "c" "a" "t" "g" "c" "t" "g" "g" "c" "a" "c" "a" "g" "a" "c" "t" "t"
## [10585] "a" "g" "a" "a" "g" "g" "t" "a" "a" "c" "t" "t" "t" "t" "a" "t" "g" "g"
## [10603] "a" "c" "c" "t" "t" "t" "t" "g" "t" "t" "g" "a" "c" "a" "g" "g" "c" "a"
## [10621] "a" "a" "c" "a" "g" "c" "a" "c" "a" "a" "g" "c" "a" "g" "c" "t" "g" "g"
## [10639] "t" "a" "c" "g" "g" "a" "c" "a" "c" "a" "a" "c" "t" "a" "t" "t" "a" "c"
## [10657] "a" "g" "t" "t" "a" "a" "t" "g" "t" "t" "t" "t" "a" "g" "c" "t" "t" "g"
## [10675] "g" "t" "t" "g" "t" "a" "c" "g" "c" "t" "g" "c" "t" "g" "t" "t" "a" "t"
## [10693] "a" "a" "a" "t" "g" "g" "a" "g" "a" "c" "a" "g" "g" "t" "g" "g" "t" "t"
## [10711] "t" "c" "t" "c" "a" "a" "t" "c" "g" "a" "t" "t" "t" "a" "c" "c" "a" "c"
## [10729] "a" "a" "c" "t" "c" "t" "t" "a" "a" "t" "g" "a" "c" "t" "t" "t" "a" "a"
## [10747] "c" "c" "t" "t" "g" "t" "g" "g" "c" "t" "a" "t" "g" "a" "a" "g" "t" "a"
## [10765] "c" "a" "a" "t" "t" "a" "t" "g" "a" "a" "c" "c" "t" "c" "t" "a" "a" "c"
## [10783] "a" "c" "a" "a" "g" "a" "c" "c" "a" "t" "g" "t" "t" "g" "a" "c" "a" "t"
## [10801] "a" "c" "t" "a" "g" "g" "a" "c" "c" "t" "c" "t" "t" "t" "c" "t" "g" "c"
## [10819] "t" "c" "a" "a" "a" "c" "t" "g" "g" "a" "a" "t" "t" "g" "c" "c" "g" "t"
## [10837] "t" "t" "t" "a" "g" "a" "t" "a" "t" "g" "t" "g" "t" "g" "c" "t" "t" "c"
## [10855] "a" "t" "t" "a" "a" "a" "a" "g" "a" "a" "t" "t" "a" "c" "t" "g" "c" "a"
## [10873] "a" "a" "a" "t" "g" "g" "t" "a" "t" "g" "a" "a" "t" "g" "g" "a" "c" "g"
## [10891] "t" "a" "c" "c" "a" "t" "a" "t" "t" "g" "g" "g" "t" "a" "g" "t" "g" "c"
## [10909] "t" "t" "t" "a" "t" "t" "a" "g" "a" "a" "g" "a" "t" "g" "a" "a" "t" "t"
## [10927] "t" "a" "c" "a" "c" "c" "t" "t" "t" "t" "g" "a" "t" "g" "t" "t" "g" "t"
## [10945] "t" "a" "g" "a" "c" "a" "a" "t" "g" "c" "t" "c" "a" "g" "g" "t" "g" "t"
## [10963] "t" "a" "c" "t" "t" "t" "c" "c" "a" "a" "a" "g" "t" "g" "c" "a" "g" "t"
## [10981] "g" "a" "a" "a" "a" "g" "a" "a" "c" "a" "a" "t" "c" "a" "a" "g" "g" "g"
## [10999] "t" "a" "c" "a" "c" "a" "c" "c" "a" "c" "t" "g" "g" "t" "t" "g" "t" "t"
## [11017] "a" "c" "t" "c" "a" "c" "a" "a" "t" "t" "t" "t" "g" "a" "c" "t" "t" "c"
## [11035] "a" "c" "t" "t" "t" "t" "a" "g" "t" "t" "t" "t" "a" "g" "t" "c" "c" "a"
## [11053] "g" "a" "g" "t" "a" "c" "t" "c" "a" "a" "t" "g" "g" "t" "c" "t" "t" "t"
## [11071] "g" "t" "t" "c" "t" "t" "t" "t" "t" "t" "t" "t" "g" "t" "a" "t" "g" "a"
## [11089] "a" "a" "a" "t" "g" "c" "c" "t" "t" "t" "t" "t" "a" "c" "c" "t" "t" "t"
## [11107] "t" "g" "c" "t" "a" "t" "g" "g" "g" "t" "a" "t" "t" "a" "t" "t" "g" "c"
## [11125] "t" "a" "t" "g" "t" "c" "t" "g" "c" "t" "t" "t" "t" "g" "c" "a" "a" "t"
## [11143] "g" "a" "t" "g" "t" "t" "t" "g" "t" "c" "a" "a" "a" "c" "a" "t" "a" "a"
## [11161] "g" "c" "a" "t" "g" "c" "a" "t" "t" "t" "c" "t" "c" "t" "g" "t" "t" "t"
## [11179] "g" "t" "t" "t" "t" "t" "g" "t" "t" "a" "c" "c" "t" "t" "c" "t" "c" "t"
## [11197] "t" "g" "c" "c" "a" "c" "t" "g" "t" "a" "g" "c" "t" "t" "a" "t" "t" "t"
## [11215] "t" "a" "a" "t" "a" "t" "g" "g" "t" "c" "t" "a" "t" "a" "t" "g" "c" "c"
## [11233] "t" "g" "c" "t" "a" "g" "t" "t" "g" "g" "g" "t" "g" "a" "t" "g" "c" "g"
## [11251] "t" "a" "t" "t" "a" "t" "g" "a" "c" "a" "t" "g" "g" "t" "t" "g" "g" "a"
## [11269] "t" "a" "t" "g" "g" "t" "t" "g" "a" "t" "a" "c" "t" "a" "g" "t" "t" "t"
## [11287] "g" "t" "c" "t" "g" "g" "t" "t" "t" "t" "a" "a" "g" "c" "t" "a" "a" "a"
## [11305] "a" "g" "a" "c" "t" "g" "t" "g" "t" "t" "a" "t" "g" "t" "a" "t" "g" "c"
## [11323] "a" "t" "c" "a" "g" "c" "t" "g" "t" "a" "g" "t" "g" "t" "t" "a" "c" "t"
## [11341] "a" "a" "t" "c" "c" "t" "t" "a" "t" "g" "a" "c" "a" "g" "c" "a" "a" "g"
## [11359] "a" "a" "c" "t" "g" "t" "g" "t" "a" "t" "g" "a" "t" "g" "a" "t" "g" "g"
## [11377] "t" "g" "c" "t" "a" "g" "g" "a" "g" "a" "g" "t" "g" "t" "g" "g" "a" "c"
## [11395] "a" "c" "t" "t" "a" "t" "g" "a" "a" "t" "g" "t" "c" "t" "t" "g" "a" "c"
## [11413] "a" "c" "t" "c" "g" "t" "t" "t" "a" "t" "a" "a" "a" "g" "t" "t" "t" "a"
## [11431] "t" "t" "a" "t" "g" "g" "t" "a" "a" "t" "g" "c" "t" "t" "t" "a" "g" "a"
## [11449] "t" "c" "a" "a" "g" "c" "c" "a" "t" "t" "t" "c" "c" "a" "t" "g" "t" "g"
## [11467] "g" "g" "c" "t" "c" "t" "t" "a" "t" "a" "a" "t" "c" "t" "c" "t" "g" "t"
## [11485] "t" "a" "c" "t" "t" "c" "t" "a" "a" "c" "t" "a" "c" "t" "c" "a" "g" "g"
## [11503] "t" "g" "t" "a" "g" "t" "t" "a" "c" "a" "a" "c" "t" "g" "t" "c" "a" "t"
## [11521] "g" "t" "t" "t" "t" "t" "g" "g" "c" "c" "a" "g" "a" "g" "g" "t" "a" "t"
## [11539] "t" "g" "t" "t" "t" "t" "t" "a" "t" "g" "t" "g" "t" "g" "t" "t" "g" "a"
## [11557] "g" "t" "a" "t" "t" "g" "c" "c" "c" "t" "a" "t" "t" "t" "t" "c" "t" "t"
## [11575] "c" "a" "t" "a" "a" "c" "t" "g" "g" "t" "a" "a" "t" "a" "c" "a" "c" "t"
## [11593] "t" "c" "a" "g" "t" "g" "t" "a" "t" "a" "a" "t" "g" "c" "t" "a" "g" "t"
## [11611] "t" "t" "a" "t" "t" "g" "t" "t" "t" "c" "t" "t" "a" "g" "g" "c" "t" "a"
## [11629] "t" "t" "t" "t" "t" "g" "t" "a" "c" "t" "t" "g" "t" "t" "a" "c" "t" "t"
## [11647] "t" "g" "g" "c" "c" "t" "c" "t" "t" "t" "t" "g" "t" "t" "t" "a" "c" "t"
## [11665] "c" "a" "a" "c" "c" "g" "c" "t" "a" "c" "t" "t" "t" "a" "g" "a" "c" "t"
## [11683] "g" "a" "c" "t" "c" "t" "t" "g" "g" "t" "g" "t" "t" "t" "a" "t" "g" "a"
## [11701] "t" "t" "a" "c" "t" "t" "a" "g" "t" "t" "t" "c" "t" "a" "c" "a" "c" "a"
## [11719] "g" "g" "a" "g" "t" "t" "t" "a" "g" "a" "t" "a" "t" "a" "t" "g" "a" "a"
## [11737] "t" "t" "c" "a" "c" "a" "g" "g" "g" "a" "c" "t" "a" "c" "t" "c" "c" "c"
## [11755] "a" "c" "c" "c" "a" "a" "g" "a" "a" "t" "a" "g" "c" "a" "t" "a" "g" "a"
## [11773] "t" "g" "c" "c" "t" "t" "c" "a" "a" "a" "c" "t" "c" "a" "a" "c" "a" "t"
## [11791] "t" "a" "a" "a" "t" "t" "g" "t" "t" "g" "g" "g" "t" "g" "t" "t" "g" "g"
## [11809] "t" "g" "g" "c" "a" "a" "a" "c" "c" "t" "t" "g" "t" "a" "t" "c" "a" "a"
## [11827] "a" "g" "t" "a" "g" "c" "c" "a" "c" "t" "g" "t" "a" "c" "a" "g" "t" "c"
## [11845] "t" "a" "a" "a" "a" "t" "g" "t" "c" "a" "g" "a" "t" "g" "t" "a" "a" "a"
## [11863] "g" "t" "g" "c" "a" "c" "a" "t" "c" "a" "g" "t" "a" "g" "t" "c" "t" "t"
## [11881] "a" "c" "t" "c" "t" "c" "a" "g" "t" "t" "t" "t" "g" "c" "a" "a" "c" "a"
## [11899] "a" "c" "t" "c" "a" "g" "a" "g" "t" "a" "g" "a" "a" "t" "c" "a" "t" "c"
## [11917] "a" "t" "c" "t" "a" "a" "a" "t" "t" "g" "t" "g" "g" "g" "c" "t" "c" "a"
## [11935] "a" "t" "g" "t" "g" "t" "c" "c" "a" "g" "t" "t" "a" "c" "a" "c" "a" "a"
## [11953] "t" "g" "a" "c" "a" "t" "t" "c" "t" "c" "t" "t" "a" "g" "c" "t" "a" "a"
## [11971] "a" "g" "a" "t" "a" "c" "t" "a" "c" "t" "g" "a" "a" "g" "c" "c" "t" "t"
## [11989] "t" "g" "a" "a" "a" "a" "a" "a" "t" "g" "g" "t" "t" "t" "c" "a" "c" "t"
## [12007] "a" "c" "t" "t" "t" "c" "t" "g" "t" "t" "t" "t" "g" "c" "t" "t" "t" "c"
## [12025] "c" "a" "t" "g" "c" "a" "g" "g" "g" "t" "g" "c" "t" "g" "t" "a" "g" "a"
## [12043] "c" "a" "t" "a" "a" "a" "c" "a" "a" "g" "c" "t" "t" "t" "g" "t" "g" "a"
## [12061] "a" "g" "a" "a" "a" "t" "g" "c" "t" "g" "g" "a" "c" "a" "a" "c" "a" "g"
## [12079] "g" "g" "c" "a" "a" "c" "c" "t" "t" "a" "c" "a" "a" "g" "c" "t" "a" "t"
## [12097] "a" "g" "c" "c" "t" "c" "a" "g" "a" "g" "t" "t" "t" "a" "g" "t" "t" "c"
## [12115] "c" "c" "t" "t" "c" "c" "a" "t" "c" "a" "t" "a" "t" "g" "c" "a" "g" "c"
## [12133] "t" "t" "t" "t" "g" "c" "t" "a" "c" "t" "g" "c" "t" "c" "a" "a" "g" "a"
## [12151] "a" "g" "c" "t" "t" "a" "t" "g" "a" "g" "c" "a" "g" "g" "c" "t" "g" "t"
## [12169] "t" "g" "c" "t" "a" "a" "t" "g" "g" "t" "g" "a" "t" "t" "c" "t" "g" "a"
## [12187] "a" "g" "t" "t" "g" "t" "t" "c" "t" "t" "a" "a" "a" "a" "a" "g" "t" "t"
## [12205] "g" "a" "a" "g" "a" "a" "g" "t" "c" "t" "t" "t" "g" "a" "a" "t" "g" "t"
## [12223] "g" "g" "c" "t" "a" "a" "a" "t" "c" "t" "g" "a" "a" "t" "t" "t" "g" "a"
## [12241] "c" "c" "g" "t" "g" "a" "t" "g" "c" "a" "g" "c" "c" "a" "t" "g" "c" "a"
## [12259] "a" "c" "g" "t" "a" "a" "g" "t" "t" "g" "g" "a" "a" "a" "a" "g" "a" "t"
## [12277] "g" "g" "c" "t" "g" "a" "t" "c" "a" "a" "g" "c" "t" "a" "t" "g" "a" "c"
## [12295] "c" "c" "a" "a" "a" "t" "g" "t" "a" "t" "a" "a" "a" "c" "a" "g" "g" "c"
## [12313] "t" "a" "g" "a" "t" "c" "t" "g" "a" "g" "g" "a" "c" "a" "a" "g" "a" "g"
## [12331] "g" "g" "c" "a" "a" "a" "a" "g" "t" "t" "a" "c" "t" "a" "g" "t" "g" "c"
## [12349] "t" "a" "t" "g" "c" "a" "g" "a" "c" "a" "a" "t" "g" "c" "t" "t" "t" "t"
## [12367] "c" "a" "c" "t" "a" "t" "g" "c" "t" "t" "a" "g" "a" "a" "a" "g" "t" "t"
## [12385] "g" "g" "a" "t" "a" "a" "t" "g" "a" "t" "g" "c" "a" "c" "t" "c" "a" "a"
## [12403] "c" "a" "a" "c" "a" "t" "t" "a" "t" "c" "a" "a" "c" "a" "a" "t" "g" "c"
## [12421] "a" "a" "g" "a" "g" "a" "t" "g" "g" "t" "t" "g" "t" "g" "t" "t" "c" "c"
## [12439] "c" "t" "t" "g" "a" "a" "c" "a" "t" "a" "a" "t" "a" "c" "c" "t" "c" "t"
## [12457] "t" "a" "c" "a" "a" "c" "a" "g" "c" "a" "g" "c" "c" "a" "a" "a" "c" "t"
## [12475] "a" "a" "t" "g" "g" "t" "t" "g" "t" "c" "a" "t" "a" "c" "c" "a" "g" "a"
## [12493] "c" "t" "a" "t" "a" "a" "c" "a" "c" "a" "t" "a" "t" "a" "a" "a" "a" "a"
## [12511] "t" "a" "c" "g" "t" "g" "t" "g" "a" "t" "g" "g" "t" "a" "c" "a" "a" "c"
## [12529] "a" "t" "t" "t" "a" "c" "t" "t" "a" "t" "g" "c" "a" "t" "c" "a" "g" "c"
## [12547] "a" "t" "t" "g" "t" "g" "g" "g" "a" "a" "a" "t" "c" "c" "a" "a" "c" "a"
## [12565] "g" "g" "t" "t" "g" "t" "a" "g" "a" "t" "g" "c" "a" "g" "a" "t" "a" "g"
## [12583] "t" "a" "a" "a" "a" "t" "t" "g" "t" "t" "c" "a" "a" "c" "t" "t" "a" "g"
## [12601] "t" "g" "a" "a" "a" "t" "t" "a" "g" "t" "a" "t" "g" "g" "a" "c" "a" "a"
## [12619] "t" "t" "c" "a" "c" "c" "t" "a" "a" "t" "t" "t" "a" "g" "c" "a" "t" "g"
## [12637] "g" "c" "c" "t" "c" "t" "t" "a" "t" "t" "g" "t" "a" "a" "c" "a" "g" "c"
## [12655] "t" "t" "t" "a" "a" "g" "g" "g" "c" "c" "a" "a" "t" "t" "c" "t" "g" "c"
## [12673] "t" "g" "t" "c" "a" "a" "a" "t" "t" "a" "c" "a" "g" "a" "a" "t" "a" "a"
## [12691] "t" "g" "a" "g" "c" "t" "t" "a" "g" "t" "c" "c" "t" "g" "t" "t" "g" "c"
## [12709] "a" "c" "t" "a" "c" "g" "a" "c" "a" "g" "a" "t" "g" "t" "c" "t" "t" "g"
## [12727] "t" "g" "c" "t" "g" "c" "c" "g" "g" "t" "a" "c" "t" "a" "c" "a" "c" "a"
## [12745] "a" "a" "c" "t" "g" "c" "t" "t" "g" "c" "a" "c" "t" "g" "a" "t" "g" "a"
## [12763] "c" "a" "a" "t" "g" "c" "g" "t" "t" "a" "g" "c" "t" "t" "a" "c" "t" "a"
## [12781] "c" "a" "a" "c" "a" "c" "a" "a" "c" "a" "a" "a" "g" "g" "g" "a" "g" "g"
## [12799] "t" "a" "g" "g" "t" "t" "t" "g" "t" "a" "c" "t" "t" "g" "c" "a" "c" "t"
## [12817] "g" "t" "t" "a" "t" "c" "c" "g" "a" "t" "t" "t" "a" "c" "a" "g" "g" "a"
## [12835] "t" "t" "t" "g" "a" "a" "a" "t" "g" "g" "g" "c" "t" "a" "g" "a" "t" "t"
## [12853] "c" "c" "c" "t" "a" "a" "g" "a" "g" "t" "g" "a" "t" "g" "g" "a" "a" "c"
## [12871] "t" "g" "g" "t" "a" "c" "t" "a" "t" "c" "t" "a" "t" "a" "c" "a" "g" "a"
## [12889] "a" "c" "t" "g" "g" "a" "a" "c" "c" "a" "c" "c" "t" "t" "g" "t" "a" "g"
## [12907] "g" "t" "t" "t" "g" "t" "t" "a" "c" "a" "g" "a" "c" "a" "c" "a" "c" "c"
## [12925] "t" "a" "a" "a" "g" "g" "t" "c" "c" "t" "a" "a" "a" "g" "t" "g" "a" "a"
## [12943] "g" "t" "a" "t" "t" "t" "a" "t" "a" "c" "t" "t" "t" "a" "t" "t" "a" "a"
## [12961] "a" "g" "g" "a" "t" "t" "a" "a" "a" "c" "a" "a" "c" "c" "t" "a" "a" "a"
## [12979] "t" "a" "g" "a" "g" "g" "t" "a" "t" "g" "g" "t" "a" "c" "t" "t" "g" "g"
## [12997] "t" "a" "g" "t" "t" "t" "a" "g" "c" "t" "g" "c" "c" "a" "c" "a" "g" "t"
## [13015] "a" "c" "g" "t" "c" "t" "a" "c" "a" "a" "g" "c" "t" "g" "g" "t" "a" "a"
## [13033] "t" "g" "c" "a" "a" "c" "a" "g" "a" "a" "g" "t" "g" "c" "c" "t" "g" "c"
## [13051] "c" "a" "a" "t" "t" "c" "a" "a" "c" "t" "g" "t" "a" "t" "t" "a" "t" "c"
## [13069] "t" "t" "t" "c" "t" "g" "t" "g" "c" "t" "t" "t" "t" "g" "c" "t" "g" "t"
## [13087] "a" "g" "a" "t" "g" "c" "t" "g" "c" "t" "a" "a" "a" "g" "c" "t" "t" "a"
## [13105] "c" "a" "a" "a" "g" "a" "t" "t" "a" "t" "c" "t" "a" "g" "c" "t" "a" "g"
## [13123] "t" "g" "g" "g" "g" "g" "a" "c" "a" "a" "c" "c" "a" "a" "t" "c" "a" "c"
## [13141] "t" "a" "a" "t" "t" "g" "t" "g" "t" "t" "a" "a" "g" "a" "t" "g" "t" "t"
## [13159] "g" "t" "g" "t" "a" "c" "a" "c" "a" "c" "a" "c" "t" "g" "g" "t" "a" "c"
## [13177] "t" "g" "g" "t" "c" "a" "g" "g" "c" "a" "a" "t" "a" "a" "c" "a" "g" "t"
## [13195] "t" "a" "c" "a" "c" "c" "g" "g" "a" "a" "g" "c" "c" "a" "a" "t" "a" "t"
## [13213] "g" "g" "a" "t" "c" "a" "a" "g" "a" "a" "t" "c" "c" "t" "t" "t" "g" "g"
## [13231] "t" "g" "g" "t" "g" "c" "a" "t" "c" "g" "t" "g" "t" "t" "g" "t" "c" "t"
## [13249] "g" "t" "a" "c" "t" "g" "c" "c" "g" "t" "t" "g" "c" "c" "a" "c" "a" "t"
## [13267] "a" "g" "a" "t" "c" "a" "t" "c" "c" "a" "a" "a" "t" "c" "c" "t" "a" "a"
## [13285] "a" "g" "g" "a" "t" "t" "t" "t" "g" "t" "g" "a" "c" "t" "t" "a" "a" "a"
## [13303] "a" "g" "g" "t" "a" "a" "g" "t" "a" "t" "g" "t" "a" "c" "a" "a" "a" "t"
## [13321] "a" "c" "c" "t" "a" "c" "a" "a" "c" "t" "t" "g" "t" "g" "c" "t" "a" "a"
## [13339] "t" "g" "a" "c" "c" "c" "t" "g" "t" "g" "g" "g" "t" "t" "t" "t" "a" "c"
## [13357] "a" "c" "t" "t" "a" "a" "a" "a" "a" "c" "a" "c" "a" "g" "t" "c" "t" "g"
## [13375] "t" "a" "c" "c" "g" "t" "c" "t" "g" "c" "g" "g" "t" "a" "t" "g" "t" "g"
## [13393] "g" "a" "a" "a" "g" "g" "t" "t" "a" "t" "g" "g" "c" "t" "g" "t" "a" "g"
## [13411] "t" "t" "g" "t" "g" "a" "t" "c" "a" "a" "c" "t" "c" "c" "g" "c" "g" "a"
## [13429] "a" "c" "c" "c" "a" "t" "g" "c" "t" "t" "c" "a" "g" "t" "c" "a" "g" "c"
## [13447] "t" "g" "a" "t" "g" "c" "a" "c" "a" "a" "t" "c" "g" "t" "t" "t" "t" "t"
## [13465] "a" "a" "a" "c" "g" "g" "g" "t" "t" "t" "g" "c" "g" "g" "t" "g" "t" "a"
## [13483] "a" "g" "t" "g" "c" "a" "g" "c" "c" "c" "g" "t" "c" "t" "t" "a" "c" "a"
## [13501] "c" "c" "g" "t" "g" "c" "g" "g" "c" "a" "c" "a" "g" "g" "c" "a" "c" "t"
## [13519] "a" "g" "t" "a" "c" "t" "g" "a" "t" "g" "t" "c" "g" "t" "a" "t" "a" "c"
## [13537] "a" "g" "g" "g" "c" "t" "t" "t" "t" "g" "a" "c" "a" "t" "c" "t" "a" "c"
## [13555] "a" "a" "t" "g" "a" "t" "a" "a" "a" "g" "t" "a" "g" "c" "t" "g" "g" "t"
## [13573] "t" "t" "t" "g" "c" "t" "a" "a" "a" "t" "t" "c" "c" "t" "a" "a" "a" "a"
## [13591] "a" "c" "t" "a" "a" "t" "t" "g" "t" "t" "g" "t" "c" "g" "c" "t" "t" "c"
## [13609] "c" "a" "a" "g" "a" "a" "a" "a" "g" "g" "a" "c" "g" "a" "a" "g" "a" "t"
## [13627] "g" "a" "c" "a" "a" "t" "t" "t" "a" "a" "t" "t" "g" "a" "t" "t" "c" "t"
## [13645] "t" "a" "c" "t" "t" "t" "g" "t" "a" "g" "t" "t" "a" "a" "g" "a" "g" "a"
## [13663] "c" "a" "c" "a" "c" "t" "t" "t" "c" "t" "c" "t" "a" "a" "c" "t" "a" "c"
## [13681] "c" "a" "a" "c" "a" "t" "g" "a" "a" "g" "a" "a" "a" "c" "a" "a" "t" "t"
## [13699] "t" "a" "t" "a" "a" "t" "t" "t" "a" "c" "t" "t" "a" "a" "g" "g" "a" "t"
## [13717] "t" "g" "t" "c" "c" "a" "g" "c" "t" "g" "t" "t" "g" "c" "t" "a" "a" "a"
## [13735] "c" "a" "t" "g" "a" "c" "t" "t" "c" "t" "t" "t" "a" "a" "g" "t" "t" "t"
## [13753] "a" "g" "a" "a" "t" "a" "g" "a" "c" "g" "g" "t" "g" "a" "c" "a" "t" "g"
## [13771] "g" "t" "a" "c" "c" "a" "c" "a" "t" "a" "t" "a" "t" "c" "a" "c" "g" "t"
## [13789] "c" "a" "a" "c" "g" "t" "c" "t" "t" "a" "c" "t" "a" "a" "a" "t" "a" "c"
## [13807] "a" "c" "a" "a" "t" "g" "g" "c" "a" "g" "a" "c" "c" "t" "c" "g" "t" "c"
## [13825] "t" "a" "t" "g" "c" "t" "t" "t" "a" "a" "g" "g" "c" "a" "t" "t" "t" "t"
## [13843] "g" "a" "t" "g" "a" "a" "g" "g" "t" "a" "a" "t" "t" "g" "t" "g" "a" "c"
## [13861] "a" "c" "a" "t" "t" "a" "a" "a" "a" "g" "a" "a" "a" "t" "a" "c" "t" "t"
## [13879] "g" "t" "c" "a" "c" "a" "t" "a" "c" "a" "a" "t" "t" "g" "t" "t" "g" "t"
## [13897] "g" "a" "t" "g" "a" "t" "g" "a" "t" "t" "a" "t" "t" "t" "c" "a" "a" "t"
## [13915] "a" "a" "a" "a" "a" "g" "g" "a" "c" "t" "g" "g" "t" "a" "t" "g" "a" "t"
## [13933] "t" "t" "t" "g" "t" "a" "g" "a" "a" "a" "a" "c" "c" "c" "a" "g" "a" "t"
## [13951] "a" "t" "a" "t" "t" "a" "c" "g" "c" "g" "t" "a" "t" "a" "c" "g" "c" "c"
## [13969] "a" "a" "c" "t" "t" "a" "g" "g" "t" "g" "a" "a" "c" "g" "t" "g" "t" "a"
## [13987] "c" "g" "c" "c" "a" "a" "g" "c" "t" "t" "t" "g" "t" "t" "a" "a" "a" "a"
## [14005] "a" "c" "a" "g" "t" "a" "c" "a" "a" "t" "t" "c" "t" "g" "t" "g" "a" "t"
## [14023] "g" "c" "c" "a" "t" "g" "c" "g" "a" "a" "a" "t" "g" "c" "t" "g" "g" "t"
## [14041] "a" "t" "t" "g" "t" "t" "g" "g" "t" "g" "t" "a" "c" "t" "g" "a" "c" "a"
## [14059] "t" "t" "a" "g" "a" "t" "a" "a" "t" "c" "a" "a" "g" "a" "t" "c" "t" "c"
## [14077] "a" "a" "t" "g" "g" "t" "a" "a" "c" "t" "g" "g" "t" "a" "t" "g" "a" "t"
## [14095] "t" "t" "c" "g" "g" "t" "g" "a" "t" "t" "t" "c" "a" "t" "a" "c" "a" "a"
## [14113] "a" "c" "c" "a" "c" "g" "c" "c" "a" "g" "g" "t" "a" "g" "t" "g" "g" "a"
## [14131] "g" "t" "t" "c" "c" "t" "g" "t" "t" "g" "t" "a" "g" "a" "t" "t" "c" "t"
## [14149] "t" "a" "t" "t" "a" "t" "t" "c" "a" "t" "t" "g" "t" "t" "a" "a" "t" "g"
## [14167] "c" "c" "t" "a" "t" "a" "t" "t" "a" "a" "c" "c" "t" "t" "g" "a" "c" "c"
## [14185] "a" "g" "g" "g" "c" "t" "t" "t" "a" "a" "c" "t" "g" "c" "a" "g" "a" "g"
## [14203] "t" "c" "a" "c" "a" "t" "g" "t" "t" "g" "a" "c" "a" "c" "t" "g" "a" "c"
## [14221] "t" "t" "a" "a" "c" "a" "a" "a" "g" "c" "c" "t" "t" "a" "c" "a" "t" "t"
## [14239] "a" "a" "g" "t" "g" "g" "g" "a" "t" "t" "t" "g" "t" "t" "a" "a" "a" "a"
## [14257] "t" "a" "t" "g" "a" "c" "t" "t" "c" "a" "c" "g" "g" "a" "a" "g" "a" "g"
## [14275] "a" "g" "g" "t" "t" "a" "a" "a" "a" "c" "t" "c" "t" "t" "t" "g" "a" "c"
## [14293] "c" "g" "t" "t" "a" "t" "t" "t" "t" "a" "a" "a" "t" "a" "t" "t" "g" "g"
## [14311] "g" "a" "t" "c" "a" "g" "a" "c" "a" "t" "a" "c" "c" "a" "c" "c" "c" "a"
## [14329] "a" "a" "t" "t" "g" "t" "g" "t" "t" "a" "a" "c" "t" "g" "t" "t" "t" "g"
## [14347] "g" "a" "t" "g" "a" "c" "a" "g" "a" "t" "g" "c" "a" "t" "t" "c" "t" "g"
## [14365] "c" "a" "t" "t" "g" "t" "g" "c" "a" "a" "a" "c" "t" "t" "t" "a" "a" "t"
## [14383] "g" "t" "t" "t" "t" "a" "t" "t" "c" "t" "c" "t" "a" "c" "a" "g" "t" "g"
## [14401] "t" "t" "c" "c" "c" "a" "c" "c" "t" "a" "c" "a" "a" "g" "t" "t" "t" "t"
## [14419] "g" "g" "a" "c" "c" "a" "c" "t" "a" "g" "t" "g" "a" "g" "a" "a" "a" "a"
## [14437] "a" "t" "a" "t" "t" "t" "g" "t" "t" "g" "a" "t" "g" "g" "t" "g" "t" "t"
## [14455] "c" "c" "a" "t" "t" "t" "g" "t" "a" "g" "t" "t" "t" "c" "a" "a" "c" "t"
## [14473] "g" "g" "a" "t" "a" "c" "c" "a" "c" "t" "t" "c" "a" "g" "a" "g" "a" "g"
## [14491] "c" "t" "a" "g" "g" "t" "g" "t" "t" "g" "t" "a" "c" "a" "t" "a" "a" "t"
## [14509] "c" "a" "g" "g" "a" "t" "g" "t" "a" "a" "a" "c" "t" "t" "a" "c" "a" "t"
## [14527] "a" "g" "c" "t" "c" "t" "a" "g" "a" "c" "t" "t" "a" "g" "t" "t" "t" "t"
## [14545] "a" "a" "g" "g" "a" "a" "t" "t" "a" "c" "t" "t" "g" "t" "g" "t" "a" "t"
## [14563] "g" "c" "t" "g" "c" "t" "g" "a" "c" "c" "c" "t" "g" "c" "t" "a" "t" "g"
## [14581] "c" "a" "c" "g" "c" "t" "g" "c" "t" "t" "c" "t" "g" "g" "t" "a" "a" "t"
## [14599] "c" "t" "a" "t" "t" "a" "c" "t" "a" "g" "a" "t" "a" "a" "a" "c" "g" "c"
## [14617] "a" "c" "t" "a" "c" "g" "t" "g" "c" "t" "t" "t" "t" "c" "a" "g" "t" "a"
## [14635] "g" "c" "t" "g" "c" "a" "c" "t" "t" "a" "c" "t" "a" "a" "c" "a" "a" "t"
## [14653] "g" "t" "t" "g" "c" "t" "t" "t" "t" "c" "a" "a" "a" "c" "t" "g" "t" "c"
## [14671] "a" "a" "a" "c" "c" "c" "g" "g" "t" "a" "a" "t" "t" "t" "t" "a" "a" "c"
## [14689] "a" "a" "a" "g" "a" "c" "t" "t" "c" "t" "a" "t" "g" "a" "c" "t" "t" "t"
## [14707] "g" "c" "t" "g" "t" "g" "t" "c" "t" "a" "a" "g" "g" "g" "t" "t" "t" "c"
## [14725] "t" "t" "t" "a" "a" "g" "g" "a" "a" "g" "g" "a" "a" "g" "t" "t" "c" "t"
## [14743] "g" "t" "t" "g" "a" "a" "t" "t" "a" "a" "a" "a" "c" "a" "c" "t" "t" "c"
## [14761] "t" "t" "c" "t" "t" "t" "g" "c" "t" "c" "a" "g" "g" "a" "t" "g" "g" "t"
## [14779] "a" "a" "t" "g" "c" "t" "g" "c" "t" "a" "t" "c" "a" "g" "c" "g" "a" "t"
## [14797] "t" "a" "t" "g" "a" "c" "t" "a" "c" "t" "a" "t" "c" "g" "t" "t" "a" "t"
## [14815] "a" "a" "t" "c" "t" "a" "c" "c" "a" "a" "c" "a" "a" "t" "g" "t" "g" "t"
## [14833] "g" "a" "t" "a" "t" "c" "a" "g" "a" "c" "a" "a" "c" "t" "a" "c" "t" "a"
## [14851] "t" "t" "t" "g" "t" "a" "g" "t" "t" "g" "a" "a" "g" "t" "t" "g" "t" "t"
## [14869] "g" "a" "t" "a" "a" "g" "t" "a" "c" "t" "t" "t" "g" "a" "t" "t" "g" "t"
## [14887] "t" "a" "c" "g" "a" "t" "g" "g" "t" "g" "g" "c" "t" "g" "t" "a" "t" "t"
## [14905] "a" "a" "t" "g" "c" "t" "a" "a" "c" "c" "a" "a" "g" "t" "c" "a" "t" "c"
## [14923] "g" "t" "c" "a" "a" "c" "a" "a" "c" "c" "t" "a" "g" "a" "c" "a" "a" "a"
## [14941] "t" "c" "a" "g" "c" "t" "g" "g" "t" "t" "t" "t" "c" "c" "a" "t" "t" "t"
## [14959] "a" "a" "t" "a" "a" "a" "t" "g" "g" "g" "g" "t" "a" "a" "g" "g" "c" "t"
## [14977] "a" "g" "a" "c" "t" "t" "t" "a" "t" "t" "a" "t" "g" "a" "t" "t" "c" "a"
## [14995] "a" "t" "g" "a" "g" "t" "t" "a" "t" "g" "a" "g" "g" "a" "t" "c" "a" "a"
## [15013] "g" "a" "t" "g" "c" "a" "c" "t" "t" "t" "t" "c" "g" "c" "a" "t" "a" "t"
## [15031] "a" "c" "a" "a" "a" "a" "c" "g" "t" "a" "a" "t" "g" "t" "c" "a" "t" "c"
## [15049] "c" "c" "t" "a" "c" "t" "a" "t" "a" "a" "c" "t" "c" "a" "a" "a" "t" "g"
## [15067] "a" "a" "t" "c" "t" "t" "a" "a" "g" "t" "a" "t" "g" "c" "c" "a" "t" "t"
## [15085] "a" "g" "t" "g" "c" "a" "a" "a" "g" "a" "a" "t" "a" "g" "a" "g" "c" "t"
## [15103] "c" "g" "c" "a" "c" "c" "g" "t" "a" "g" "c" "t" "g" "g" "t" "g" "t" "c"
## [15121] "t" "c" "t" "a" "t" "c" "t" "g" "t" "a" "g" "t" "a" "c" "t" "a" "t" "g"
## [15139] "a" "c" "c" "a" "a" "t" "a" "g" "a" "c" "a" "g" "t" "t" "t" "c" "a" "t"
## [15157] "c" "a" "a" "a" "a" "a" "t" "t" "a" "t" "t" "g" "a" "a" "a" "t" "c" "a"
## [15175] "a" "t" "a" "g" "c" "c" "g" "c" "c" "a" "c" "t" "a" "g" "a" "g" "g" "a"
## [15193] "g" "c" "t" "a" "c" "t" "g" "t" "a" "g" "t" "a" "a" "t" "t" "g" "g" "a"
## [15211] "a" "c" "a" "a" "g" "c" "a" "a" "a" "t" "t" "c" "t" "a" "t" "g" "g" "t"
## [15229] "g" "g" "t" "t" "g" "g" "c" "a" "c" "a" "a" "c" "a" "t" "g" "t" "t" "a"
## [15247] "a" "a" "a" "a" "c" "t" "g" "t" "t" "t" "a" "t" "a" "g" "t" "g" "a" "t"
## [15265] "g" "t" "a" "g" "a" "a" "a" "a" "c" "c" "c" "t" "c" "a" "c" "c" "t" "t"
## [15283] "a" "t" "g" "g" "g" "t" "t" "g" "g" "g" "a" "t" "t" "a" "t" "c" "c" "t"
## [15301] "a" "a" "a" "t" "g" "t" "g" "a" "t" "a" "g" "a" "g" "c" "c" "a" "t" "g"
## [15319] "c" "c" "t" "a" "a" "c" "a" "t" "g" "c" "t" "t" "a" "g" "a" "a" "t" "t"
## [15337] "a" "t" "g" "g" "c" "c" "t" "c" "a" "c" "t" "t" "g" "t" "t" "c" "t" "t"
## [15355] "g" "c" "t" "c" "g" "c" "a" "a" "a" "c" "a" "t" "a" "c" "a" "a" "c" "g"
## [15373] "t" "g" "t" "t" "g" "t" "a" "g" "c" "t" "t" "g" "t" "c" "a" "c" "a" "c"
## [15391] "c" "g" "t" "t" "t" "c" "t" "a" "t" "a" "g" "a" "t" "t" "a" "g" "c" "t"
## [15409] "a" "a" "t" "g" "a" "g" "t" "g" "t" "g" "c" "t" "c" "a" "a" "g" "t" "a"
## [15427] "t" "t" "g" "a" "g" "t" "g" "a" "a" "a" "t" "g" "g" "t" "c" "a" "t" "g"
## [15445] "t" "g" "t" "g" "g" "c" "g" "g" "t" "t" "c" "a" "c" "t" "a" "t" "a" "t"
## [15463] "g" "t" "t" "a" "a" "a" "c" "c" "a" "g" "g" "t" "g" "g" "a" "a" "c" "c"
## [15481] "t" "c" "a" "t" "c" "a" "g" "g" "a" "g" "a" "t" "g" "c" "c" "a" "c" "a"
## [15499] "a" "c" "t" "g" "c" "t" "t" "a" "t" "g" "c" "t" "a" "a" "t" "a" "g" "t"
## [15517] "g" "t" "t" "t" "t" "t" "a" "a" "c" "a" "t" "t" "t" "g" "t" "c" "a" "a"
## [15535] "g" "c" "t" "g" "t" "c" "a" "c" "g" "g" "c" "c" "a" "a" "t" "g" "t" "t"
## [15553] "a" "a" "t" "g" "c" "a" "c" "t" "t" "t" "t" "a" "t" "c" "t" "a" "c" "t"
## [15571] "g" "a" "t" "g" "g" "t" "a" "a" "c" "a" "a" "a" "a" "t" "t" "g" "c" "c"
## [15589] "g" "a" "t" "a" "a" "g" "t" "a" "t" "g" "t" "c" "c" "g" "c" "a" "a" "t"
## [15607] "t" "t" "a" "c" "a" "a" "c" "a" "c" "a" "g" "a" "c" "t" "t" "t" "a" "t"
## [15625] "g" "a" "g" "t" "g" "t" "c" "t" "c" "t" "a" "t" "a" "g" "a" "a" "a" "t"
## [15643] "a" "g" "a" "g" "a" "t" "g" "t" "t" "g" "a" "c" "a" "c" "a" "g" "a" "c"
## [15661] "t" "t" "t" "g" "t" "g" "a" "a" "t" "g" "a" "g" "t" "t" "t" "t" "a" "c"
## [15679] "g" "c" "a" "t" "a" "t" "t" "t" "g" "c" "g" "t" "a" "a" "a" "c" "a" "t"
## [15697] "t" "t" "c" "t" "c" "a" "a" "t" "g" "a" "t" "g" "a" "t" "a" "c" "t" "c"
## [15715] "t" "c" "t" "g" "a" "c" "g" "a" "t" "g" "c" "t" "g" "t" "t" "g" "t" "g"
## [15733] "t" "g" "t" "t" "t" "c" "a" "a" "t" "a" "g" "c" "a" "c" "t" "t" "a" "t"
## [15751] "g" "c" "a" "t" "c" "t" "c" "a" "a" "g" "g" "t" "c" "t" "a" "g" "t" "g"
## [15769] "g" "c" "t" "a" "g" "c" "a" "t" "a" "a" "a" "g" "a" "a" "c" "t" "t" "t"
## [15787] "a" "a" "g" "t" "c" "a" "g" "t" "t" "c" "t" "t" "t" "a" "t" "t" "a" "t"
## [15805] "c" "a" "a" "a" "a" "c" "a" "a" "t" "g" "t" "t" "t" "t" "t" "a" "t" "g"
## [15823] "t" "c" "t" "g" "a" "a" "g" "c" "a" "a" "a" "a" "t" "g" "t" "t" "g" "g"
## [15841] "a" "c" "t" "g" "a" "g" "a" "c" "t" "g" "a" "c" "c" "t" "t" "a" "c" "t"
## [15859] "a" "a" "a" "g" "g" "a" "c" "c" "t" "c" "a" "t" "g" "a" "a" "t" "t" "t"
## [15877] "t" "g" "c" "t" "c" "t" "c" "a" "a" "c" "a" "t" "a" "c" "a" "a" "t" "g"
## [15895] "c" "t" "a" "g" "t" "t" "a" "a" "a" "c" "a" "g" "g" "g" "t" "g" "a" "t"
## [15913] "g" "a" "t" "t" "a" "t" "g" "t" "g" "t" "a" "c" "c" "t" "t" "c" "c" "t"
## [15931] "t" "a" "c" "c" "c" "a" "g" "a" "t" "c" "c" "a" "t" "c" "a" "a" "g" "a"
## [15949] "a" "t" "c" "c" "t" "a" "g" "g" "g" "g" "c" "c" "g" "g" "c" "t" "g" "t"
## [15967] "t" "t" "t" "g" "t" "a" "g" "a" "t" "g" "a" "t" "a" "t" "c" "g" "t" "a"
## [15985] "a" "a" "a" "a" "c" "a" "g" "a" "t" "g" "g" "t" "a" "c" "a" "c" "t" "t"
## [16003] "a" "t" "g" "a" "t" "t" "g" "a" "a" "c" "g" "g" "t" "t" "c" "g" "t" "g"
## [16021] "t" "c" "t" "t" "t" "a" "g" "c" "t" "a" "t" "a" "g" "a" "t" "g" "c" "t"
## [16039] "t" "a" "c" "c" "c" "a" "c" "t" "t" "a" "c" "t" "a" "a" "a" "c" "a" "t"
## [16057] "c" "c" "t" "a" "a" "t" "c" "a" "g" "g" "a" "g" "t" "a" "t" "g" "c" "t"
## [16075] "g" "a" "t" "g" "t" "c" "t" "t" "t" "c" "a" "t" "t" "t" "g" "t" "a" "c"
## [16093] "t" "t" "a" "c" "a" "a" "t" "a" "c" "a" "t" "a" "a" "g" "a" "a" "a" "g"
## [16111] "c" "t" "a" "c" "a" "t" "g" "a" "t" "g" "a" "g" "t" "t" "a" "a" "c" "a"
## [16129] "g" "g" "a" "c" "a" "c" "a" "t" "g" "t" "t" "a" "g" "a" "c" "a" "t" "g"
## [16147] "t" "a" "t" "t" "c" "t" "g" "t" "t" "a" "t" "g" "c" "t" "t" "a" "c" "t"
## [16165] "a" "a" "t" "g" "a" "t" "a" "a" "c" "a" "c" "t" "t" "c" "a" "a" "g" "g"
## [16183] "t" "a" "t" "t" "g" "g" "g" "a" "a" "c" "c" "t" "g" "a" "g" "t" "t" "t"
## [16201] "t" "a" "t" "g" "a" "g" "g" "c" "t" "a" "t" "g" "t" "a" "c" "a" "c" "a"
## [16219] "c" "c" "g" "c" "a" "t" "a" "c" "a" "g" "t" "c" "t" "t" "a" "c" "a" "g"
## [16237] "g" "c" "t" "g" "t" "t" "g" "g" "g" "g" "c" "t" "t" "g" "t" "g" "t" "t"
## [16255] "c" "t" "t" "t" "g" "c" "a" "a" "t" "t" "c" "a" "c" "a" "g" "a" "c" "t"
## [16273] "t" "c" "a" "t" "t" "a" "a" "g" "a" "t" "g" "t" "g" "g" "t" "g" "c" "t"
## [16291] "t" "g" "c" "a" "t" "a" "c" "g" "t" "a" "g" "a" "c" "c" "a" "t" "t" "c"
## [16309] "t" "t" "a" "t" "g" "t" "t" "g" "t" "a" "a" "a" "t" "g" "c" "t" "g" "t"
## [16327] "t" "a" "c" "g" "a" "c" "c" "a" "t" "g" "t" "c" "a" "t" "a" "t" "c" "a"
## [16345] "a" "c" "a" "t" "c" "a" "c" "a" "t" "a" "a" "a" "t" "t" "a" "g" "t" "c"
## [16363] "t" "t" "g" "t" "c" "t" "g" "t" "t" "a" "a" "t" "c" "c" "g" "t" "a" "t"
## [16381] "g" "t" "t" "t" "g" "c" "a" "a" "t" "g" "c" "t" "c" "c" "a" "g" "g" "t"
## [16399] "t" "g" "t" "g" "a" "t" "g" "t" "c" "a" "c" "a" "g" "a" "t" "g" "t" "g"
## [16417] "a" "c" "t" "c" "a" "a" "c" "t" "t" "t" "a" "c" "t" "t" "a" "g" "g" "a"
## [16435] "g" "g" "t" "a" "t" "g" "a" "g" "c" "t" "a" "t" "t" "a" "t" "t" "g" "t"
## [16453] "a" "a" "a" "t" "c" "a" "c" "a" "t" "a" "a" "a" "c" "c" "a" "c" "c" "c"
## [16471] "a" "t" "t" "a" "g" "t" "t" "t" "t" "c" "c" "a" "t" "t" "g" "t" "g" "t"
## [16489] "g" "c" "t" "a" "a" "t" "g" "g" "a" "c" "a" "a" "g" "t" "t" "t" "t" "t"
## [16507] "g" "g" "t" "t" "t" "a" "t" "a" "t" "a" "a" "a" "a" "a" "t" "a" "c" "a"
## [16525] "t" "g" "t" "g" "t" "t" "g" "g" "t" "a" "g" "c" "g" "a" "t" "a" "a" "t"
## [16543] "g" "t" "t" "a" "c" "t" "g" "a" "c" "t" "t" "t" "a" "a" "t" "g" "c" "a"
## [16561] "a" "t" "t" "g" "c" "a" "a" "c" "a" "t" "g" "t" "g" "a" "c" "t" "g" "g"
## [16579] "a" "c" "a" "a" "a" "t" "g" "c" "t" "g" "g" "t" "g" "a" "t" "t" "a" "c"
## [16597] "a" "t" "t" "t" "t" "a" "g" "c" "t" "a" "a" "c" "a" "c" "c" "t" "g" "t"
## [16615] "a" "c" "t" "g" "a" "a" "a" "g" "a" "c" "t" "c" "a" "a" "g" "c" "t" "t"
## [16633] "t" "t" "t" "g" "c" "a" "g" "c" "a" "g" "a" "a" "a" "c" "g" "c" "t" "c"
## [16651] "a" "a" "a" "g" "c" "t" "a" "c" "t" "g" "a" "g" "g" "a" "g" "a" "c" "a"
## [16669] "t" "t" "t" "a" "a" "a" "c" "t" "g" "t" "c" "t" "t" "a" "t" "g" "g" "t"
## [16687] "a" "t" "t" "g" "c" "t" "a" "c" "t" "g" "t" "a" "c" "g" "t" "g" "a" "a"
## [16705] "g" "t" "g" "c" "t" "g" "t" "c" "t" "g" "a" "c" "a" "g" "a" "g" "a" "a"
## [16723] "t" "t" "a" "c" "a" "t" "c" "t" "t" "t" "c" "a" "t" "g" "g" "g" "a" "a"
## [16741] "g" "t" "t" "g" "g" "t" "a" "a" "a" "c" "c" "t" "a" "g" "a" "c" "c" "a"
## [16759] "c" "c" "a" "c" "t" "t" "a" "a" "c" "c" "g" "a" "a" "a" "t" "t" "a" "t"
## [16777] "g" "t" "c" "t" "t" "t" "a" "c" "t" "g" "g" "t" "t" "a" "t" "c" "g" "t"
## [16795] "g" "t" "a" "a" "c" "t" "a" "a" "a" "a" "a" "c" "a" "g" "t" "a" "a" "a"
## [16813] "g" "t" "a" "c" "a" "a" "a" "t" "a" "g" "g" "a" "g" "a" "g" "t" "a" "c"
## [16831] "a" "c" "c" "t" "t" "t" "g" "a" "a" "a" "a" "a" "g" "g" "t" "g" "a" "c"
## [16849] "t" "a" "t" "g" "g" "t" "g" "a" "t" "g" "c" "t" "g" "t" "t" "g" "t" "t"
## [16867] "t" "a" "c" "c" "g" "a" "g" "g" "t" "a" "c" "a" "a" "c" "a" "a" "c" "t"
## [16885] "t" "a" "c" "a" "a" "a" "t" "t" "a" "a" "a" "t" "g" "t" "t" "g" "g" "t"
## [16903] "g" "a" "t" "t" "a" "t" "t" "t" "t" "g" "t" "g" "c" "t" "g" "a" "c" "a"
## [16921] "t" "c" "a" "c" "a" "t" "a" "c" "a" "g" "t" "a" "a" "t" "g" "c" "c" "a"
## [16939] "t" "t" "a" "a" "g" "t" "g" "c" "a" "c" "c" "t" "a" "c" "a" "c" "t" "a"
## [16957] "g" "t" "g" "c" "c" "a" "c" "a" "a" "g" "a" "g" "c" "a" "c" "t" "a" "t"
## [16975] "g" "t" "t" "a" "g" "a" "a" "t" "t" "a" "c" "t" "g" "g" "c" "t" "t" "a"
## [16993] "t" "a" "c" "c" "c" "a" "a" "c" "a" "c" "t" "c" "a" "a" "t" "a" "t" "c"
## [17011] "t" "c" "a" "g" "a" "t" "g" "a" "g" "t" "t" "t" "t" "c" "t" "a" "g" "c"
## [17029] "a" "a" "t" "g" "t" "t" "g" "c" "a" "a" "a" "t" "t" "a" "t" "c" "a" "a"
## [17047] "a" "a" "g" "g" "t" "t" "g" "g" "t" "a" "t" "g" "c" "a" "a" "a" "a" "g"
## [17065] "t" "a" "t" "t" "c" "t" "a" "c" "a" "c" "t" "c" "c" "a" "g" "g" "g" "a"
## [17083] "c" "c" "a" "c" "c" "t" "g" "g" "t" "a" "c" "t" "g" "g" "t" "a" "a" "g"
## [17101] "a" "g" "t" "c" "a" "t" "t" "t" "t" "g" "c" "t" "a" "t" "t" "g" "g" "c"
## [17119] "c" "t" "a" "g" "c" "t" "c" "t" "c" "t" "a" "c" "t" "a" "c" "c" "c" "t"
## [17137] "t" "c" "t" "g" "c" "t" "c" "g" "c" "a" "t" "a" "g" "t" "g" "t" "a" "t"
## [17155] "a" "c" "a" "g" "c" "t" "t" "g" "c" "t" "c" "t" "c" "a" "t" "g" "c" "c"
## [17173] "g" "c" "t" "g" "t" "t" "g" "a" "t" "g" "c" "a" "c" "t" "a" "t" "g" "t"
## [17191] "g" "a" "g" "a" "a" "g" "g" "c" "a" "t" "t" "a" "a" "a" "a" "t" "a" "t"
## [17209] "t" "t" "g" "c" "c" "t" "a" "t" "a" "g" "a" "t" "a" "a" "a" "t" "g" "t"
## [17227] "a" "g" "t" "a" "g" "a" "a" "t" "t" "a" "t" "a" "c" "c" "t" "g" "c" "a"
## [17245] "c" "g" "t" "g" "c" "t" "c" "g" "t" "g" "t" "a" "g" "a" "g" "t" "g" "t"
## [17263] "t" "t" "t" "g" "a" "t" "a" "a" "a" "t" "t" "c" "a" "a" "a" "g" "t" "g"
## [17281] "a" "a" "t" "t" "c" "a" "a" "c" "a" "t" "t" "a" "g" "a" "a" "c" "a" "g"
## [17299] "t" "a" "t" "g" "t" "c" "t" "t" "t" "t" "g" "t" "a" "c" "t" "g" "t" "a"
## [17317] "a" "a" "t" "g" "c" "a" "t" "t" "g" "c" "c" "t" "g" "a" "g" "a" "c" "g"
## [17335] "a" "c" "a" "g" "c" "a" "g" "a" "t" "a" "t" "a" "g" "t" "t" "g" "t" "c"
## [17353] "t" "t" "t" "g" "a" "t" "g" "a" "a" "a" "t" "t" "t" "c" "a" "a" "t" "g"
## [17371] "g" "c" "c" "a" "c" "a" "a" "a" "t" "t" "a" "t" "g" "a" "t" "t" "t" "g"
## [17389] "a" "g" "t" "g" "t" "t" "g" "t" "c" "a" "a" "t" "g" "c" "c" "a" "g" "a"
## [17407] "t" "t" "a" "c" "g" "t" "g" "c" "t" "a" "a" "g" "c" "a" "c" "t" "a" "t"
## [17425] "g" "t" "g" "t" "a" "c" "a" "t" "t" "g" "g" "c" "g" "a" "c" "c" "c" "t"
## [17443] "g" "c" "t" "c" "a" "a" "t" "t" "a" "c" "c" "t" "g" "c" "a" "c" "c" "a"
## [17461] "c" "g" "c" "a" "c" "a" "t" "t" "g" "c" "t" "a" "a" "c" "t" "a" "a" "g"
## [17479] "g" "g" "c" "a" "c" "a" "c" "t" "a" "g" "a" "a" "c" "c" "a" "g" "a" "a"
## [17497] "t" "a" "t" "t" "t" "c" "a" "a" "t" "t" "c" "a" "g" "t" "g" "t" "g" "t"
## [17515] "a" "g" "a" "c" "t" "t" "a" "t" "g" "a" "a" "a" "a" "c" "t" "a" "t" "a"
## [17533] "g" "g" "t" "c" "c" "a" "g" "a" "c" "a" "t" "g" "t" "t" "c" "c" "t" "c"
## [17551] "g" "g" "a" "a" "c" "t" "t" "g" "t" "c" "g" "g" "c" "g" "t" "t" "g" "t"
## [17569] "c" "c" "t" "g" "c" "t" "g" "a" "a" "a" "t" "t" "g" "t" "t" "g" "a" "c"
## [17587] "a" "c" "t" "g" "t" "g" "a" "g" "t" "g" "c" "t" "t" "t" "g" "g" "t" "t"
## [17605] "t" "a" "t" "g" "a" "t" "a" "a" "t" "a" "a" "g" "c" "t" "t" "a" "a" "a"
## [17623] "g" "c" "a" "c" "a" "t" "a" "a" "a" "g" "a" "c" "a" "a" "a" "t" "c" "a"
## [17641] "g" "c" "t" "c" "a" "a" "t" "g" "c" "t" "t" "t" "a" "a" "a" "a" "t" "g"
## [17659] "t" "t" "t" "t" "a" "t" "a" "a" "g" "g" "g" "t" "g" "t" "t" "a" "t" "c"
## [17677] "a" "c" "g" "c" "a" "t" "g" "a" "t" "g" "t" "t" "t" "c" "a" "t" "c" "t"
## [17695] "g" "c" "a" "a" "t" "t" "a" "a" "c" "a" "g" "g" "c" "c" "a" "c" "a" "a"
## [17713] "a" "t" "a" "g" "g" "c" "g" "t" "g" "g" "t" "a" "a" "g" "a" "g" "a" "a"
## [17731] "t" "t" "c" "c" "t" "t" "a" "c" "a" "c" "g" "t" "a" "a" "c" "c" "c" "t"
## [17749] "g" "c" "t" "t" "g" "g" "a" "g" "a" "a" "a" "a" "g" "c" "t" "g" "t" "c"
## [17767] "t" "t" "t" "a" "t" "t" "t" "c" "a" "c" "c" "t" "t" "a" "t" "a" "a" "t"
## [17785] "t" "c" "a" "c" "a" "g" "a" "a" "t" "g" "c" "t" "g" "t" "a" "g" "c" "c"
## [17803] "t" "c" "a" "a" "a" "g" "a" "t" "t" "t" "t" "g" "g" "g" "a" "c" "t" "a"
## [17821] "c" "c" "a" "a" "c" "t" "c" "a" "a" "a" "c" "t" "g" "t" "t" "g" "a" "t"
## [17839] "t" "c" "a" "t" "c" "a" "c" "a" "g" "g" "g" "c" "t" "c" "a" "g" "a" "a"
## [17857] "t" "a" "t" "g" "a" "c" "t" "a" "t" "g" "t" "c" "a" "t" "a" "t" "t" "c"
## [17875] "a" "c" "t" "c" "a" "a" "a" "c" "c" "a" "c" "t" "g" "a" "a" "a" "c" "a"
## [17893] "g" "c" "t" "c" "a" "c" "t" "c" "t" "t" "g" "t" "a" "a" "t" "g" "t" "a"
## [17911] "a" "a" "c" "a" "g" "a" "t" "t" "t" "a" "a" "t" "g" "t" "t" "g" "c" "t"
## [17929] "a" "t" "t" "a" "c" "c" "a" "g" "a" "g" "c" "a" "a" "a" "a" "g" "t" "a"
## [17947] "g" "g" "c" "a" "t" "a" "c" "t" "t" "t" "g" "c" "a" "t" "a" "a" "t" "g"
## [17965] "t" "c" "t" "g" "a" "t" "a" "g" "a" "g" "a" "c" "c" "t" "t" "t" "a" "t"
## [17983] "g" "a" "c" "a" "a" "g" "t" "t" "g" "c" "a" "a" "t" "t" "t" "a" "c" "a"
## [18001] "a" "g" "t" "c" "t" "t" "g" "a" "a" "a" "t" "t" "c" "c" "a" "c" "g" "t"
## [18019] "a" "g" "g" "a" "a" "t" "g" "t" "g" "g" "c" "a" "a" "c" "t" "t" "t" "a"
## [18037] "c" "a" "a" "g" "c" "t" "g" "a" "a" "a" "a" "t" "g" "t" "a" "a" "c" "a"
## [18055] "g" "g" "a" "c" "t" "c" "t" "t" "t" "a" "a" "a" "g" "a" "t" "t" "g" "t"
## [18073] "a" "g" "t" "a" "a" "g" "g" "t" "a" "a" "t" "c" "a" "c" "t" "g" "g" "g"
## [18091] "t" "t" "a" "c" "a" "t" "c" "c" "t" "a" "c" "a" "c" "a" "g" "g" "c" "a"
## [18109] "c" "c" "t" "a" "c" "a" "c" "a" "c" "c" "t" "c" "a" "g" "t" "g" "t" "t"
## [18127] "g" "a" "c" "a" "c" "t" "a" "a" "a" "t" "t" "c" "a" "a" "a" "a" "c" "t"
## [18145] "g" "a" "a" "g" "g" "t" "t" "t" "a" "t" "g" "t" "g" "t" "t" "g" "a" "c"
## [18163] "a" "t" "a" "c" "c" "t" "g" "g" "c" "a" "t" "a" "c" "c" "t" "a" "a" "g"
## [18181] "g" "a" "c" "a" "t" "g" "a" "c" "c" "t" "a" "t" "a" "g" "a" "a" "g" "a"
## [18199] "c" "t" "c" "a" "t" "c" "t" "c" "t" "a" "t" "g" "a" "t" "g" "g" "g" "t"
## [18217] "t" "t" "t" "a" "a" "a" "a" "t" "g" "a" "a" "t" "t" "a" "t" "c" "a" "a"
## [18235] "g" "t" "t" "a" "a" "t" "g" "g" "t" "t" "a" "c" "c" "c" "t" "a" "a" "c"
## [18253] "a" "t" "g" "t" "t" "t" "a" "t" "c" "a" "c" "c" "c" "g" "c" "g" "a" "a"
## [18271] "g" "a" "a" "g" "c" "t" "a" "t" "a" "a" "g" "a" "c" "a" "t" "g" "t" "a"
## [18289] "c" "g" "t" "g" "c" "a" "t" "g" "g" "a" "t" "t" "g" "g" "c" "t" "t" "c"
## [18307] "g" "a" "t" "g" "t" "c" "g" "a" "g" "g" "g" "g" "t" "g" "t" "c" "a" "t"
## [18325] "g" "c" "t" "a" "c" "t" "a" "g" "a" "g" "a" "a" "g" "c" "t" "g" "t" "t"
## [18343] "g" "g" "t" "a" "c" "c" "a" "a" "t" "t" "t" "a" "c" "c" "t" "t" "t" "a"
## [18361] "c" "a" "g" "c" "t" "a" "g" "g" "t" "t" "t" "t" "t" "c" "t" "a" "c" "a"
## [18379] "g" "g" "t" "g" "t" "t" "a" "a" "c" "c" "t" "a" "g" "t" "t" "g" "c" "t"
## [18397] "g" "t" "a" "c" "c" "t" "a" "c" "a" "g" "g" "t" "t" "a" "t" "g" "t" "t"
## [18415] "g" "a" "t" "a" "c" "a" "c" "c" "t" "a" "a" "t" "a" "a" "t" "a" "c" "a"
## [18433] "g" "a" "t" "t" "t" "t" "t" "c" "c" "a" "g" "a" "g" "t" "t" "a" "g" "t"
## [18451] "g" "c" "t" "a" "a" "a" "c" "c" "a" "c" "c" "g" "c" "c" "t" "g" "g" "a"
## [18469] "g" "a" "t" "c" "a" "a" "t" "t" "t" "a" "a" "a" "c" "a" "c" "c" "t" "c"
## [18487] "a" "t" "a" "c" "c" "a" "c" "t" "t" "a" "t" "g" "t" "a" "c" "a" "a" "a"
## [18505] "g" "g" "a" "c" "t" "t" "c" "c" "t" "t" "g" "g" "a" "a" "t" "g" "t" "a"
## [18523] "g" "t" "g" "c" "g" "t" "a" "t" "a" "a" "a" "g" "a" "t" "t" "g" "t" "a"
## [18541] "c" "a" "a" "a" "t" "g" "t" "t" "a" "a" "g" "t" "g" "a" "c" "a" "c" "a"
## [18559] "c" "t" "t" "a" "a" "a" "a" "a" "t" "c" "t" "c" "t" "c" "t" "g" "a" "c"
## [18577] "a" "g" "a" "g" "t" "c" "g" "t" "a" "t" "t" "t" "g" "t" "c" "t" "t" "a"
## [18595] "t" "g" "g" "g" "c" "a" "c" "a" "t" "g" "g" "c" "t" "t" "t" "g" "a" "g"
## [18613] "t" "t" "g" "a" "c" "a" "t" "c" "t" "a" "t" "g" "a" "a" "g" "t" "a" "t"
## [18631] "t" "t" "t" "g" "t" "g" "a" "a" "a" "a" "t" "a" "g" "g" "a" "c" "c" "t"
## [18649] "g" "a" "g" "c" "g" "c" "a" "c" "c" "t" "g" "t" "t" "g" "t" "c" "t" "a"
## [18667] "t" "g" "t" "g" "a" "t" "a" "g" "a" "c" "g" "t" "g" "c" "c" "a" "c" "a"
## [18685] "t" "g" "c" "t" "t" "t" "t" "c" "c" "a" "c" "t" "g" "c" "t" "t" "c" "a"
## [18703] "g" "a" "c" "a" "c" "t" "t" "a" "t" "g" "c" "c" "t" "g" "t" "t" "g" "g"
## [18721] "c" "a" "t" "c" "a" "t" "t" "c" "t" "a" "t" "t" "g" "g" "a" "t" "t" "t"
## [18739] "g" "a" "t" "t" "a" "c" "g" "t" "c" "t" "a" "t" "a" "a" "t" "c" "c" "g"
## [18757] "t" "t" "t" "a" "t" "g" "a" "t" "t" "g" "a" "t" "g" "t" "t" "c" "a" "a"
## [18775] "c" "a" "a" "t" "g" "g" "g" "g" "t" "t" "t" "t" "a" "c" "a" "g" "g" "t"
## [18793] "a" "a" "c" "c" "t" "a" "c" "a" "a" "a" "g" "c" "a" "a" "c" "c" "a" "t"
## [18811] "g" "a" "t" "c" "t" "g" "t" "a" "t" "t" "g" "t" "c" "a" "a" "g" "t" "c"
## [18829] "c" "a" "t" "g" "g" "t" "a" "a" "t" "g" "c" "a" "c" "a" "t" "g" "t" "a"
## [18847] "g" "c" "t" "a" "g" "t" "t" "g" "t" "g" "a" "t" "g" "c" "a" "a" "t" "c"
## [18865] "a" "t" "g" "a" "c" "t" "a" "g" "g" "t" "g" "t" "c" "t" "a" "g" "c" "t"
## [18883] "g" "t" "c" "c" "a" "c" "g" "a" "g" "t" "g" "c" "t" "t" "t" "g" "t" "t"
## [18901] "a" "a" "g" "c" "g" "t" "g" "t" "t" "g" "a" "c" "t" "g" "g" "a" "c" "t"
## [18919] "a" "t" "t" "g" "a" "a" "t" "a" "t" "c" "c" "t" "a" "t" "a" "a" "t" "t"
## [18937] "g" "g" "t" "g" "a" "t" "g" "a" "a" "c" "t" "g" "a" "a" "g" "a" "t" "t"
## [18955] "a" "a" "t" "g" "c" "g" "g" "c" "t" "t" "g" "t" "a" "g" "a" "a" "a" "g"
## [18973] "g" "t" "t" "c" "a" "a" "c" "a" "c" "a" "t" "g" "g" "t" "t" "g" "t" "t"
## [18991] "a" "a" "a" "g" "c" "t" "g" "c" "a" "t" "t" "a" "t" "t" "a" "g" "c" "a"
## [19009] "g" "a" "c" "a" "a" "a" "t" "t" "c" "c" "c" "a" "g" "t" "t" "c" "t" "t"
## [19027] "c" "a" "c" "g" "a" "c" "a" "t" "t" "g" "g" "t" "a" "a" "c" "c" "c" "t"
## [19045] "a" "a" "a" "g" "c" "t" "a" "t" "t" "a" "a" "g" "t" "g" "t" "g" "t" "a"
## [19063] "c" "c" "t" "c" "a" "a" "g" "c" "t" "g" "a" "t" "g" "t" "a" "g" "a" "a"
## [19081] "t" "g" "g" "a" "a" "g" "t" "t" "c" "t" "a" "t" "g" "a" "t" "g" "c" "a"
## [19099] "c" "a" "g" "c" "c" "t" "t" "g" "t" "a" "g" "t" "g" "a" "c" "a" "a" "a"
## [19117] "g" "c" "t" "t" "a" "t" "a" "a" "a" "a" "t" "a" "g" "a" "a" "g" "a" "a"
## [19135] "t" "t" "a" "t" "t" "c" "t" "a" "t" "t" "c" "t" "t" "a" "t" "g" "c" "c"
## [19153] "a" "c" "a" "c" "a" "t" "t" "c" "t" "g" "a" "c" "a" "a" "a" "t" "t" "c"
## [19171] "a" "c" "a" "g" "a" "t" "g" "g" "t" "g" "t" "a" "t" "g" "c" "c" "t" "a"
## [19189] "t" "t" "t" "t" "g" "g" "a" "a" "t" "t" "g" "c" "a" "a" "t" "g" "t" "c"
## [19207] "g" "a" "t" "a" "g" "a" "t" "a" "t" "c" "c" "t" "g" "c" "t" "a" "a" "t"
## [19225] "t" "c" "c" "a" "t" "t" "g" "t" "t" "t" "g" "t" "a" "g" "a" "t" "t" "t"
## [19243] "g" "a" "c" "a" "c" "t" "a" "g" "a" "g" "t" "g" "c" "t" "a" "t" "c" "t"
## [19261] "a" "a" "c" "c" "t" "t" "a" "a" "c" "t" "t" "g" "c" "c" "t" "g" "g" "t"
## [19279] "t" "g" "t" "g" "a" "t" "g" "g" "t" "g" "g" "c" "a" "g" "t" "t" "t" "g"
## [19297] "t" "a" "t" "g" "t" "a" "a" "a" "t" "a" "a" "a" "c" "a" "t" "g" "c" "a"
## [19315] "t" "t" "c" "c" "a" "c" "a" "c" "a" "c" "c" "a" "g" "c" "t" "t" "t" "t"
## [19333] "g" "a" "t" "a" "a" "a" "a" "g" "t" "g" "c" "t" "t" "t" "t" "g" "t" "t"
## [19351] "a" "a" "t" "t" "t" "a" "a" "a" "a" "c" "a" "a" "t" "t" "a" "c" "c" "a"
## [19369] "t" "t" "t" "t" "t" "c" "t" "a" "t" "t" "a" "c" "t" "c" "t" "g" "a" "c"
## [19387] "a" "g" "t" "c" "c" "a" "t" "g" "t" "g" "a" "g" "t" "c" "t" "c" "a" "t"
## [19405] "g" "g" "a" "a" "a" "a" "c" "a" "a" "g" "t" "a" "g" "t" "g" "t" "c" "a"
## [19423] "g" "a" "t" "a" "t" "a" "g" "a" "t" "t" "a" "t" "g" "t" "a" "c" "c" "a"
## [19441] "c" "t" "a" "a" "a" "g" "t" "c" "t" "g" "c" "t" "a" "c" "g" "t" "g" "t"
## [19459] "a" "t" "a" "a" "c" "a" "c" "g" "t" "t" "g" "c" "a" "a" "t" "t" "t" "a"
## [19477] "g" "g" "t" "g" "g" "t" "g" "c" "t" "g" "t" "c" "t" "g" "t" "a" "g" "a"
## [19495] "c" "a" "t" "c" "a" "t" "g" "c" "t" "a" "a" "t" "g" "a" "g" "t" "a" "c"
## [19513] "a" "g" "a" "t" "t" "g" "t" "a" "t" "c" "t" "c" "g" "a" "t" "g" "c" "t"
## [19531] "t" "a" "t" "a" "a" "c" "a" "t" "g" "a" "t" "g" "a" "t" "c" "t" "c" "a"
## [19549] "g" "c" "t" "g" "g" "c" "t" "t" "t" "a" "g" "c" "t" "t" "g" "t" "g" "g"
## [19567] "g" "t" "t" "t" "a" "c" "a" "a" "a" "c" "a" "a" "t" "t" "t" "g" "a" "t"
## [19585] "a" "c" "t" "t" "a" "t" "a" "a" "c" "c" "t" "c" "t" "g" "g" "a" "a" "c"
## [19603] "a" "c" "t" "t" "t" "t" "a" "c" "a" "a" "g" "a" "c" "t" "t" "c" "a" "g"
## [19621] "a" "g" "t" "t" "t" "a" "g" "a" "a" "a" "a" "t" "g" "t" "g" "g" "c" "t"
## [19639] "t" "t" "t" "a" "a" "t" "g" "t" "t" "g" "t" "a" "a" "a" "t" "a" "a" "g"
## [19657] "g" "g" "a" "c" "a" "c" "t" "t" "t" "g" "a" "t" "g" "g" "a" "c" "a" "a"
## [19675] "c" "a" "g" "g" "g" "t" "g" "a" "a" "g" "t" "a" "c" "c" "a" "g" "t" "t"
## [19693] "t" "c" "t" "a" "t" "c" "a" "t" "t" "a" "a" "t" "a" "a" "c" "a" "c" "t"
## [19711] "g" "t" "t" "t" "a" "c" "a" "c" "a" "a" "a" "a" "g" "t" "t" "g" "a" "t"
## [19729] "g" "g" "t" "g" "t" "t" "g" "a" "t" "g" "t" "a" "g" "a" "a" "t" "t" "g"
## [19747] "t" "t" "t" "g" "a" "a" "a" "a" "t" "a" "a" "a" "a" "c" "a" "a" "c" "a"
## [19765] "t" "t" "a" "c" "c" "t" "g" "t" "t" "a" "a" "t" "g" "t" "a" "g" "c" "a"
## [19783] "t" "t" "t" "g" "a" "g" "c" "t" "t" "t" "g" "g" "g" "c" "t" "a" "a" "g"
## [19801] "c" "g" "c" "a" "a" "c" "a" "t" "t" "a" "a" "a" "c" "c" "a" "g" "t" "a"
## [19819] "c" "c" "a" "g" "a" "g" "g" "t" "g" "a" "a" "a" "a" "t" "a" "c" "t" "c"
## [19837] "a" "a" "t" "a" "a" "t" "t" "t" "g" "g" "g" "t" "g" "t" "g" "g" "a" "c"
## [19855] "a" "t" "t" "g" "c" "t" "g" "c" "t" "a" "a" "t" "a" "c" "t" "g" "t" "g"
## [19873] "a" "t" "c" "t" "g" "g" "g" "a" "c" "t" "a" "c" "a" "a" "a" "a" "g" "a"
## [19891] "g" "a" "t" "g" "c" "t" "c" "c" "a" "g" "c" "a" "c" "a" "t" "a" "t" "a"
## [19909] "t" "c" "t" "a" "c" "t" "a" "t" "t" "g" "g" "t" "g" "t" "t" "t" "g" "t"
## [19927] "t" "c" "t" "a" "t" "g" "a" "c" "t" "g" "a" "c" "a" "t" "a" "g" "c" "c"
## [19945] "a" "a" "g" "a" "a" "a" "c" "c" "a" "a" "c" "t" "g" "a" "a" "a" "c" "g"
## [19963] "a" "t" "t" "t" "g" "t" "g" "c" "a" "c" "c" "a" "c" "t" "c" "a" "c" "t"
## [19981] "g" "t" "c" "t" "t" "t" "t" "t" "t" "g" "a" "t" "g" "g" "t" "a" "g" "a"
## [19999] "g" "t" "t" "g" "a" "t" "g" "g" "t" "c" "a" "a" "g" "t" "a" "g" "a" "c"
## [20017] "t" "t" "a" "t" "t" "t" "a" "g" "a" "a" "a" "t" "g" "c" "c" "c" "g" "t"
## [20035] "a" "a" "t" "g" "g" "t" "g" "t" "t" "c" "t" "t" "a" "t" "t" "a" "c" "a"
## [20053] "g" "a" "a" "g" "g" "t" "a" "g" "t" "g" "t" "t" "a" "a" "a" "g" "g" "t"
## [20071] "t" "t" "a" "c" "a" "a" "c" "c" "a" "t" "c" "t" "g" "t" "a" "g" "g" "t"
## [20089] "c" "c" "c" "a" "a" "a" "c" "a" "a" "g" "c" "t" "a" "g" "t" "c" "t" "t"
## [20107] "a" "a" "t" "g" "g" "a" "g" "t" "c" "a" "c" "a" "t" "t" "a" "a" "t" "t"
## [20125] "g" "g" "a" "g" "a" "a" "g" "c" "c" "g" "t" "a" "a" "a" "a" "a" "c" "a"
## [20143] "c" "a" "g" "t" "t" "c" "a" "a" "t" "t" "a" "t" "t" "a" "t" "a" "a" "g"
## [20161] "a" "a" "a" "g" "t" "t" "g" "a" "t" "g" "g" "t" "g" "t" "t" "g" "t" "c"
## [20179] "c" "a" "a" "c" "a" "a" "t" "t" "a" "c" "c" "t" "g" "a" "a" "a" "c" "t"
## [20197] "t" "a" "c" "t" "t" "t" "a" "c" "t" "c" "a" "g" "a" "g" "t" "a" "g" "a"
## [20215] "a" "a" "t" "t" "t" "a" "c" "a" "a" "g" "a" "a" "t" "t" "t" "a" "a" "a"
## [20233] "c" "c" "c" "a" "g" "g" "a" "g" "t" "c" "a" "a" "a" "t" "g" "g" "a" "a"
## [20251] "a" "t" "t" "g" "a" "t" "t" "t" "c" "t" "t" "a" "g" "a" "a" "t" "t" "a"
## [20269] "g" "c" "t" "a" "t" "g" "g" "a" "t" "g" "a" "a" "t" "t" "c" "a" "t" "t"
## [20287] "g" "a" "a" "c" "g" "g" "t" "a" "t" "a" "a" "a" "t" "t" "a" "g" "a" "a"
## [20305] "g" "g" "c" "t" "a" "t" "g" "c" "c" "t" "t" "c" "g" "a" "a" "c" "a" "t"
## [20323] "a" "t" "c" "g" "t" "t" "t" "a" "t" "g" "g" "a" "g" "a" "t" "t" "t" "t"
## [20341] "a" "g" "t" "c" "a" "t" "a" "g" "t" "c" "a" "g" "t" "t" "a" "g" "g" "t"
## [20359] "g" "g" "t" "t" "t" "a" "c" "a" "t" "c" "t" "a" "c" "t" "g" "a" "t" "t"
## [20377] "g" "g" "a" "c" "t" "a" "g" "c" "t" "a" "a" "a" "c" "g" "t" "t" "t" "t"
## [20395] "a" "a" "g" "g" "a" "a" "t" "c" "a" "c" "c" "t" "t" "t" "t" "g" "a" "a"
## [20413] "t" "t" "a" "g" "a" "a" "g" "a" "t" "t" "t" "t" "a" "t" "t" "c" "c" "t"
## [20431] "a" "t" "g" "g" "a" "c" "a" "g" "t" "a" "c" "a" "g" "t" "t" "a" "a" "a"
## [20449] "a" "a" "c" "t" "a" "t" "t" "t" "c" "a" "t" "a" "a" "c" "a" "g" "a" "t"
## [20467] "g" "c" "g" "c" "a" "a" "a" "c" "a" "g" "g" "t" "t" "c" "a" "t" "c" "t"
## [20485] "a" "a" "g" "t" "g" "t" "g" "t" "g" "t" "g" "t" "t" "c" "t" "g" "t" "t"
## [20503] "a" "t" "t" "g" "a" "t" "t" "t" "a" "t" "t" "a" "c" "t" "t" "g" "a" "t"
## [20521] "g" "a" "t" "t" "t" "t" "g" "t" "t" "g" "a" "a" "a" "t" "a" "a" "t" "a"
## [20539] "a" "a" "a" "t" "c" "c" "c" "a" "a" "g" "a" "t" "t" "t" "a" "t" "c" "t"
## [20557] "g" "t" "a" "g" "t" "t" "t" "c" "t" "a" "a" "g" "g" "t" "t" "g" "t" "c"
## [20575] "a" "a" "a" "g" "t" "g" "a" "c" "t" "a" "t" "t" "g" "a" "c" "t" "a" "t"
## [20593] "a" "c" "a" "g" "a" "a" "a" "t" "t" "t" "c" "a" "t" "t" "t" "a" "t" "g"
## [20611] "c" "t" "t" "t" "g" "g" "t" "g" "t" "a" "a" "a" "g" "a" "t" "g" "g" "c"
## [20629] "c" "a" "t" "g" "t" "a" "g" "a" "a" "a" "c" "a" "t" "t" "t" "t" "a" "c"
## [20647] "c" "c" "a" "a" "a" "a" "t" "t" "a" "c" "a" "a" "t" "c" "t" "a" "g" "t"
## [20665] "c" "a" "a" "g" "c" "g" "t" "g" "g" "c" "a" "a" "c" "c" "g" "g" "g" "t"
## [20683] "g" "t" "t" "g" "c" "t" "a" "t" "g" "c" "c" "t" "a" "a" "t" "c" "t" "t"
## [20701] "t" "a" "c" "a" "a" "a" "a" "t" "g" "c" "a" "a" "a" "g" "a" "a" "t" "g"
## [20719] "c" "t" "a" "t" "t" "a" "g" "a" "a" "a" "a" "g" "t" "g" "t" "g" "a" "c"
## [20737] "c" "t" "t" "c" "a" "a" "a" "a" "t" "t" "a" "t" "g" "g" "t" "g" "a" "t"
## [20755] "a" "g" "t" "g" "c" "a" "a" "c" "a" "t" "t" "a" "c" "c" "t" "a" "a" "a"
## [20773] "g" "g" "c" "a" "t" "a" "a" "t" "g" "a" "t" "g" "a" "a" "t" "g" "t" "c"
## [20791] "g" "c" "a" "a" "a" "a" "t" "a" "t" "a" "c" "t" "c" "a" "a" "c" "t" "g"
## [20809] "t" "g" "t" "c" "a" "a" "t" "a" "t" "t" "t" "a" "a" "a" "c" "a" "c" "a"
## [20827] "t" "t" "a" "a" "c" "a" "t" "t" "a" "g" "c" "t" "g" "t" "a" "c" "c" "c"
## [20845] "t" "a" "t" "a" "a" "t" "a" "t" "g" "a" "g" "a" "g" "t" "t" "a" "t" "a"
## [20863] "c" "a" "t" "t" "t" "t" "g" "g" "t" "g" "c" "t" "g" "g" "t" "t" "c" "t"
## [20881] "g" "a" "t" "a" "a" "a" "g" "g" "a" "g" "t" "t" "g" "c" "a" "c" "c" "a"
## [20899] "g" "g" "t" "a" "c" "a" "g" "c" "t" "g" "t" "t" "t" "t" "a" "a" "g" "a"
## [20917] "c" "a" "g" "t" "g" "g" "t" "t" "g" "c" "c" "t" "a" "c" "g" "g" "g" "t"
## [20935] "a" "c" "g" "c" "t" "g" "c" "t" "t" "g" "t" "c" "g" "a" "t" "t" "c" "a"
## [20953] "g" "a" "t" "c" "t" "t" "a" "a" "t" "g" "a" "c" "t" "t" "t" "g" "t" "c"
## [20971] "t" "c" "t" "g" "a" "t" "g" "c" "a" "g" "a" "t" "t" "c" "a" "a" "c" "t"
## [20989] "t" "t" "g" "a" "t" "t" "g" "g" "t" "g" "a" "t" "t" "g" "t" "g" "c" "a"
## [21007] "a" "c" "t" "g" "t" "a" "c" "a" "t" "a" "c" "a" "g" "c" "t" "a" "a" "t"
## [21025] "a" "a" "a" "t" "g" "g" "g" "a" "t" "c" "t" "c" "a" "t" "t" "a" "t" "t"
## [21043] "a" "g" "t" "g" "a" "t" "a" "t" "g" "t" "a" "c" "g" "a" "c" "c" "c" "t"
## [21061] "a" "a" "g" "a" "c" "t" "a" "a" "a" "a" "a" "t" "g" "t" "t" "a" "c" "a"
## [21079] "a" "a" "a" "g" "a" "a" "a" "a" "t" "g" "a" "c" "t" "c" "t" "a" "a" "a"
## [21097] "g" "a" "g" "g" "g" "t" "t" "t" "t" "t" "t" "c" "a" "c" "t" "t" "a" "c"
## [21115] "a" "t" "t" "t" "g" "t" "g" "g" "g" "t" "t" "t" "a" "t" "a" "c" "a" "a"
## [21133] "c" "a" "a" "a" "a" "g" "c" "t" "a" "g" "c" "t" "c" "t" "t" "g" "g" "a"
## [21151] "g" "g" "t" "t" "c" "c" "g" "t" "g" "g" "c" "t" "a" "t" "a" "a" "a" "g"
## [21169] "a" "t" "a" "a" "c" "a" "g" "a" "a" "c" "a" "t" "t" "c" "t" "t" "g" "g"
## [21187] "a" "a" "t" "g" "c" "t" "g" "a" "t" "c" "t" "t" "t" "a" "t" "a" "a" "g"
## [21205] "c" "t" "c" "a" "t" "g" "g" "g" "a" "c" "a" "c" "t" "t" "c" "g" "c" "a"
## [21223] "t" "g" "g" "t" "g" "g" "a" "c" "a" "g" "c" "c" "t" "t" "t" "g" "t" "t"
## [21241] "a" "c" "t" "a" "a" "t" "g" "t" "g" "a" "a" "t" "g" "c" "g" "t" "c" "a"
## [21259] "t" "c" "a" "t" "c" "t" "g" "a" "a" "g" "c" "a" "t" "t" "t" "t" "t" "a"
## [21277] "a" "t" "t" "g" "g" "a" "t" "g" "t" "a" "a" "t" "t" "a" "t" "c" "t" "t"
## [21295] "g" "g" "c" "a" "a" "a" "c" "c" "a" "c" "g" "c" "g" "a" "a" "c" "a" "a"
## [21313] "a" "t" "a" "g" "a" "t" "g" "g" "t" "t" "a" "t" "g" "t" "c" "a" "t" "g"
## [21331] "c" "a" "t" "g" "c" "a" "a" "a" "t" "t" "a" "c" "a" "t" "a" "t" "t" "t"
## [21349] "t" "g" "g" "a" "g" "g" "a" "a" "t" "a" "c" "a" "a" "a" "t" "c" "c" "a"
## [21367] "a" "t" "t" "c" "a" "g" "t" "t" "g" "t" "c" "t" "t" "c" "c" "t" "a" "t"
## [21385] "t" "c" "t" "t" "t" "a" "t" "t" "t" "g" "a" "c" "a" "t" "g" "a" "g" "t"
## [21403] "a" "a" "a" "t" "t" "t" "c" "c" "c" "c" "t" "t" "a" "a" "a" "t" "t" "a"
## [21421] "a" "g" "g" "g" "g" "t" "a" "c" "t" "g" "c" "t" "g" "t" "t" "a" "t" "g"
## [21439] "t" "c" "t" "t" "t" "a" "a" "a" "a" "g" "a" "a" "g" "g" "t" "c" "a" "a"
## [21457] "a" "t" "c" "a" "a" "t" "g" "a" "t" "a" "t" "g" "a" "t" "t" "t" "t" "a"
## [21475] "t" "c" "t" "c" "t" "t" "c" "t" "t" "a" "g" "t" "a" "a" "a" "g" "g" "t"
## [21493] "a" "g" "a" "c" "t" "t" "a" "t" "a" "a" "t" "t" "a" "g" "a" "g" "a" "a"
## [21511] "a" "a" "c" "a" "a" "c" "a" "g" "a" "g" "t" "t" "g" "t" "t" "a" "t" "t"
## [21529] "t" "c" "t" "a" "g" "t" "g" "a" "t" "g" "t" "t" "c" "t" "t" "g" "t" "t"
## [21547] "a" "a" "c" "a" "a" "c" "t" "a" "a" "a" "c" "g" "a" "a" "c" "a" "a" "t"
## [21565] "g" "t" "t" "t" "g" "t" "t" "t" "t" "t" "c" "t" "t" "g" "t" "t" "t" "t"
## [21583] "a" "t" "t" "g" "c" "c" "a" "c" "t" "a" "g" "t" "c" "t" "c" "t" "a" "g"
## [21601] "t" "c" "a" "g" "t" "g" "t" "g" "t" "t" "a" "a" "t" "c" "t" "t" "a" "c"
## [21619] "a" "a" "c" "c" "a" "g" "a" "a" "c" "t" "c" "a" "a" "t" "t" "a" "c" "c"
## [21637] "c" "c" "c" "t" "g" "c" "a" "t" "a" "c" "a" "c" "t" "a" "a" "t" "t" "c"
## [21655] "t" "t" "t" "c" "a" "c" "a" "c" "g" "t" "g" "g" "t" "g" "t" "t" "t" "a"
## [21673] "t" "t" "a" "c" "c" "c" "t" "g" "a" "c" "a" "a" "a" "g" "t" "t" "t" "t"
## [21691] "c" "a" "g" "a" "t" "c" "c" "t" "c" "a" "g" "t" "t" "t" "t" "a" "c" "a"
## [21709] "t" "t" "c" "a" "a" "c" "t" "c" "a" "g" "g" "a" "c" "t" "t" "g" "t" "t"
## [21727] "c" "t" "t" "a" "c" "c" "t" "t" "t" "c" "t" "t" "t" "t" "c" "c" "a" "a"
## [21745] "t" "g" "t" "t" "a" "c" "t" "t" "g" "g" "t" "t" "c" "c" "a" "t" "g" "c"
## [21763] "t" "a" "t" "a" "c" "a" "t" "g" "t" "c" "t" "c" "t" "g" "g" "g" "a" "c"
## [21781] "c" "a" "a" "t" "g" "g" "t" "a" "c" "t" "a" "a" "g" "a" "g" "g" "t" "t"
## [21799] "t" "g" "a" "t" "a" "a" "c" "c" "c" "t" "g" "t" "c" "c" "t" "a" "c" "c"
## [21817] "a" "t" "t" "t" "a" "a" "t" "g" "a" "t" "g" "g" "t" "g" "t" "t" "t" "a"
## [21835] "t" "t" "t" "t" "g" "c" "t" "t" "c" "c" "a" "c" "t" "g" "a" "g" "a" "a"
## [21853] "g" "t" "c" "t" "a" "a" "c" "a" "t" "a" "a" "t" "a" "a" "g" "a" "g" "g"
## [21871] "c" "t" "g" "g" "a" "t" "t" "t" "t" "t" "g" "g" "t" "a" "c" "t" "a" "c"
## [21889] "t" "t" "t" "a" "g" "a" "t" "t" "c" "g" "a" "a" "g" "a" "c" "c" "c" "a"
## [21907] "g" "t" "c" "c" "c" "t" "a" "c" "t" "t" "a" "t" "t" "g" "t" "t" "a" "a"
## [21925] "t" "a" "a" "c" "g" "c" "t" "a" "c" "t" "a" "a" "t" "g" "t" "t" "g" "t"
## [21943] "t" "a" "t" "t" "a" "a" "a" "g" "t" "c" "t" "g" "t" "g" "a" "a" "t" "t"
## [21961] "t" "c" "a" "a" "t" "t" "t" "t" "g" "t" "a" "a" "t" "g" "a" "t" "c" "c"
## [21979] "a" "t" "t" "t" "t" "t" "g" "g" "g" "t" "g" "t" "t" "t" "a" "t" "t" "a"
## [21997] "c" "c" "a" "c" "a" "a" "a" "a" "a" "c" "a" "a" "c" "a" "a" "a" "a" "g"
## [22015] "t" "t" "g" "g" "a" "t" "g" "g" "a" "a" "a" "g" "t" "g" "a" "g" "t" "t"
## [22033] "c" "a" "g" "a" "g" "t" "t" "t" "a" "t" "t" "c" "t" "a" "g" "t" "g" "c"
## [22051] "g" "a" "a" "t" "a" "a" "t" "t" "g" "c" "a" "c" "t" "t" "t" "t" "g" "a"
## [22069] "a" "t" "a" "t" "g" "t" "c" "t" "c" "t" "c" "a" "g" "c" "c" "t" "t" "t"
## [22087] "t" "c" "t" "t" "a" "t" "g" "g" "a" "c" "c" "t" "t" "g" "a" "a" "g" "g"
## [22105] "a" "a" "a" "a" "c" "a" "g" "g" "g" "t" "a" "a" "t" "t" "t" "c" "a" "a"
## [22123] "a" "a" "a" "t" "c" "t" "t" "a" "g" "g" "g" "a" "a" "t" "t" "t" "g" "t"
## [22141] "g" "t" "t" "t" "a" "a" "g" "a" "a" "t" "a" "t" "t" "g" "a" "t" "g" "g"
## [22159] "t" "t" "a" "t" "t" "t" "t" "a" "a" "a" "a" "t" "a" "t" "a" "t" "t" "c"
## [22177] "t" "a" "a" "g" "c" "a" "c" "a" "c" "g" "c" "c" "t" "a" "t" "t" "a" "a"
## [22195] "t" "t" "t" "a" "g" "t" "g" "c" "g" "t" "g" "a" "t" "c" "t" "c" "c" "c"
## [22213] "t" "c" "a" "g" "g" "g" "t" "t" "t" "t" "t" "c" "g" "g" "c" "t" "t" "t"
## [22231] "a" "g" "a" "a" "c" "c" "a" "t" "t" "g" "g" "t" "a" "g" "a" "t" "t" "t"
## [22249] "g" "c" "c" "a" "a" "t" "a" "g" "g" "t" "a" "t" "t" "a" "a" "c" "a" "t"
## [22267] "c" "a" "c" "t" "a" "g" "g" "t" "t" "t" "c" "a" "a" "a" "c" "t" "t" "t"
## [22285] "a" "c" "t" "t" "g" "c" "t" "t" "t" "a" "c" "a" "t" "a" "g" "a" "a" "g"
## [22303] "t" "t" "a" "t" "t" "t" "g" "a" "c" "t" "c" "c" "t" "g" "g" "t" "g" "a"
## [22321] "t" "t" "c" "t" "t" "c" "t" "t" "c" "a" "g" "g" "t" "t" "g" "g" "a" "c"
## [22339] "a" "g" "c" "t" "g" "g" "t" "g" "c" "t" "g" "c" "a" "g" "c" "t" "t" "a"
## [22357] "t" "t" "a" "t" "g" "t" "g" "g" "g" "t" "t" "a" "t" "c" "t" "t" "c" "a"
## [22375] "a" "c" "c" "t" "a" "g" "g" "a" "c" "t" "t" "t" "t" "c" "t" "a" "t" "t"
## [22393] "a" "a" "a" "a" "t" "a" "t" "a" "a" "t" "g" "a" "a" "a" "a" "t" "g" "g"
## [22411] "a" "a" "c" "c" "a" "t" "t" "a" "c" "a" "g" "a" "t" "g" "c" "t" "g" "t"
## [22429] "a" "g" "a" "c" "t" "g" "t" "g" "c" "a" "c" "t" "t" "g" "a" "c" "c" "c"
## [22447] "t" "c" "t" "c" "t" "c" "a" "g" "a" "a" "a" "c" "a" "a" "a" "g" "t" "g"
## [22465] "t" "a" "c" "g" "t" "t" "g" "a" "a" "a" "t" "c" "c" "t" "t" "c" "a" "c"
## [22483] "t" "g" "t" "a" "g" "a" "a" "a" "a" "a" "g" "g" "a" "a" "t" "c" "t" "a"
## [22501] "t" "c" "a" "a" "a" "c" "t" "t" "c" "t" "a" "a" "c" "t" "t" "t" "a" "g"
## [22519] "a" "g" "t" "c" "c" "a" "a" "c" "c" "a" "a" "c" "a" "g" "a" "a" "t" "c"
## [22537] "t" "a" "t" "t" "g" "t" "t" "a" "g" "a" "t" "t" "t" "c" "c" "t" "a" "a"
## [22555] "t" "a" "t" "t" "a" "c" "a" "a" "a" "c" "t" "t" "g" "t" "g" "c" "c" "c"
## [22573] "t" "t" "t" "t" "g" "g" "t" "g" "a" "a" "g" "t" "t" "t" "t" "t" "a" "a"
## [22591] "c" "g" "c" "c" "a" "c" "c" "a" "g" "a" "t" "t" "t" "g" "c" "a" "t" "c"
## [22609] "t" "g" "t" "t" "t" "a" "t" "g" "c" "t" "t" "g" "g" "a" "a" "c" "a" "g"
## [22627] "g" "a" "a" "g" "a" "g" "a" "a" "t" "c" "a" "g" "c" "a" "a" "c" "t" "g"
## [22645] "t" "g" "t" "t" "g" "c" "t" "g" "a" "t" "t" "a" "t" "t" "c" "t" "g" "t"
## [22663] "c" "c" "t" "a" "t" "a" "t" "a" "a" "t" "t" "c" "c" "g" "c" "a" "t" "c"
## [22681] "a" "t" "t" "t" "t" "c" "c" "a" "c" "t" "t" "t" "t" "a" "a" "g" "t" "g"
## [22699] "t" "t" "a" "t" "g" "g" "a" "g" "t" "g" "t" "c" "t" "c" "c" "t" "a" "c"
## [22717] "t" "a" "a" "a" "t" "t" "a" "a" "a" "t" "g" "a" "t" "c" "t" "c" "t" "g"
## [22735] "c" "t" "t" "t" "a" "c" "t" "a" "a" "t" "g" "t" "c" "t" "a" "t" "g" "c"
## [22753] "a" "g" "a" "t" "t" "c" "a" "t" "t" "t" "g" "t" "a" "a" "t" "t" "a" "g"
## [22771] "a" "g" "g" "t" "g" "a" "t" "g" "a" "a" "g" "t" "c" "a" "g" "a" "c" "a"
## [22789] "a" "a" "t" "c" "g" "c" "t" "c" "c" "a" "g" "g" "g" "c" "a" "a" "a" "c"
## [22807] "t" "g" "g" "a" "a" "a" "g" "a" "t" "t" "g" "c" "t" "g" "a" "t" "t" "a"
## [22825] "t" "a" "a" "t" "t" "a" "t" "a" "a" "a" "t" "t" "a" "c" "c" "a" "g" "a"
## [22843] "t" "g" "a" "t" "t" "t" "t" "a" "c" "a" "g" "g" "c" "t" "g" "c" "g" "t"
## [22861] "t" "a" "t" "a" "g" "c" "t" "t" "g" "g" "a" "a" "t" "t" "c" "t" "a" "a"
## [22879] "c" "a" "a" "t" "c" "t" "t" "g" "a" "t" "t" "c" "t" "a" "a" "g" "g" "t"
## [22897] "t" "g" "g" "t" "g" "g" "t" "a" "a" "t" "t" "a" "t" "a" "a" "t" "t" "a"
## [22915] "c" "c" "t" "g" "t" "a" "t" "a" "g" "a" "t" "t" "g" "t" "t" "t" "a" "g"
## [22933] "g" "a" "a" "g" "t" "c" "t" "a" "a" "t" "c" "t" "c" "a" "a" "a" "c" "c"
## [22951] "t" "t" "t" "t" "g" "a" "g" "a" "g" "a" "g" "a" "t" "a" "t" "t" "t" "c"
## [22969] "a" "a" "c" "t" "g" "a" "a" "a" "t" "c" "t" "a" "t" "c" "a" "g" "g" "c"
## [22987] "c" "g" "g" "t" "a" "g" "c" "a" "c" "a" "c" "c" "t" "t" "g" "t" "a" "a"
## [23005] "t" "g" "g" "t" "g" "t" "t" "g" "a" "a" "g" "g" "t" "t" "t" "t" "a" "a"
## [23023] "t" "t" "g" "t" "t" "a" "c" "t" "t" "t" "c" "c" "t" "t" "t" "a" "c" "a"
## [23041] "a" "t" "c" "a" "t" "a" "t" "g" "g" "t" "t" "t" "c" "c" "a" "a" "c" "c"
## [23059] "c" "a" "c" "t" "a" "a" "t" "g" "g" "t" "g" "t" "t" "g" "g" "t" "t" "a"
## [23077] "c" "c" "a" "a" "c" "c" "a" "t" "a" "c" "a" "g" "a" "g" "t" "a" "g" "t"
## [23095] "a" "g" "t" "a" "c" "t" "t" "t" "c" "t" "t" "t" "t" "g" "a" "a" "c" "t"
## [23113] "t" "c" "t" "a" "c" "a" "t" "g" "c" "a" "c" "c" "a" "g" "c" "a" "a" "c"
## [23131] "t" "g" "t" "t" "t" "g" "t" "g" "g" "a" "c" "c" "t" "a" "a" "a" "a" "a"
## [23149] "g" "t" "c" "t" "a" "c" "t" "a" "a" "t" "t" "t" "g" "g" "t" "t" "a" "a"
## [23167] "a" "a" "a" "c" "a" "a" "a" "t" "g" "t" "g" "t" "c" "a" "a" "t" "t" "t"
## [23185] "c" "a" "a" "c" "t" "t" "c" "a" "a" "t" "g" "g" "t" "t" "t" "a" "a" "c"
## [23203] "a" "g" "g" "c" "a" "c" "a" "g" "g" "t" "g" "t" "t" "c" "t" "t" "a" "c"
## [23221] "t" "g" "a" "g" "t" "c" "t" "a" "a" "c" "a" "a" "a" "a" "a" "g" "t" "t"
## [23239] "t" "c" "t" "g" "c" "c" "t" "t" "t" "c" "c" "a" "a" "c" "a" "a" "t" "t"
## [23257] "t" "g" "g" "c" "a" "g" "a" "g" "a" "c" "a" "t" "t" "g" "c" "t" "g" "a"
## [23275] "c" "a" "c" "t" "a" "c" "t" "g" "a" "t" "g" "c" "t" "g" "t" "c" "c" "g"
## [23293] "t" "g" "a" "t" "c" "c" "a" "c" "a" "g" "a" "c" "a" "c" "t" "t" "g" "a"
## [23311] "g" "a" "t" "t" "c" "t" "t" "g" "a" "c" "a" "t" "t" "a" "c" "a" "c" "c"
## [23329] "a" "t" "g" "t" "t" "c" "t" "t" "t" "t" "g" "g" "t" "g" "g" "t" "g" "t"
## [23347] "c" "a" "g" "t" "g" "t" "t" "a" "t" "a" "a" "c" "a" "c" "c" "a" "g" "g"
## [23365] "a" "a" "c" "a" "a" "a" "t" "a" "c" "t" "t" "c" "t" "a" "a" "c" "c" "a"
## [23383] "g" "g" "t" "t" "g" "c" "t" "g" "t" "t" "c" "t" "t" "t" "a" "t" "c" "a"
## [23401] "g" "g" "a" "t" "g" "t" "t" "a" "a" "c" "t" "g" "c" "a" "c" "a" "g" "a"
## [23419] "a" "g" "t" "c" "c" "c" "t" "g" "t" "t" "g" "c" "t" "a" "t" "t" "c" "a"
## [23437] "t" "g" "c" "a" "g" "a" "t" "c" "a" "a" "c" "t" "t" "a" "c" "t" "c" "c"
## [23455] "t" "a" "c" "t" "t" "g" "g" "c" "g" "t" "g" "t" "t" "t" "a" "t" "t" "c"
## [23473] "t" "a" "c" "a" "g" "g" "t" "t" "c" "t" "a" "a" "t" "g" "t" "t" "t" "t"
## [23491] "t" "c" "a" "a" "a" "c" "a" "c" "g" "t" "g" "c" "a" "g" "g" "c" "t" "g"
## [23509] "t" "t" "t" "a" "a" "t" "a" "g" "g" "g" "g" "c" "t" "g" "a" "a" "c" "a"
## [23527] "t" "g" "t" "c" "a" "a" "c" "a" "a" "c" "t" "c" "a" "t" "a" "t" "g" "a"
## [23545] "g" "t" "g" "t" "g" "a" "c" "a" "t" "a" "c" "c" "c" "a" "t" "t" "g" "g"
## [23563] "t" "g" "c" "a" "g" "g" "t" "a" "t" "a" "t" "g" "c" "g" "c" "t" "a" "g"
## [23581] "t" "t" "a" "t" "c" "a" "g" "a" "c" "t" "c" "a" "g" "a" "c" "t" "a" "a"
## [23599] "t" "t" "c" "t" "c" "c" "t" "c" "g" "g" "c" "g" "g" "g" "c" "a" "c" "g"
## [23617] "t" "a" "g" "t" "g" "t" "a" "g" "c" "t" "a" "g" "t" "c" "a" "a" "t" "c"
## [23635] "c" "a" "t" "c" "a" "t" "t" "g" "c" "c" "t" "a" "c" "a" "c" "t" "a" "t"
## [23653] "g" "t" "c" "a" "c" "t" "t" "g" "g" "t" "g" "c" "a" "g" "a" "a" "a" "a"
## [23671] "t" "t" "c" "a" "g" "t" "t" "g" "c" "t" "t" "a" "c" "t" "c" "t" "a" "a"
## [23689] "t" "a" "a" "c" "t" "c" "t" "a" "t" "t" "g" "c" "c" "a" "t" "a" "c" "c"
## [23707] "c" "a" "c" "a" "a" "a" "t" "t" "t" "t" "a" "c" "t" "a" "t" "t" "a" "g"
## [23725] "t" "g" "t" "t" "a" "c" "c" "a" "c" "a" "g" "a" "a" "a" "t" "t" "c" "t"
## [23743] "a" "c" "c" "a" "g" "t" "g" "t" "c" "t" "a" "t" "g" "a" "c" "c" "a" "a"
## [23761] "g" "a" "c" "a" "t" "c" "a" "g" "t" "a" "g" "a" "t" "t" "g" "t" "a" "c"
## [23779] "a" "a" "t" "g" "t" "a" "c" "a" "t" "t" "t" "g" "t" "g" "g" "t" "g" "a"
## [23797] "t" "t" "c" "a" "a" "c" "t" "g" "a" "a" "t" "g" "c" "a" "g" "c" "a" "a"
## [23815] "t" "c" "t" "t" "t" "t" "g" "t" "t" "g" "c" "a" "a" "t" "a" "t" "g" "g"
## [23833] "c" "a" "g" "t" "t" "t" "t" "t" "g" "t" "a" "c" "a" "c" "a" "a" "t" "t"
## [23851] "a" "a" "a" "c" "c" "g" "t" "g" "c" "t" "t" "t" "a" "a" "c" "t" "g" "g"
## [23869] "a" "a" "t" "a" "g" "c" "t" "g" "t" "t" "g" "a" "a" "c" "a" "a" "g" "a"
## [23887] "c" "a" "a" "a" "a" "a" "c" "a" "c" "c" "c" "a" "a" "g" "a" "a" "g" "t"
## [23905] "t" "t" "t" "t" "g" "c" "a" "c" "a" "a" "g" "t" "c" "a" "a" "a" "c" "a"
## [23923] "a" "a" "t" "t" "t" "a" "c" "a" "a" "a" "a" "c" "a" "c" "c" "a" "c" "c"
## [23941] "a" "a" "t" "t" "a" "a" "a" "g" "a" "t" "t" "t" "t" "g" "g" "t" "g" "g"
## [23959] "t" "t" "t" "t" "a" "a" "t" "t" "t" "t" "t" "c" "a" "c" "a" "a" "a" "t"
## [23977] "a" "t" "t" "a" "c" "c" "a" "g" "a" "t" "c" "c" "a" "t" "c" "a" "a" "a"
## [23995] "a" "c" "c" "a" "a" "g" "c" "a" "a" "g" "a" "g" "g" "t" "c" "a" "t" "t"
## [24013] "t" "a" "t" "t" "g" "a" "a" "g" "a" "t" "c" "t" "a" "c" "t" "t" "t" "t"
## [24031] "c" "a" "a" "c" "a" "a" "a" "g" "t" "g" "a" "c" "a" "c" "t" "t" "g" "c"
## [24049] "a" "g" "a" "t" "g" "c" "t" "g" "g" "c" "t" "t" "c" "a" "t" "c" "a" "a"
## [24067] "a" "c" "a" "a" "t" "a" "t" "g" "g" "t" "g" "a" "t" "t" "g" "c" "c" "t"
## [24085] "t" "g" "g" "t" "g" "a" "t" "a" "t" "t" "g" "c" "t" "g" "c" "t" "a" "g"
## [24103] "a" "g" "a" "c" "c" "t" "c" "a" "t" "t" "t" "g" "t" "g" "c" "a" "c" "a"
## [24121] "a" "a" "a" "g" "t" "t" "t" "a" "a" "c" "g" "g" "c" "c" "t" "t" "a" "c"
## [24139] "t" "g" "t" "t" "t" "t" "g" "c" "c" "a" "c" "c" "t" "t" "t" "g" "c" "t"
## [24157] "c" "a" "c" "a" "g" "a" "t" "g" "a" "a" "a" "t" "g" "a" "t" "t" "g" "c"
## [24175] "t" "c" "a" "a" "t" "a" "c" "a" "c" "t" "t" "c" "t" "g" "c" "a" "c" "t"
## [24193] "g" "t" "t" "a" "g" "c" "g" "g" "g" "t" "a" "c" "a" "a" "t" "c" "a" "c"
## [24211] "t" "t" "c" "t" "g" "g" "t" "t" "g" "g" "a" "c" "c" "t" "t" "t" "g" "g"
## [24229] "t" "g" "c" "a" "g" "g" "t" "g" "c" "t" "g" "c" "a" "t" "t" "a" "c" "a"
## [24247] "a" "a" "t" "a" "c" "c" "a" "t" "t" "t" "g" "c" "t" "a" "t" "g" "c" "a"
## [24265] "a" "a" "t" "g" "g" "c" "t" "t" "a" "t" "a" "g" "g" "t" "t" "t" "a" "a"
## [24283] "t" "g" "g" "t" "a" "t" "t" "g" "g" "a" "g" "t" "t" "a" "c" "a" "c" "a"
## [24301] "g" "a" "a" "t" "g" "t" "t" "c" "t" "c" "t" "a" "t" "g" "a" "g" "a" "a"
## [24319] "c" "c" "a" "a" "a" "a" "a" "t" "t" "g" "a" "t" "t" "g" "c" "c" "a" "a"
## [24337] "c" "c" "a" "a" "t" "t" "t" "a" "a" "t" "a" "g" "t" "g" "c" "t" "a" "t"
## [24355] "t" "g" "g" "c" "a" "a" "a" "a" "t" "t" "c" "a" "a" "g" "a" "c" "t" "c"
## [24373] "a" "c" "t" "t" "t" "c" "t" "t" "c" "c" "a" "c" "a" "g" "c" "a" "a" "g"
## [24391] "t" "g" "c" "a" "c" "t" "t" "g" "g" "a" "a" "a" "a" "c" "t" "t" "c" "a"
## [24409] "a" "g" "a" "t" "g" "t" "g" "g" "t" "c" "a" "a" "c" "c" "a" "a" "a" "a"
## [24427] "t" "g" "c" "a" "c" "a" "a" "g" "c" "t" "t" "t" "a" "a" "a" "c" "a" "c"
## [24445] "g" "c" "t" "t" "g" "t" "t" "a" "a" "a" "c" "a" "a" "c" "t" "t" "a" "g"
## [24463] "c" "t" "c" "c" "a" "a" "t" "t" "t" "t" "g" "g" "t" "g" "c" "a" "a" "t"
## [24481] "t" "t" "c" "a" "a" "g" "t" "g" "t" "t" "t" "t" "a" "a" "a" "t" "g" "a"
## [24499] "t" "a" "t" "c" "c" "t" "t" "t" "c" "a" "c" "g" "t" "c" "t" "t" "g" "a"
## [24517] "c" "a" "a" "a" "g" "t" "t" "g" "a" "g" "g" "c" "t" "g" "a" "a" "g" "t"
## [24535] "g" "c" "a" "a" "a" "t" "t" "g" "a" "t" "a" "g" "g" "t" "t" "g" "a" "t"
## [24553] "c" "a" "c" "a" "g" "g" "c" "a" "g" "a" "c" "t" "t" "c" "a" "a" "a" "g"
## [24571] "t" "t" "t" "g" "c" "a" "g" "a" "c" "a" "t" "a" "t" "g" "t" "g" "a" "c"
## [24589] "t" "c" "a" "a" "c" "a" "a" "t" "t" "a" "a" "t" "t" "a" "g" "a" "g" "c"
## [24607] "t" "g" "c" "a" "g" "a" "a" "a" "t" "c" "a" "g" "a" "g" "c" "t" "t" "c"
## [24625] "t" "g" "c" "t" "a" "a" "t" "c" "t" "t" "g" "c" "t" "g" "c" "t" "a" "c"
## [24643] "t" "a" "a" "a" "a" "t" "g" "t" "c" "a" "g" "a" "g" "t" "g" "t" "g" "t"
## [24661] "a" "c" "t" "t" "g" "g" "a" "c" "a" "a" "t" "c" "a" "a" "a" "a" "a" "g"
## [24679] "a" "g" "t" "t" "g" "a" "t" "t" "t" "t" "t" "g" "t" "g" "g" "a" "a" "a"
## [24697] "g" "g" "g" "c" "t" "a" "t" "c" "a" "t" "c" "t" "t" "a" "t" "g" "t" "c"
## [24715] "c" "t" "t" "c" "c" "c" "t" "c" "a" "g" "t" "c" "a" "g" "c" "a" "c" "c"
## [24733] "t" "c" "a" "t" "g" "g" "t" "g" "t" "a" "g" "t" "c" "t" "t" "c" "t" "t"
## [24751] "g" "c" "a" "t" "g" "t" "g" "a" "c" "t" "t" "a" "t" "g" "t" "c" "c" "c"
## [24769] "t" "g" "c" "a" "c" "a" "a" "g" "a" "a" "a" "a" "g" "a" "a" "c" "t" "t"
## [24787] "c" "a" "c" "a" "a" "c" "t" "g" "c" "t" "c" "c" "t" "g" "c" "c" "a" "t"
## [24805] "t" "t" "g" "t" "c" "a" "t" "g" "a" "t" "g" "g" "a" "a" "a" "a" "g" "c"
## [24823] "a" "c" "a" "c" "t" "t" "t" "c" "c" "t" "c" "g" "t" "g" "a" "a" "g" "g"
## [24841] "t" "g" "t" "c" "t" "t" "t" "g" "t" "t" "t" "c" "a" "a" "a" "t" "g" "g"
## [24859] "c" "a" "c" "a" "c" "a" "c" "t" "g" "g" "t" "t" "t" "g" "t" "a" "a" "c"
## [24877] "a" "c" "a" "a" "a" "g" "g" "a" "a" "t" "t" "t" "t" "t" "a" "t" "g" "a"
## [24895] "a" "c" "c" "a" "c" "a" "a" "a" "t" "c" "a" "t" "t" "a" "c" "t" "a" "c"
## [24913] "a" "g" "a" "c" "a" "a" "c" "a" "c" "a" "t" "t" "t" "g" "t" "g" "t" "c"
## [24931] "t" "g" "g" "t" "a" "a" "c" "t" "g" "t" "g" "a" "t" "g" "t" "t" "g" "t"
## [24949] "a" "a" "t" "a" "g" "g" "a" "a" "t" "t" "g" "t" "c" "a" "a" "c" "a" "a"
## [24967] "c" "a" "c" "a" "g" "t" "t" "t" "a" "t" "g" "a" "t" "c" "c" "t" "t" "t"
## [24985] "g" "c" "a" "a" "c" "c" "t" "g" "a" "a" "t" "t" "a" "g" "a" "c" "t" "c"
## [25003] "a" "t" "t" "c" "a" "a" "g" "g" "a" "g" "g" "a" "g" "t" "t" "a" "g" "a"
## [25021] "t" "a" "a" "a" "t" "a" "t" "t" "t" "t" "a" "a" "g" "a" "a" "t" "c" "a"
## [25039] "t" "a" "c" "a" "t" "c" "a" "c" "c" "a" "g" "a" "t" "g" "t" "t" "g" "a"
## [25057] "t" "t" "t" "a" "g" "g" "t" "g" "a" "c" "a" "t" "c" "t" "c" "t" "g" "g"
## [25075] "c" "a" "t" "t" "a" "a" "t" "g" "c" "t" "t" "c" "a" "g" "t" "t" "g" "t"
## [25093] "a" "a" "a" "c" "a" "t" "t" "c" "a" "a" "a" "a" "a" "g" "a" "a" "a" "t"
## [25111] "t" "g" "a" "c" "c" "g" "c" "c" "t" "c" "a" "a" "t" "g" "a" "g" "g" "t"
## [25129] "t" "g" "c" "c" "a" "a" "g" "a" "a" "t" "t" "t" "a" "a" "a" "t" "g" "a"
## [25147] "a" "t" "c" "t" "c" "t" "c" "a" "t" "c" "g" "a" "t" "c" "t" "c" "c" "a"
## [25165] "a" "g" "a" "a" "c" "t" "t" "g" "g" "a" "a" "a" "g" "t" "a" "t" "g" "a"
## [25183] "g" "c" "a" "g" "t" "a" "t" "a" "t" "a" "a" "a" "a" "t" "g" "g" "c" "c"
## [25201] "a" "t" "g" "g" "t" "a" "c" "a" "t" "t" "t" "g" "g" "c" "t" "a" "g" "g"
## [25219] "t" "t" "t" "t" "a" "t" "a" "g" "c" "t" "g" "g" "c" "t" "t" "g" "a" "t"
## [25237] "t" "g" "c" "c" "a" "t" "a" "g" "t" "a" "a" "t" "g" "g" "t" "g" "a" "c"
## [25255] "a" "a" "t" "t" "a" "t" "g" "c" "t" "t" "t" "g" "c" "t" "g" "t" "a" "t"
## [25273] "g" "a" "c" "c" "a" "g" "t" "t" "g" "c" "t" "g" "t" "a" "g" "t" "t" "g"
## [25291] "t" "c" "t" "c" "a" "a" "g" "g" "g" "c" "t" "g" "t" "t" "g" "t" "t" "c"
## [25309] "t" "t" "g" "t" "g" "g" "a" "t" "c" "c" "t" "g" "c" "t" "g" "c" "a" "a"
## [25327] "a" "t" "t" "t" "g" "a" "t" "g" "a" "a" "g" "a" "c" "g" "a" "c" "t" "c"
## [25345] "t" "g" "a" "g" "c" "c" "a" "g" "t" "g" "c" "t" "c" "a" "a" "a" "g" "g"
## [25363] "a" "g" "t" "c" "a" "a" "a" "t" "t" "a" "c" "a" "t" "t" "a" "c" "a" "c"
## [25381] "a" "t" "a" "a" "a" "c" "g" "a" "a" "c" "t" "t" "a" "t" "g" "g" "a" "t"
## [25399] "t" "t" "g" "t" "t" "t" "a" "t" "g" "a" "g" "a" "a" "t" "c" "t" "t" "c"
## [25417] "a" "c" "a" "a" "t" "t" "g" "g" "a" "a" "c" "t" "g" "t" "a" "a" "c" "t"
## [25435] "t" "t" "g" "a" "a" "g" "c" "a" "a" "g" "g" "t" "g" "a" "a" "a" "t" "c"
## [25453] "a" "a" "g" "g" "a" "t" "g" "c" "t" "a" "c" "t" "c" "c" "t" "t" "c" "a"
## [25471] "g" "a" "t" "t" "t" "t" "g" "t" "t" "c" "g" "c" "g" "c" "t" "a" "c" "t"
## [25489] "g" "c" "a" "a" "c" "g" "a" "t" "a" "c" "c" "g" "a" "t" "a" "c" "a" "a"
## [25507] "g" "c" "c" "t" "c" "a" "c" "t" "c" "c" "c" "t" "t" "t" "c" "g" "g" "a"
## [25525] "t" "g" "g" "c" "t" "t" "a" "t" "t" "g" "t" "t" "g" "g" "c" "g" "t" "t"
## [25543] "g" "c" "a" "c" "t" "t" "c" "t" "t" "g" "c" "t" "g" "t" "t" "t" "t" "t"
## [25561] "c" "a" "g" "a" "g" "c" "g" "c" "t" "t" "c" "c" "a" "a" "a" "a" "t" "c"
## [25579] "a" "t" "a" "a" "c" "c" "c" "t" "c" "a" "a" "a" "a" "a" "g" "a" "g" "a"
## [25597] "t" "g" "g" "c" "a" "a" "c" "t" "a" "g" "c" "a" "c" "t" "c" "t" "c" "c"
## [25615] "a" "a" "g" "g" "g" "t" "g" "t" "t" "c" "a" "c" "t" "t" "t" "g" "t" "t"
## [25633] "t" "g" "c" "a" "a" "c" "t" "t" "g" "c" "t" "g" "t" "t" "g" "t" "t" "g"
## [25651] "t" "t" "t" "g" "t" "a" "a" "c" "a" "g" "t" "t" "t" "a" "c" "t" "c" "a"
## [25669] "c" "a" "c" "c" "t" "t" "t" "t" "g" "c" "t" "c" "g" "t" "t" "g" "c" "t"
## [25687] "g" "c" "t" "g" "g" "c" "c" "t" "t" "g" "a" "a" "g" "c" "c" "c" "c" "t"
## [25705] "t" "t" "t" "c" "t" "c" "t" "a" "t" "c" "t" "t" "t" "a" "t" "g" "c" "t"
## [25723] "t" "t" "a" "g" "t" "c" "t" "a" "c" "t" "t" "c" "t" "t" "g" "c" "a" "g"
## [25741] "a" "g" "t" "a" "t" "a" "a" "a" "c" "t" "t" "t" "g" "t" "a" "a" "g" "a"
## [25759] "a" "t" "a" "a" "t" "a" "a" "t" "g" "a" "g" "g" "c" "t" "t" "t" "g" "g"
## [25777] "c" "t" "t" "t" "g" "c" "t" "g" "g" "a" "a" "a" "t" "g" "c" "c" "g" "t"
## [25795] "t" "c" "c" "a" "a" "a" "a" "a" "c" "c" "c" "a" "t" "t" "a" "c" "t" "t"
## [25813] "t" "a" "t" "g" "a" "t" "g" "c" "c" "a" "a" "c" "t" "a" "t" "t" "t" "t"
## [25831] "c" "t" "t" "t" "g" "c" "t" "g" "g" "c" "a" "t" "a" "c" "t" "a" "a" "t"
## [25849] "t" "g" "t" "t" "a" "c" "g" "a" "c" "t" "a" "t" "t" "g" "t" "a" "t" "a"
## [25867] "c" "c" "t" "t" "a" "c" "a" "a" "t" "a" "g" "t" "g" "t" "a" "a" "c" "t"
## [25885] "t" "c" "t" "t" "c" "a" "a" "t" "t" "g" "t" "c" "a" "t" "t" "a" "c" "t"
## [25903] "t" "c" "a" "g" "g" "t" "g" "a" "t" "g" "g" "c" "a" "c" "a" "a" "c" "a"
## [25921] "a" "g" "t" "c" "c" "t" "a" "t" "t" "t" "c" "t" "g" "a" "a" "c" "a" "t"
## [25939] "g" "a" "c" "t" "a" "c" "c" "a" "g" "a" "t" "t" "g" "g" "t" "g" "g" "t"
## [25957] "t" "a" "t" "a" "c" "t" "g" "a" "a" "a" "a" "a" "t" "g" "g" "g" "a" "a"
## [25975] "t" "c" "t" "g" "g" "a" "g" "t" "a" "a" "a" "a" "g" "a" "c" "t" "g" "t"
## [25993] "g" "t" "t" "g" "t" "a" "t" "t" "a" "c" "a" "c" "a" "g" "t" "t" "a" "c"
## [26011] "t" "t" "c" "a" "c" "t" "t" "c" "a" "g" "a" "c" "t" "a" "t" "t" "a" "c"
## [26029] "c" "a" "g" "c" "t" "g" "t" "a" "c" "t" "c" "a" "a" "c" "t" "c" "a" "a"
## [26047] "t" "t" "g" "a" "g" "t" "a" "c" "a" "g" "a" "c" "a" "c" "t" "g" "g" "t"
## [26065] "g" "t" "t" "g" "a" "a" "c" "a" "t" "g" "t" "t" "a" "c" "c" "t" "t" "c"
## [26083] "t" "t" "c" "a" "t" "c" "t" "a" "c" "a" "a" "t" "a" "a" "a" "a" "t" "t"
## [26101] "g" "t" "t" "g" "a" "t" "g" "a" "g" "c" "c" "t" "g" "a" "a" "g" "a" "a"
## [26119] "c" "a" "t" "g" "t" "c" "c" "a" "a" "a" "t" "t" "c" "a" "c" "a" "c" "a"
## [26137] "a" "t" "c" "g" "a" "c" "g" "g" "t" "t" "c" "a" "t" "c" "c" "g" "g" "a"
## [26155] "g" "t" "t" "g" "t" "t" "a" "a" "t" "c" "c" "a" "g" "t" "a" "a" "t" "g"
## [26173] "g" "a" "a" "c" "c" "a" "a" "t" "t" "t" "a" "t" "g" "a" "t" "g" "a" "a"
## [26191] "c" "c" "g" "a" "c" "g" "a" "c" "g" "a" "c" "t" "a" "c" "t" "a" "g" "c"
## [26209] "g" "t" "g" "c" "c" "t" "t" "t" "g" "t" "a" "a" "g" "c" "a" "c" "a" "a"
## [26227] "g" "c" "t" "g" "a" "t" "g" "a" "g" "t" "a" "c" "g" "a" "a" "c" "t" "t"
## [26245] "a" "t" "g" "t" "a" "c" "t" "c" "a" "t" "t" "c" "g" "t" "t" "t" "c" "g"
## [26263] "g" "a" "a" "g" "a" "g" "a" "c" "a" "g" "g" "t" "a" "c" "g" "t" "t" "a"
## [26281] "a" "t" "a" "g" "t" "t" "a" "a" "t" "a" "g" "c" "g" "t" "a" "c" "t" "t"
## [26299] "c" "t" "t" "t" "t" "t" "c" "t" "t" "g" "c" "t" "t" "t" "c" "g" "t" "g"
## [26317] "g" "t" "a" "t" "t" "c" "t" "t" "g" "c" "t" "a" "g" "t" "t" "a" "c" "a"
## [26335] "c" "t" "a" "g" "c" "c" "a" "t" "c" "c" "t" "t" "a" "c" "t" "g" "c" "g"
## [26353] "c" "t" "t" "c" "g" "a" "t" "t" "g" "t" "g" "t" "g" "c" "g" "t" "a" "c"
## [26371] "t" "g" "c" "t" "g" "c" "a" "a" "t" "a" "t" "t" "g" "t" "t" "a" "a" "c"
## [26389] "g" "t" "g" "a" "g" "t" "c" "t" "t" "g" "t" "a" "a" "a" "a" "c" "c" "t"
## [26407] "t" "c" "t" "t" "t" "t" "t" "a" "c" "g" "t" "t" "t" "a" "c" "t" "c" "t"
## [26425] "c" "g" "t" "g" "t" "t" "a" "a" "a" "a" "a" "t" "c" "t" "g" "a" "a" "t"
## [26443] "t" "c" "t" "t" "c" "t" "a" "g" "a" "g" "t" "t" "c" "c" "t" "g" "a" "t"
## [26461] "c" "t" "t" "c" "t" "g" "g" "t" "c" "t" "a" "a" "a" "c" "g" "a" "a" "c"
## [26479] "t" "a" "a" "a" "t" "a" "t" "t" "a" "t" "a" "t" "t" "a" "g" "t" "t" "t"
## [26497] "t" "t" "c" "t" "g" "t" "t" "t" "g" "g" "a" "a" "c" "t" "t" "t" "a" "a"
## [26515] "t" "t" "t" "t" "a" "g" "c" "c" "a" "t" "g" "g" "c" "a" "g" "a" "t" "t"
## [26533] "c" "c" "a" "a" "c" "g" "g" "t" "a" "c" "t" "a" "t" "t" "a" "c" "c" "g"
## [26551] "t" "t" "g" "a" "a" "g" "a" "g" "c" "t" "t" "a" "a" "a" "a" "a" "g" "c"
## [26569] "t" "c" "c" "t" "t" "g" "a" "a" "c" "a" "a" "t" "g" "g" "a" "a" "c" "c"
## [26587] "t" "a" "g" "t" "a" "a" "t" "a" "g" "g" "t" "t" "t" "c" "c" "t" "a" "t"
## [26605] "t" "c" "c" "t" "t" "a" "c" "a" "t" "g" "g" "a" "t" "t" "t" "g" "t" "c"
## [26623] "t" "t" "c" "t" "a" "c" "a" "a" "t" "t" "t" "g" "c" "c" "t" "a" "t" "g"
## [26641] "c" "c" "a" "a" "c" "a" "g" "g" "a" "a" "t" "a" "g" "g" "t" "t" "t" "t"
## [26659] "t" "g" "t" "a" "t" "a" "t" "a" "a" "t" "t" "a" "a" "g" "t" "t" "a" "a"
## [26677] "t" "t" "t" "t" "c" "c" "t" "c" "t" "g" "g" "c" "t" "g" "t" "t" "a" "t"
## [26695] "g" "g" "c" "c" "a" "g" "t" "a" "a" "c" "t" "t" "t" "a" "g" "c" "t" "t"
## [26713] "g" "t" "t" "t" "t" "g" "t" "g" "c" "t" "t" "g" "c" "t" "g" "c" "t" "g"
## [26731] "t" "t" "t" "a" "c" "a" "g" "a" "a" "t" "a" "a" "a" "t" "t" "g" "g" "a"
## [26749] "t" "c" "a" "c" "c" "g" "g" "t" "g" "g" "a" "a" "t" "t" "g" "c" "t" "a"
## [26767] "t" "c" "g" "c" "a" "a" "t" "g" "g" "c" "t" "t" "g" "t" "c" "t" "t" "g"
## [26785] "t" "a" "g" "g" "c" "t" "t" "g" "a" "t" "g" "t" "g" "g" "c" "t" "c" "a"
## [26803] "g" "c" "t" "a" "c" "t" "t" "c" "a" "t" "t" "g" "c" "t" "t" "c" "t" "t"
## [26821] "t" "c" "a" "g" "a" "c" "t" "g" "t" "t" "t" "g" "c" "g" "c" "g" "t" "a"
## [26839] "c" "g" "c" "g" "t" "t" "c" "c" "a" "t" "g" "t" "g" "g" "t" "c" "a" "t"
## [26857] "t" "c" "a" "a" "t" "c" "c" "a" "g" "a" "a" "a" "c" "t" "a" "a" "c" "a"
## [26875] "t" "t" "c" "t" "t" "c" "t" "c" "a" "a" "c" "g" "t" "g" "c" "c" "a" "c"
## [26893] "t" "c" "c" "a" "t" "g" "g" "c" "a" "c" "t" "a" "t" "t" "c" "t" "g" "a"
## [26911] "c" "c" "a" "g" "a" "c" "c" "g" "c" "t" "t" "c" "t" "a" "g" "a" "a" "a"
## [26929] "g" "t" "g" "a" "a" "c" "t" "c" "g" "t" "a" "a" "t" "c" "g" "g" "a" "g"
## [26947] "c" "t" "g" "t" "g" "a" "t" "c" "c" "t" "t" "c" "g" "t" "g" "g" "a" "c"
## [26965] "a" "t" "c" "t" "t" "c" "g" "t" "a" "t" "t" "g" "c" "t" "g" "g" "a" "c"
## [26983] "a" "c" "c" "a" "t" "c" "t" "a" "g" "g" "a" "c" "g" "c" "t" "g" "t" "g"
## [27001] "a" "c" "a" "t" "c" "a" "a" "g" "g" "a" "c" "c" "t" "g" "c" "c" "t" "a"
## [27019] "a" "a" "g" "a" "a" "a" "t" "c" "a" "c" "t" "g" "t" "t" "g" "c" "t" "a"
## [27037] "c" "a" "t" "c" "a" "c" "g" "a" "a" "c" "g" "c" "t" "t" "t" "c" "t" "t"
## [27055] "a" "t" "t" "a" "c" "a" "a" "a" "t" "t" "g" "g" "g" "a" "g" "c" "t" "t"
## [27073] "c" "g" "c" "a" "g" "c" "g" "t" "g" "t" "a" "g" "c" "a" "g" "g" "t" "g"
## [27091] "a" "c" "t" "c" "a" "g" "g" "t" "t" "t" "t" "g" "c" "t" "g" "c" "a" "t"
## [27109] "a" "c" "a" "g" "t" "c" "g" "c" "t" "a" "c" "a" "g" "g" "a" "t" "t" "g"
## [27127] "g" "c" "a" "a" "c" "t" "a" "t" "a" "a" "a" "t" "t" "a" "a" "a" "c" "a"
## [27145] "c" "a" "g" "a" "c" "c" "a" "t" "t" "c" "c" "a" "g" "t" "a" "g" "c" "a"
## [27163] "g" "t" "g" "a" "c" "a" "a" "t" "a" "t" "t" "g" "c" "t" "t" "t" "g" "c"
## [27181] "t" "t" "g" "t" "a" "c" "a" "g" "t" "a" "a" "g" "t" "g" "a" "c" "a" "a"
## [27199] "c" "a" "g" "a" "t" "g" "t" "t" "t" "c" "a" "t" "c" "t" "c" "g" "t" "t"
## [27217] "g" "a" "c" "t" "t" "t" "c" "a" "g" "g" "t" "t" "a" "c" "t" "a" "t" "a"
## [27235] "g" "c" "a" "g" "a" "g" "a" "t" "a" "t" "t" "a" "c" "t" "a" "a" "t" "t"
## [27253] "a" "t" "t" "a" "t" "g" "a" "g" "g" "a" "c" "t" "t" "t" "t" "a" "a" "a"
## [27271] "g" "t" "t" "t" "c" "c" "a" "t" "t" "t" "g" "g" "a" "a" "t" "c" "t" "t"
## [27289] "g" "a" "t" "t" "a" "c" "a" "t" "c" "a" "t" "a" "a" "a" "c" "c" "t" "c"
## [27307] "a" "t" "a" "a" "t" "t" "a" "a" "a" "a" "a" "t" "t" "t" "a" "t" "c" "t"
## [27325] "a" "a" "g" "t" "c" "a" "c" "t" "a" "a" "c" "t" "g" "a" "g" "a" "a" "t"
## [27343] "a" "a" "a" "t" "a" "t" "t" "c" "t" "c" "a" "a" "t" "t" "a" "g" "a" "t"
## [27361] "g" "a" "a" "g" "a" "g" "c" "a" "a" "c" "c" "a" "a" "t" "g" "g" "a" "g"
## [27379] "a" "t" "t" "g" "a" "t" "t" "a" "a" "a" "c" "g" "a" "a" "c" "a" "t" "g"
## [27397] "a" "a" "a" "a" "t" "t" "a" "t" "t" "c" "t" "t" "t" "t" "c" "t" "t" "g"
## [27415] "g" "c" "a" "c" "t" "g" "a" "t" "a" "a" "c" "a" "c" "t" "c" "g" "c" "t"
## [27433] "a" "c" "t" "t" "g" "t" "g" "a" "g" "c" "t" "t" "t" "a" "t" "c" "a" "c"
## [27451] "t" "a" "c" "c" "a" "a" "g" "a" "g" "t" "g" "t" "g" "t" "t" "a" "g" "a"
## [27469] "g" "g" "t" "a" "c" "a" "a" "c" "a" "g" "t" "a" "c" "t" "t" "t" "t" "a"
## [27487] "a" "a" "a" "g" "a" "a" "c" "c" "t" "t" "g" "c" "t" "c" "t" "t" "c" "t"
## [27505] "g" "g" "a" "a" "c" "a" "t" "a" "c" "g" "a" "g" "g" "g" "c" "a" "a" "t"
## [27523] "t" "c" "a" "c" "c" "a" "t" "t" "t" "c" "a" "t" "c" "c" "t" "c" "t" "a"
## [27541] "g" "c" "t" "g" "a" "t" "a" "a" "c" "a" "a" "a" "t" "t" "t" "g" "c" "a"
## [27559] "c" "t" "g" "a" "c" "t" "t" "g" "c" "t" "t" "t" "a" "g" "c" "a" "c" "t"
## [27577] "c" "a" "a" "t" "t" "t" "g" "c" "t" "t" "t" "t" "g" "c" "t" "t" "g" "t"
## [27595] "c" "c" "t" "g" "a" "c" "g" "g" "c" "g" "t" "a" "a" "a" "a" "c" "a" "c"
## [27613] "g" "t" "c" "t" "a" "t" "c" "a" "g" "t" "t" "a" "c" "g" "t" "g" "c" "c"
## [27631] "a" "g" "a" "t" "c" "a" "g" "t" "t" "t" "c" "a" "c" "c" "t" "a" "a" "a"
## [27649] "c" "t" "g" "t" "t" "c" "a" "t" "c" "a" "g" "a" "c" "a" "a" "g" "a" "g"
## [27667] "g" "a" "a" "g" "t" "t" "c" "a" "a" "g" "a" "a" "c" "t" "t" "t" "a" "c"
## [27685] "t" "c" "t" "c" "c" "a" "a" "t" "t" "t" "t" "t" "c" "t" "t" "a" "t" "t"
## [27703] "g" "t" "t" "g" "c" "g" "g" "c" "a" "a" "t" "a" "g" "t" "g" "t" "t" "t"
## [27721] "a" "t" "a" "a" "c" "a" "c" "t" "t" "t" "g" "c" "t" "t" "c" "a" "c" "a"
## [27739] "c" "t" "c" "a" "a" "a" "a" "g" "a" "a" "a" "g" "a" "c" "a" "g" "a" "a"
## [27757] "t" "g" "a" "t" "t" "g" "a" "a" "c" "t" "t" "t" "c" "a" "t" "t" "a" "a"
## [27775] "t" "t" "g" "a" "c" "t" "t" "c" "t" "a" "t" "t" "t" "g" "t" "g" "c" "t"
## [27793] "t" "t" "t" "t" "a" "g" "c" "c" "t" "t" "t" "c" "t" "g" "c" "t" "a" "t"
## [27811] "t" "c" "c" "t" "t" "g" "t" "t" "t" "t" "a" "a" "t" "t" "a" "t" "g" "c"
## [27829] "t" "t" "a" "t" "t" "a" "t" "c" "t" "t" "t" "t" "g" "g" "t" "t" "c" "t"
## [27847] "c" "a" "c" "t" "t" "g" "a" "a" "c" "t" "g" "c" "a" "a" "g" "a" "t" "c"
## [27865] "a" "t" "a" "a" "t" "g" "a" "a" "a" "c" "t" "t" "g" "t" "c" "a" "c" "g"
## [27883] "c" "c" "t" "a" "a" "a" "c" "g" "a" "a" "c" "a" "t" "g" "a" "a" "a" "t"
## [27901] "t" "t" "c" "t" "t" "g" "t" "t" "t" "t" "c" "t" "t" "a" "g" "g" "a" "a"
## [27919] "t" "c" "a" "t" "c" "a" "c" "a" "a" "c" "t" "g" "t" "a" "g" "c" "t" "g"
## [27937] "c" "a" "t" "t" "t" "c" "a" "c" "c" "a" "a" "g" "a" "a" "t" "g" "t" "a"
## [27955] "g" "t" "t" "t" "a" "c" "a" "g" "t" "c" "a" "t" "g" "t" "a" "c" "t" "c"
## [27973] "a" "a" "c" "a" "t" "c" "a" "a" "c" "c" "a" "t" "a" "t" "g" "t" "a" "g"
## [27991] "t" "t" "g" "a" "t" "g" "a" "c" "c" "c" "g" "t" "g" "t" "c" "c" "t" "a"
## [28009] "t" "t" "c" "a" "c" "t" "t" "c" "t" "a" "t" "t" "c" "t" "a" "a" "a" "t"
## [28027] "g" "g" "t" "a" "t" "a" "t" "t" "a" "g" "a" "g" "t" "a" "g" "g" "a" "g"
## [28045] "c" "t" "a" "g" "a" "a" "a" "a" "t" "c" "a" "g" "c" "a" "c" "c" "t" "t"
## [28063] "t" "a" "a" "t" "t" "g" "a" "a" "t" "t" "g" "t" "g" "c" "g" "t" "g" "g"
## [28081] "a" "t" "g" "a" "g" "g" "c" "t" "g" "g" "t" "t" "c" "t" "a" "a" "a" "t"
## [28099] "c" "a" "c" "c" "c" "a" "t" "t" "c" "a" "g" "t" "a" "c" "a" "t" "c" "g"
## [28117] "a" "t" "a" "t" "c" "g" "g" "t" "a" "a" "t" "t" "a" "t" "a" "c" "a" "g"
## [28135] "t" "t" "t" "c" "c" "t" "g" "t" "t" "t" "a" "c" "c" "t" "t" "t" "t" "a"
## [28153] "c" "a" "a" "t" "t" "a" "a" "t" "t" "g" "c" "c" "a" "g" "g" "a" "a" "c"
## [28171] "c" "t" "a" "a" "a" "t" "t" "g" "g" "g" "t" "a" "g" "t" "c" "t" "t" "g"
## [28189] "t" "a" "g" "t" "g" "c" "g" "t" "t" "g" "t" "t" "c" "g" "t" "t" "c" "t"
## [28207] "a" "t" "g" "a" "a" "g" "a" "c" "t" "t" "t" "t" "t" "a" "g" "a" "g" "t"
## [28225] "a" "t" "c" "a" "t" "g" "a" "c" "g" "t" "t" "c" "g" "t" "g" "t" "t" "g"
## [28243] "t" "t" "t" "t" "a" "g" "a" "t" "t" "t" "c" "a" "t" "c" "t" "a" "a" "a"
## [28261] "c" "g" "a" "a" "c" "a" "a" "a" "c" "t" "a" "a" "a" "a" "t" "g" "t" "c"
## [28279] "t" "g" "a" "t" "a" "a" "t" "g" "g" "a" "c" "c" "c" "c" "a" "a" "a" "a"
## [28297] "t" "c" "a" "g" "c" "g" "a" "a" "a" "t" "g" "c" "a" "c" "c" "c" "c" "g"
## [28315] "c" "a" "t" "t" "a" "c" "g" "t" "t" "t" "g" "g" "t" "g" "g" "a" "c" "c"
## [28333] "c" "t" "c" "a" "g" "a" "t" "t" "c" "a" "a" "c" "t" "g" "g" "c" "a" "g"
## [28351] "t" "a" "a" "c" "c" "a" "g" "a" "a" "t" "g" "g" "a" "g" "a" "a" "c" "g"
## [28369] "c" "a" "g" "t" "g" "g" "g" "g" "c" "g" "c" "g" "a" "t" "c" "a" "a" "a"
## [28387] "a" "c" "a" "a" "c" "g" "t" "c" "g" "g" "c" "c" "c" "c" "a" "a" "g" "g"
## [28405] "t" "t" "t" "a" "c" "c" "c" "a" "a" "t" "a" "a" "t" "a" "c" "t" "g" "c"
## [28423] "g" "t" "c" "t" "t" "g" "g" "t" "t" "c" "a" "c" "c" "g" "c" "t" "c" "t"
## [28441] "c" "a" "c" "t" "c" "a" "a" "c" "a" "t" "g" "g" "c" "a" "a" "g" "g" "a"
## [28459] "a" "g" "a" "c" "c" "t" "t" "a" "a" "a" "t" "t" "c" "c" "c" "t" "c" "g"
## [28477] "a" "g" "g" "a" "c" "a" "a" "g" "g" "c" "g" "t" "t" "c" "c" "a" "a" "t"
## [28495] "t" "a" "a" "c" "a" "c" "c" "a" "a" "t" "a" "g" "c" "a" "g" "t" "c" "c"
## [28513] "a" "g" "a" "t" "g" "a" "c" "c" "a" "a" "a" "t" "t" "g" "g" "c" "t" "a"
## [28531] "c" "t" "a" "c" "c" "g" "a" "a" "g" "a" "g" "c" "t" "a" "c" "c" "a" "g"
## [28549] "a" "c" "g" "a" "a" "t" "t" "c" "g" "t" "g" "g" "t" "g" "g" "t" "g" "a"
## [28567] "c" "g" "g" "t" "a" "a" "a" "a" "t" "g" "a" "a" "a" "g" "a" "t" "c" "t"
## [28585] "c" "a" "g" "t" "c" "c" "a" "a" "g" "a" "t" "g" "g" "t" "a" "t" "t" "t"
## [28603] "c" "t" "a" "c" "t" "a" "c" "c" "t" "a" "g" "g" "a" "a" "c" "t" "g" "g"
## [28621] "g" "c" "c" "a" "g" "a" "a" "g" "c" "t" "g" "g" "a" "c" "t" "t" "c" "c"
## [28639] "c" "t" "a" "t" "g" "g" "t" "g" "c" "t" "a" "a" "c" "a" "a" "a" "g" "a"
## [28657] "c" "g" "g" "c" "a" "t" "c" "a" "t" "a" "t" "g" "g" "g" "t" "t" "g" "c"
## [28675] "a" "a" "c" "t" "g" "a" "g" "g" "g" "a" "g" "c" "c" "t" "t" "g" "a" "a"
## [28693] "t" "a" "c" "a" "c" "c" "a" "a" "a" "a" "g" "a" "t" "c" "a" "c" "a" "t"
## [28711] "t" "g" "g" "c" "a" "c" "c" "c" "g" "c" "a" "a" "t" "c" "c" "t" "g" "c"
## [28729] "t" "a" "a" "c" "a" "a" "t" "g" "c" "t" "g" "c" "a" "a" "t" "c" "g" "t"
## [28747] "g" "c" "t" "a" "c" "a" "a" "c" "t" "t" "c" "c" "t" "c" "a" "a" "g" "g"
## [28765] "a" "a" "c" "a" "a" "c" "a" "t" "t" "g" "c" "c" "a" "a" "a" "a" "g" "g"
## [28783] "c" "t" "t" "c" "t" "a" "c" "g" "c" "a" "g" "a" "a" "g" "g" "g" "a" "g"
## [28801] "c" "a" "g" "a" "g" "g" "c" "g" "g" "c" "a" "g" "t" "c" "a" "a" "g" "c"
## [28819] "c" "t" "c" "t" "t" "c" "t" "c" "g" "t" "t" "c" "c" "t" "c" "a" "t" "c"
## [28837] "a" "c" "g" "t" "a" "g" "t" "c" "g" "c" "a" "a" "c" "a" "g" "t" "t" "c"
## [28855] "a" "a" "g" "a" "a" "a" "t" "t" "c" "a" "a" "c" "t" "c" "c" "a" "g" "g"
## [28873] "c" "a" "g" "c" "a" "g" "t" "a" "g" "g" "g" "g" "a" "a" "c" "t" "t" "c"
## [28891] "t" "c" "c" "t" "g" "c" "t" "a" "g" "a" "a" "t" "g" "g" "c" "t" "g" "g"
## [28909] "c" "a" "a" "t" "g" "g" "c" "g" "g" "t" "g" "a" "t" "g" "c" "t" "g" "c"
## [28927] "t" "c" "t" "t" "g" "c" "t" "t" "t" "g" "c" "t" "g" "c" "t" "g" "c" "t"
## [28945] "t" "g" "a" "c" "a" "g" "a" "t" "t" "g" "a" "a" "c" "c" "a" "g" "c" "t"
## [28963] "t" "g" "a" "g" "a" "g" "c" "a" "a" "a" "a" "t" "g" "t" "c" "t" "g" "g"
## [28981] "t" "a" "a" "a" "g" "g" "c" "c" "a" "a" "c" "a" "a" "c" "a" "a" "c" "a"
## [28999] "a" "g" "g" "c" "c" "a" "a" "a" "c" "t" "g" "t" "c" "a" "c" "t" "a" "a"
## [29017] "g" "a" "a" "a" "t" "c" "t" "g" "c" "t" "g" "c" "t" "g" "a" "g" "g" "c"
## [29035] "t" "t" "c" "t" "a" "a" "g" "a" "a" "g" "c" "c" "t" "c" "g" "g" "c" "a"
## [29053] "a" "a" "a" "a" "c" "g" "t" "a" "c" "t" "g" "c" "c" "a" "c" "t" "a" "a"
## [29071] "a" "g" "c" "a" "t" "a" "c" "a" "a" "t" "g" "t" "a" "a" "c" "a" "c" "a"
## [29089] "a" "g" "c" "t" "t" "t" "c" "g" "g" "c" "a" "g" "a" "c" "g" "t" "g" "g"
## [29107] "t" "c" "c" "a" "g" "a" "a" "c" "a" "a" "a" "c" "c" "c" "a" "a" "g" "g"
## [29125] "a" "a" "a" "t" "t" "t" "t" "g" "g" "g" "g" "a" "c" "c" "a" "g" "g" "a"
## [29143] "a" "c" "t" "a" "a" "t" "c" "a" "g" "a" "c" "a" "a" "g" "g" "a" "a" "c"
## [29161] "t" "g" "a" "t" "t" "a" "c" "a" "a" "a" "c" "a" "t" "t" "g" "g" "c" "c"
## [29179] "g" "c" "a" "a" "a" "t" "t" "g" "c" "a" "c" "a" "a" "t" "t" "t" "g" "c"
## [29197] "c" "c" "c" "c" "a" "g" "c" "g" "c" "t" "t" "c" "a" "g" "c" "g" "t" "t"
## [29215] "c" "t" "t" "c" "g" "g" "a" "a" "t" "g" "t" "c" "g" "c" "g" "c" "a" "t"
## [29233] "t" "g" "g" "c" "a" "t" "g" "g" "a" "a" "g" "t" "c" "a" "c" "a" "c" "c"
## [29251] "t" "t" "c" "g" "g" "g" "a" "a" "c" "g" "t" "g" "g" "t" "t" "g" "a" "c"
## [29269] "c" "t" "a" "c" "a" "c" "a" "g" "g" "t" "g" "c" "c" "a" "t" "c" "a" "a"
## [29287] "a" "t" "t" "g" "g" "a" "t" "g" "a" "c" "a" "a" "a" "g" "a" "t" "c" "c"
## [29305] "a" "a" "a" "t" "t" "t" "c" "a" "a" "a" "g" "a" "t" "c" "a" "a" "g" "t"
## [29323] "c" "a" "t" "t" "t" "t" "g" "c" "t" "g" "a" "a" "t" "a" "a" "g" "c" "a"
## [29341] "t" "a" "t" "t" "g" "a" "c" "g" "c" "a" "t" "a" "c" "a" "a" "a" "a" "c"
## [29359] "a" "t" "t" "c" "c" "c" "a" "c" "c" "a" "a" "c" "a" "g" "a" "g" "c" "c"
## [29377] "t" "a" "a" "a" "a" "a" "g" "g" "a" "c" "a" "a" "a" "a" "a" "g" "a" "a"
## [29395] "g" "a" "a" "g" "g" "c" "t" "g" "a" "t" "g" "a" "a" "a" "c" "t" "c" "a"
## [29413] "a" "g" "c" "c" "t" "t" "a" "c" "c" "g" "c" "a" "g" "a" "g" "a" "c" "a"
## [29431] "g" "a" "a" "g" "a" "a" "a" "c" "a" "g" "c" "a" "a" "a" "c" "t" "g" "t"
## [29449] "g" "a" "c" "t" "c" "t" "t" "c" "t" "t" "c" "c" "t" "g" "c" "t" "g" "c"
## [29467] "a" "g" "a" "t" "t" "t" "g" "g" "a" "t" "g" "a" "t" "t" "t" "c" "t" "c"
## [29485] "c" "a" "a" "a" "c" "a" "a" "t" "t" "g" "c" "a" "a" "c" "a" "a" "t" "c"
## [29503] "c" "a" "t" "g" "a" "g" "c" "a" "g" "t" "g" "c" "t" "g" "a" "c" "t" "c"
## [29521] "a" "a" "c" "t" "c" "a" "g" "g" "c" "c" "t" "a" "a" "a" "c" "t" "c" "a"
## [29539] "t" "g" "c" "a" "g" "a" "c" "c" "a" "c" "a" "c" "a" "a" "g" "g" "c" "a"
## [29557] "g" "a" "t" "g" "g" "g" "c" "t" "a" "t" "a" "t" "a" "a" "a" "c" "g" "t"
## [29575] "t" "t" "t" "c" "g" "c" "t" "t" "t" "t" "c" "c" "g" "t" "t" "t" "a" "c"
## [29593] "g" "a" "t" "a" "t" "a" "t" "a" "g" "t" "c" "t" "a" "c" "t" "c" "t" "t"
## [29611] "g" "t" "g" "c" "a" "g" "a" "a" "t" "g" "a" "a" "t" "t" "c" "t" "c" "g"
## [29629] "t" "a" "a" "c" "t" "a" "c" "a" "t" "a" "g" "c" "a" "c" "a" "a" "g" "t"
## [29647] "a" "g" "a" "t" "g" "t" "a" "g" "t" "t" "a" "a" "c" "t" "t" "t" "a" "a"
## [29665] "t" "c" "t" "c" "a" "c" "a" "t" "a" "g" "c" "a" "a" "t" "c" "t" "t" "t"
## [29683] "a" "a" "t" "c" "a" "g" "t" "g" "t" "g" "t" "a" "a" "c" "a" "t" "t" "a"
## [29701] "g" "g" "g" "a" "g" "g" "a" "c" "t" "t" "g" "a" "a" "a" "g" "a" "g" "c"
## [29719] "c" "a" "c" "c" "a" "c" "a" "t" "t" "t" "t" "c" "a" "c" "c" "g" "a" "g"
## [29737] "g" "c" "c" "a" "c" "g" "c" "g" "g" "a" "g" "t" "a" "c" "g" "a" "t" "c"
## [29755] "g" "a" "g" "t" "g" "t" "a" "c" "a" "g" "t" "g" "a" "a" "c" "a" "a" "t"
## [29773] "g" "c" "t" "a" "g" "g" "g" "a" "g" "a" "g" "c" "t" "g" "c" "c" "t" "a"
## [29791] "t" "a" "t" "g" "g" "a" "a" "g" "a" "g" "c" "c" "c" "t" "a" "a" "t" "g"
## [29809] "t" "g" "t" "a" "a" "a" "a" "t" "t" "a" "a" "t" "t" "t" "t" "a" "g" "t"
## [29827] "a" "g" "t" "g" "c" "t" "a" "t" "c" "c" "c" "c" "a" "t" "g" "t" "g" "a"
## [29845] "t" "t" "t" "t" "a" "a" "t" "a" "g" "c" "t" "t" "c" "t" "t" "a" "g" "g"
## [29863] "a" "g" "a" "a" "t" "g" "a" "c" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
## [29881] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
## [29899] "a" "a" "a" "a" "a"

# Pandemic historical records
 pnds <- pandemics.data(tgt="pandemics")

## Data obtained fromPandemic historical records -- data from  https://www.visualcapitalist.com/history-of-pandemics-deadliest/

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

# Pandemics vaccines development times
 pnds.vacs <- pandemics.data(tgt="pandemics_vaccines")

## Data obtained fromPandemics vaccine development times -- data from  https://www.visualcapitalist.com/the-race-to-save-lives-comparing-vaccine-development-timelines/

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

# summary report for an specific location with default number of entries
report.summary(geo.loc="Libya")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:27:13 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-confirmed data detected -- 90 records (out of
## 284) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-CONFIRMED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:17 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-confirmed  Totals: 489940 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange  t-2  t-3  t-7 t-14 t-30
## 1          Libya                489940     100       0.11          1373 1815 2307 2457 3773 3063
## -------------------------------------------------------------------------------- 
##   Global Perc. Average:  0.11 (sd: NA) 
##   Global Perc. Average in top  1 :  0.11 (sd: NA) 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:27:19 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-deaths data detected -- 78 records (out of 284)
## show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-DEATHS Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:22 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-deaths  Totals: 6222 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Libya                  6222 1.27            12   9  10  13   11   14
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:27:24 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-recovered data detected -- 258 records (out of
## 269) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-RECOVERED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:27 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-recovered  Totals: 0 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Libya                     0    0             0   0   0   0    0    0
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Warning in report.summary(geo.loc = "Libya"): Graphical Output: Data yields non-
## positive results!

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): Column Active has 10 entries reporting negative values!
##   on entries: 33 417 489 1356 1449 1895 2563 2581 2738 3969

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): number of 'active+recovered+deaths' cases does NOT match the number of 'confirmed' cases!
##  on 8 entries -- 3956 3957 3959 3961 3966 3967 3969 3970
##   ||  FIPS  Admin2  Province_State  Country_Region  Last_Update  Lat  Long_  Confirmed  Deaths  Recovered  Active  Combined_Key  Incident_Rate  Case_Fatality_Ratio
##   3956 3957 3959 3961 3966 3967 3969 3970 || c(NA, NA, NA, NA, NA, NA, NA, NA)  c("", "", "", "", "", "", "", "")  c("Anguilla", "Bermuda", "Cayman Islands", "England", "Northern Ireland", "Scotland", "Unknown", "Wales")  c("United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom")  c("2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49")  c(18.2206, 32.3078, 19.3133, 52.3555, 54.7877, 56.4907, NA, 52.1307)  c(-63.0686, -64.7505, -81.2546, -1.1743, -6.4923, -4.2026, NA, -3.7837)  c(16, 691, 390, 3358064, 104274, 180533, 0, 192912)  c(0, 12, 2, 93816, 1861, 6112, 0, 4775)  c(15, 653, 357, 0, 0, 0, 0, 0)  c(2, 12, 28, 3358064, 104274, 180533, -106564, 192912)  c("Anguilla, United Kingdom", "Bermuda, United Kingdom", "Cayman Islands, United Kingdom", "England, United Kingdom", "Northern Ireland, United Kingdom", "Scotland, United Kingdom", "Unknown, United Kingdom", "Wales, United Kingdom")  c(113.318224236768, 1111.23600918536, 605.599513085819, 5998.98530115833, 5541.77295918367, 3304.46799553383, NA, 6146.43471611547)  c(0, 1.73661360347323, 0.512820512820513, 2.7937525907785, 1.78472102345743, 3.38553062321016, NA, 2.47522186281828)

## *** 17 entries were removed due to data inconsistences

##  >>> checking data consistency...

## This function applies to TimeSeries data only

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  CONFIRMED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:27 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  DEATHS Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:27 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  RECOVERED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:27 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  ACTIVE Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:27 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

##       Confirmed  Deaths  Recovered   Active 
##   Totals 
##       119402 1883    99586   17933 
##   Average 
##       119402 1883    99586   17933 
##   Standard Deviation 
##       NA NA  NA  NA 
##   
## 
##  * Statistical estimators computed considering 1 independent reported entries

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

##  
## 
## ******************************************************************************** 
## ********************************  OVERALL SUMMARY******************************** 
## ******************************************************************************** 
##   ****  Time Series  LIBYA TOTS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       489940 6222    0 
##              1.27%       0% 
##   ****  Time Series  LIBYA AVGS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       489940 6222    0 
##              1.27%       0% 
##   ****  Time Series  LIBYA SDS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       NA NA  NA 
##              NA%         NA% 
##   
## 
##  * Statistical estimators computed considering 1/1/1 independent reported entries per case-type 
## ********************************************************************************

 # summary report for an specific location with top 5
report.summary(Nentries=5, geo.loc="Libya")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:27:30 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-confirmed data detected -- 90 records (out of
## 284) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-CONFIRMED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:34 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-confirmed  Totals: 489940 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange  t-2  t-3  t-7 t-14 t-30
## 1          Libya                489940     100       0.11          1373 1815 2307 2457 3773 3063
## -------------------------------------------------------------------------------- 
##   Global Perc. Average:  0.11 (sd: NA) 
##   Global Perc. Average in top  1 :  0.11 (sd: NA) 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:27:36 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-deaths data detected -- 78 records (out of 284)
## show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-DEATHS Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:40 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-deaths  Totals: 6222 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Libya                  6222 1.27            12   9  10  13   11   14
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:27:42 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-recovered data detected -- 258 records (out of
## 269) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-RECOVERED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:45 
## ################################################################################ 
##   Number of Countries/Regions reported:  1 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-recovered  Totals: 0 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Libya                     0    0             0   0   0   0    0    0
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Warning in report.summary(Nentries = 5, geo.loc = "Libya"): Graphical Output:
## Data yields non-positive results!

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): Column Active has 10 entries reporting negative values!
##   on entries: 33 417 489 1356 1449 1895 2563 2581 2738 3969

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): number of 'active+recovered+deaths' cases does NOT match the number of 'confirmed' cases!
##  on 8 entries -- 3956 3957 3959 3961 3966 3967 3969 3970
##   ||  FIPS  Admin2  Province_State  Country_Region  Last_Update  Lat  Long_  Confirmed  Deaths  Recovered  Active  Combined_Key  Incident_Rate  Case_Fatality_Ratio
##   3956 3957 3959 3961 3966 3967 3969 3970 || c(NA, NA, NA, NA, NA, NA, NA, NA)  c("", "", "", "", "", "", "", "")  c("Anguilla", "Bermuda", "Cayman Islands", "England", "Northern Ireland", "Scotland", "Unknown", "Wales")  c("United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom")  c("2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49")  c(18.2206, 32.3078, 19.3133, 52.3555, 54.7877, 56.4907, NA, 52.1307)  c(-63.0686, -64.7505, -81.2546, -1.1743, -6.4923, -4.2026, NA, -3.7837)  c(16, 691, 390, 3358064, 104274, 180533, 0, 192912)  c(0, 12, 2, 93816, 1861, 6112, 0, 4775)  c(15, 653, 357, 0, 0, 0, 0, 0)  c(2, 12, 28, 3358064, 104274, 180533, -106564, 192912)  c("Anguilla, United Kingdom", "Bermuda, United Kingdom", "Cayman Islands, United Kingdom", "England, United Kingdom", "Northern Ireland, United Kingdom", "Scotland, United Kingdom", "Unknown, United Kingdom", "Wales, United Kingdom")  c(113.318224236768, 1111.23600918536, 605.599513085819, 5998.98530115833, 5541.77295918367, 3304.46799553383, NA, 6146.43471611547)  c(0, 1.73661360347323, 0.512820512820513, 2.7937525907785, 1.78472102345743, 3.38553062321016, NA, 2.47522186281828)

## *** 17 entries were removed due to data inconsistences

##  >>> checking data consistency...

## This function applies to TimeSeries data only

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  CONFIRMED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:45 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  DEATHS Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:46 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  RECOVERED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:46 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  ACTIVE Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:27:46 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 1 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 1 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##   Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1    Libya    119402           0.12   1883        1.58     99586           83.4  17933       15.02
## ============================================================================================================================================

##       Confirmed  Deaths  Recovered   Active 
##   Totals 
##       119402 1883    99586   17933 
##   Average 
##       119402 1883    99586   17933 
##   Standard Deviation 
##       NA NA  NA  NA 
##   
## 
##  * Statistical estimators computed considering 1 independent reported entries

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

##  
## 
## ******************************************************************************** 
## ********************************  OVERALL SUMMARY******************************** 
## ******************************************************************************** 
##   ****  Time Series  LIBYA TOTS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       489940 6222    0 
##              1.27%       0% 
##   ****  Time Series  LIBYA AVGS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       489940 6222    0 
##              1.27%       0% 
##   ****  Time Series  LIBYA SDS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       NA NA  NA 
##              NA%         NA% 
##   
## 
##  * Statistical estimators computed considering 1/1/1 independent reported entries per case-type 
## ********************************************************************************

# it can combine several locations
report.summary(Nentries=30, geo.loc=c("Egypt","Algeria","Qatar","Sudan","Saudi Arabia"))

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:27:48 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-confirmed data detected -- 90 records (out of
## 284) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-CONFIRMED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:52 
## ################################################################################ 
##   Number of Countries/Regions reported:  5 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-confirmed  Totals: 1898440 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange  t-2  t-3  t-7 t-14 t-30
## 1   Saudi Arabia                741864   39.08       0.17           627  841 1052 1569 2866 4541
## 2          Egypt                475341   25.04       0.11          1892 1989 2003 2071 2189 1809
## 3          Qatar                355397   18.72       0.08           365  394  416  447  783 2551
## 4        Algeria                264488   13.93       0.06           123  164  147  375  518 2521
## 5          Sudan                 61350    3.23       0.01            99   28 1284    0  110  684
## -------------------------------------------------------------------------------- 
##   Global Perc. Average:  0.09 (sd: 0.06) 
##   Global Perc. Average in top  5 :  0.09 (sd: 0.06) 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:27:53 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-deaths data detected -- 78 records (out of 284)
## show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-DEATHS Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:27:57 
## ################################################################################ 
##   Number of Countries/Regions reported:  5 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-deaths  Totals: 44254 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Egypt                 23889 5.03            32  51  54  61   62   35
## 2   Saudi Arabia                  8990 1.21             3   1   2   1    3    2
## 3        Algeria                  6816 2.58             4   7   8  10   12    8
## 4          Sudan                  3895 6.35             1   2  61   0    6    7
## 5          Qatar                   664 0.19             2   0   0   2    2    1
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:27:58 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

##  >>> checking data consistency...

## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-recovered data detected -- 258 records (out of
## 269) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-RECOVERED Cases  -- Data dated:  2022-02-23  ::  2022-02-25 06:28:02 
## ################################################################################ 
##   Number of Countries/Regions reported:  5 
##   Number of Cities/Provinces reported:  1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------- 
##   For selected locations ts-recovered  Totals: 0 
## -------------------------------------------------------------------------------- 
##   Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1          Egypt                     0    0             0   0   0   0    0    0
## 2        Algeria                     0    0             0   0   0   0    0    0
## 3          Qatar                     0    0             0   0   0   0    0    0
## 4          Sudan                     0    0             0   0   0   0    0    0
## 5   Saudi Arabia                     0    0             0   0   0   0    0    0
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================

## Warning in report.summary(Nentries = 30, geo.loc = c("Egypt", "Algeria", :
## Graphical Output: Data yields non-positive results!

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): Column Active has 10 entries reporting negative values!
##   on entries: 33 417 489 1356 1449 1895 2563 2581 2738 3969

## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): number of 'active+recovered+deaths' cases does NOT match the number of 'confirmed' cases!
##  on 8 entries -- 3956 3957 3959 3961 3966 3967 3969 3970
##   ||  FIPS  Admin2  Province_State  Country_Region  Last_Update  Lat  Long_  Confirmed  Deaths  Recovered  Active  Combined_Key  Incident_Rate  Case_Fatality_Ratio
##   3956 3957 3959 3961 3966 3967 3969 3970 || c(NA, NA, NA, NA, NA, NA, NA, NA)  c("", "", "", "", "", "", "", "")  c("Anguilla", "Bermuda", "Cayman Islands", "England", "Northern Ireland", "Scotland", "Unknown", "Wales")  c("United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom")  c("2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49", "2021-02-02 05:22:49")  c(18.2206, 32.3078, 19.3133, 52.3555, 54.7877, 56.4907, NA, 52.1307)  c(-63.0686, -64.7505, -81.2546, -1.1743, -6.4923, -4.2026, NA, -3.7837)  c(16, 691, 390, 3358064, 104274, 180533, 0, 192912)  c(0, 12, 2, 93816, 1861, 6112, 0, 4775)  c(15, 653, 357, 0, 0, 0, 0, 0)  c(2, 12, 28, 3358064, 104274, 180533, -106564, 192912)  c("Anguilla, United Kingdom", "Bermuda, United Kingdom", "Cayman Islands, United Kingdom", "England, United Kingdom", "Northern Ireland, United Kingdom", "Scotland, United Kingdom", "Unknown, United Kingdom", "Wales, United Kingdom")  c(113.318224236768, 1111.23600918536, 605.599513085819, 5998.98530115833, 5541.77295918367, 3304.46799553383, NA, 6146.43471611547)  c(0, 1.73661360347323, 0.512820512820513, 2.7937525907785, 1.78472102345743, 3.38553062321016, NA, 2.47522186281828)

## *** 17 entries were removed due to data inconsistences

##  >>> checking data consistency...

## This function applies to TimeSeries data only

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  CONFIRMED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:28:02 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 5 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Saudi Arabia    368329           0.37   6379        1.73    359839          97.69   2111        0.57
## 2        Egypt    166492           0.17   9360        5.62    130107          78.15  27025       16.23
## 3        Qatar    151720           0.15    249        0.16    145953          96.20   5518        3.64
## 4      Algeria    107578           0.11   2894        2.69     73530          68.35  31154       28.96
## 5        Sudan     29449           0.03   1812        6.15     21504          73.02   6133       20.83
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  DEATHS Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:28:02 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 5 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1        Egypt    166492           0.17   9360        5.62    130107          78.15  27025       16.23
## 2 Saudi Arabia    368329           0.37   6379        1.73    359839          97.69   2111        0.57
## 3      Algeria    107578           0.11   2894        2.69     73530          68.35  31154       28.96
## 4        Sudan     29449           0.03   1812        6.15     21504          73.02   6133       20.83
## 5        Qatar    151720           0.15    249        0.16    145953          96.20   5518        3.64
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  RECOVERED Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:28:02 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 5 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Saudi Arabia    368329           0.37   6379        1.73    359839          97.69   2111        0.57
## 2        Qatar    151720           0.15    249        0.16    145953          96.20   5518        3.64
## 3        Egypt    166492           0.17   9360        5.62    130107          78.15  27025       16.23
## 4      Algeria    107578           0.11   2894        2.69     73530          68.35  31154       28.96
## 5        Sudan     29449           0.03   1812        6.15     21504          73.02   6133       20.83
## ============================================================================================================================================

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  ACTIVE Cases  -- Data dated:  2021-02-02  ::  2022-02-25 06:28:02 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 5 
##   Number of Cities/Provinces reported: 1 
##   Unique number of distinct geographical locations combined: 5 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1      Algeria    107578           0.11   2894        2.69     73530          68.35  31154       28.96
## 2        Egypt    166492           0.17   9360        5.62    130107          78.15  27025       16.23
## 3        Sudan     29449           0.03   1812        6.15     21504          73.02   6133       20.83
## 4        Qatar    151720           0.15    249        0.16    145953          96.20   5518        3.64
## 5 Saudi Arabia    368329           0.37   6379        1.73    359839          97.69   2111        0.57
## ============================================================================================================================================

##       Confirmed  Deaths  Recovered   Active 
##   Totals 
##       823568 20694   730933  71941 
##   Average 
##       164713.6   4138.8  146186.6    14388.2 
##   Standard Deviation 
##       125690.31  3686.92 129161.42   13586.2 
##   
## 
##  * Statistical estimators computed considering 5 independent reported entries

##  >>> checking data integrity...

## checking for ... Country Province Lat Long

## No critical issues have been found.

## Possible <<Aggregated data-type>> detected...

## checking for ... Active Deaths Recovered Confirmed

## No critical issues have been found.

##  
## 
## ******************************************************************************** 
## ********************************  OVERALL SUMMARY******************************** 
## ******************************************************************************** 
##   ****  Time Series  EGYPT,ALGERIA,QATAR,SUDAN,SAUDI ARABIA TOTS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       1898440    44254   0 
##              2.33%       0% 
##   ****  Time Series  EGYPT,ALGERIA,QATAR,SUDAN,SAUDI ARABIA AVGS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       379688 8850.8  0 
##              2.33%       0% 
##   ****  Time Series  EGYPT,ALGERIA,QATAR,SUDAN,SAUDI ARABIA SDS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       252745.49  8970.66 0 
##              3.55%       0% 
##   
## 
##  * Statistical estimators computed considering 5/5/5 independent reported entries per case-type 
## ********************************************************************************

# totals for confirmed cases for "Ontario"
tots.per.location(covid19.confirmed.cases,geo.loc="Libya")

## LIBYA  --  489940 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53389 -31573 -18261  30737  91779 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -90957.63    2763.79  -32.91   <2e-16 ***
## x.var          640.21       6.26  102.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 38160 on 762 degrees of freedom
## Multiple R-squared:  0.9321, Adjusted R-squared:  0.932 
## F-statistic: 1.046e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7507 -1.3923  0.1198  1.8188  2.8132 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.7818585  0.1495524   25.29   <2e-16 ***
## x.var       0.0156259  0.0003387   46.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 762 degrees of freedom
## Multiple R-squared:  0.7364, Adjusted R-squared:  0.736 
## F-statistic:  2128 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -220.50  -167.35    11.74    97.51   130.65  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.413e+00  3.470e-04   27124   <2e-16 ***
## x.var       5.121e-03  5.721e-07    8951   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 122639059  on 763  degrees of freedom
## Residual deviance:  13466385  on 762  degrees of freedom
## AIC: 13475131
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total for confirmed cases for "Canada"
tots.per.location(covid19.confirmed.cases,geo.loc="Egypt")

## EGYPT  --  475341 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -29380 -13550  -3333  11883  67994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -41302.596   1292.543  -31.95   <2e-16 ***
## x.var          587.238      2.927  200.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17850 on 762 degrees of freedom
## Multiple R-squared:  0.9814, Adjusted R-squared:  0.9814 
## F-statistic: 4.024e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3213 -0.9065  0.4730  1.3987  2.4846 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0892644  0.1566383   45.26   <2e-16 ***
## x.var       0.0100880  0.0003548   28.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.163 on 762 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.5142 
## F-statistic: 808.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -287.37   -85.11    29.19    85.02   122.62  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.045e+01  2.584e-04   40449   <2e-16 ***
## x.var       3.584e-03  4.537e-07    7900   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86357718  on 763  degrees of freedom
## Residual deviance: 12540366  on 762  degrees of freedom
## AIC: 12550086
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of deaths for "Mainland China"
tots.per.location(covid19.TS.deaths,geo.loc="Saudi Arabia")

## SAUDI ARABIA  --  8990 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1720.6  -803.4   260.6   653.3  1391.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 178.8590    65.6926   2.723  0.00662 ** 
## x.var        13.7850     0.1488  92.651  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 907 on 762 degrees of freedom
## Multiple R-squared:  0.9185, Adjusted R-squared:  0.9184 
## F-statistic:  8584 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8430 -0.8891  0.3191  1.4740  2.0395 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3069326  0.1307682   32.94   <2e-16 ***
## x.var       0.0086457  0.0002962   29.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 762 degrees of freedom
## Multiple R-squared:  0.5279, Adjusted R-squared:  0.5273 
## F-statistic: 852.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -62.137  -26.057    6.603   19.457   31.179  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.398e+00  1.335e-03    5542   <2e-16 ***
## x.var       2.703e-03  2.456e-06    1100   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2049545  on 763  degrees of freedom
## Residual deviance:  711054  on 762  degrees of freedom
## AIC: 718163
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Egypt", confBnd=TRUE)

## EGYPT  --  475341 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -29380 -13550  -3333  11883  67994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -41302.596   1292.543  -31.95   <2e-16 ***
## x.var          587.238      2.927  200.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17850 on 762 degrees of freedom
## Multiple R-squared:  0.9814, Adjusted R-squared:  0.9814 
## F-statistic: 4.024e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3213 -0.9065  0.4730  1.3987  2.4846 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0892644  0.1566383   45.26   <2e-16 ***
## x.var       0.0100880  0.0003548   28.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.163 on 762 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.5142 
## F-statistic: 808.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -287.37   -85.11    29.19    85.02   122.62  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.045e+01  2.584e-04   40449   <2e-16 ***
## x.var       3.584e-03  4.537e-07    7900   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86357718  on 763  degrees of freedom
## Residual deviance: 12540366  on 762  degrees of freedom
## AIC: 12550086
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Jordan", confBnd=TRUE)

## JORDAN  --  1599422 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -202304  -83838  -11614   78147  456969 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -257053.30    8750.92  -29.37   <2e-16 ***
## x.var          1831.81      19.82   92.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 120800 on 762 degrees of freedom
## Multiple R-squared:  0.9181, Adjusted R-squared:  0.918 
## F-statistic:  8542 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0196 -0.9883  0.1215  1.6400  2.8394 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3522516  0.1390049   31.31   <2e-16 ***
## x.var       0.0162762  0.0003148   51.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.919 on 762 degrees of freedom
## Multiple R-squared:  0.7782, Adjusted R-squared:  0.7779 
## F-statistic:  2673 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -459.8  -324.9  -153.9   170.7   480.9  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.050e+01  2.029e-04   51777   <2e-16 ***
## x.var       5.065e-03  3.351e-07   15116   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 377996750  on 763  degrees of freedom
## Residual deviance:  68393439  on 762  degrees of freedom
## AIC: 68402846
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Qatar", confBnd=TRUE)

## QATAR  --  355397 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37877 -16157     46  15596  46086 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5507.634   1506.073   3.657 0.000273 ***
## x.var        397.696      3.411 116.591  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20790 on 762 degrees of freedom
## Multiple R-squared:  0.9469, Adjusted R-squared:  0.9468 
## F-statistic: 1.359e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8581 -0.9479  0.4874  1.5878  2.5111 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.5166879  0.1670742   44.99   <2e-16 ***
## x.var       0.0089854  0.0003784   23.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.307 on 762 degrees of freedom
## Multiple R-squared:  0.4253, Adjusted R-squared:  0.4245 
## F-statistic: 563.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -328.17   -75.62    52.66    84.36   105.42  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.077e+01  2.480e-04   43406   <2e-16 ***
## x.var       2.696e-03  4.566e-07    5905   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 52686712  on 763  degrees of freedom
## Residual deviance: 14162372  on 762  degrees of freedom
## AIC: 14172077
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Sudan", confBnd=TRUE)

## SUDAN  --  61350 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4925.9 -2711.3    67.7  2372.2  8060.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3469.1908   218.9102  -15.85   <2e-16 ***
## x.var          74.3198     0.4958  149.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3022 on 762 degrees of freedom
## Multiple R-squared:  0.9672, Adjusted R-squared:  0.9672 
## F-statistic: 2.247e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5276 -1.1704  0.4815  1.6181  2.5198 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9949111  0.1533921   32.56   <2e-16 ***
## x.var       0.0102436  0.0003474   29.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.118 on 762 degrees of freedom
## Multiple R-squared:  0.5329, Adjusted R-squared:  0.5323 
## F-statistic: 869.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -119.902   -37.106     2.019    35.337    69.639  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.623e+00  6.723e-04   12828   <2e-16 ***
## x.var       3.276e-03  1.199e-06    2733   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 10922114  on 763  degrees of freedom
## Residual deviance:  2307030  on 762  degrees of freedom
## AIC: 2315145
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Kuwait", confBnd=TRUE)

## KUWAIT  --  616409 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -49647 -21283  -4765  20124 109051 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -71212.655   2414.900  -29.49   <2e-16 ***
## x.var          757.292      5.469  138.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33340 on 762 degrees of freedom
## Multiple R-squared:  0.9618, Adjusted R-squared:  0.9617 
## F-statistic: 1.917e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3139 -0.9678  0.4746  1.5825  2.1174 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9629834  0.1502228   46.35   <2e-16 ***
## x.var       0.0106330  0.0003402   31.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.074 on 762 degrees of freedom
## Multiple R-squared:  0.5617, Adjusted R-squared:  0.5612 
## F-statistic: 976.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -291.98  -126.27    32.86    77.91   169.52  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.042e+01  2.492e-04   41821   <2e-16 ***
## x.var       3.965e-03  4.298e-07    9227   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 119712401  on 763  degrees of freedom
## Residual deviance:  15624543  on 762  degrees of freedom
## AIC: 15634317
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# read the time series data for all the cases
all.data <- covid19.data('ts-ALL')

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:28:10 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:28:12 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:28:15 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

# run on all the cases
tots.per.location(all.data,"Libya")

## Processing confirmed cases

## LIBYA  --  489940 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53389 -31573 -18261  30737  91779 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -90957.63    2763.79  -32.91   <2e-16 ***
## x.var          640.21       6.26  102.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 38160 on 762 degrees of freedom
## Multiple R-squared:  0.9321, Adjusted R-squared:  0.932 
## F-statistic: 1.046e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7507 -1.3923  0.1198  1.8188  2.8132 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.7818585  0.1495524   25.29   <2e-16 ***
## x.var       0.0156259  0.0003387   46.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 762 degrees of freedom
## Multiple R-squared:  0.7364, Adjusted R-squared:  0.736 
## F-statistic:  2128 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -220.50  -167.35    11.74    97.51   130.65  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.413e+00  3.470e-04   27124   <2e-16 ***
## x.var       5.121e-03  5.721e-07    8951   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 122639059  on 763  degrees of freedom
## Residual deviance:  13466385  on 762  degrees of freedom
## AIC: 13475131
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing death cases

## LIBYA  --  6222 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -584.69 -421.01  -85.95  425.88 1224.63 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.234e+03  3.296e+01  -37.43   <2e-16 ***
## x.var        9.165e+00  7.465e-02  122.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 455.1 on 762 degrees of freedom
## Multiple R-squared:  0.9519, Adjusted R-squared:  0.9518 
## F-statistic: 1.507e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.34108 -1.32919  0.01945  1.28877  1.85501 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.4858960  0.0959052   15.49   <2e-16 ***
## x.var       0.0120448  0.0002172   55.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.324 on 762 degrees of freedom
## Multiple R-squared:  0.8014, Adjusted R-squared:  0.8011 
## F-statistic:  3075 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -27.8914  -21.8888    0.0592    9.2914   21.6681  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.331e+00  2.770e-03    1925   <2e-16 ***
## x.var       4.892e-03  4.603e-06    1063   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1726887  on 763  degrees of freedom
## Residual deviance:  221273  on 762  degrees of freedom
## AIC: 227181
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing recovered cases

## LIBYA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -71008 -59051 -26597  47252 137356 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23118.05    4708.26   4.910 1.11e-06 ***
## x.var          62.68      10.66   5.878 6.20e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 65010 on 762 degrees of freedom
## Multiple R-squared:  0.04338,    Adjusted R-squared:  0.04212 
## F-statistic: 34.55 on 1 and 762 DF,  p-value: 6.199e-09
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.778 -5.219 -0.369  5.521  6.700 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6.7807285  0.3722516  18.215  < 2e-16 ***
## x.var       -0.0023111  0.0008431  -2.741  0.00626 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.14 on 762 degrees of freedom
## Multiple R-squared:  0.009765,   Adjusted R-squared:  0.008465 
## F-statistic: 7.514 on 1 and 762 DF,  p-value: 0.006265
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -388.7  -341.6  -239.5   203.4   451.0  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.020e+01  3.851e-04   26483   <2e-16 ***
## x.var       1.355e-03  7.761e-07    1745   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 71995289  on 763  degrees of freedom
## Residual deviance: 68867587  on 762  degrees of freedom
## AIC: 68872987
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## [[1]]
## [[1]][[1]]
## [[1]][[1]][[1]]
## list()
## 
## [[1]][[1]][[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1   LIBYA LIBYA          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          0          0          0          0
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          0          0          0          0          0          0          0
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          0          0          0          0          0          0          0
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          0          0          0          0          0          0          0
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1          0          0          0          0          0          0          0
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1          0          0          0          0          0          0          0
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1          0          1          1          1          1          3          8
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1          8         10         10         11         11         18         18
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1         19         20         21         24         24         24         25
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1         26         35         48         49         49         49         51
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1         51         51         59         60         61         61         61
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1         61         61         61         61         63         63         63
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1         63         63         64         64         64         64         64
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1         64         64         64         64         64         65         65
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1         65         68         69         71         72         75         75
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1         75         77         99        105        118        130        156
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1        168        182        196        209        239        256        256
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1        332        359        378        393        409        418        454
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1        467        484        500        510        520        544        571
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1        595        639        670        698        713        727        762
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1        802        824        874        891        918        989       1046
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1       1117       1182       1268       1342       1342       1389       1433
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1       1512       1563       1589       1652       1704       1791       1866
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1       1980       2088       2176       2314       2424       2547       2669
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1       2827       3017       3222       3438       3621       3691       3837
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1       4063       4224       4475       4879       5079       5232       5451
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1       5929       6302       6611       7050       7327       7738       8172
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1       8579       9068       9463       9707      10121      10437      10437
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1      11009      11281      11834      12274      12629      12958      13423
##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
## 1      13966      14624      15156      15773      16445      17094      17749
##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
## 1      18834      19583      20462      20939      21908      22348      22781
##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
## 1      23515      24144      24936      25822      26438      27234      27949
##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
## 1      28796      29446      30097      30632      31290      31828      32364
##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
## 1      33213      34014      34525      35208      35717      36087      36809
##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1      37437      38468      39513      40292      41368      41686      42712
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
## 1      43821      44985      45821      46676      47845      47845      48790
##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1      49949      50906      51625      52620      53384      54374      56013
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1      57223      57975      58874      59656      60628      61095      62045
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1      62907      63688      64587      65440      66444      67039      68117
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
## 1      69040      70010      70885      71804      72628      72628      73602
##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1      74324      74936      75465      76006      76808      76808      77823
##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
## 1      78473      79180      79797      80407      81273      81273      82430
##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
## 1      82809      83417      84087      84849      85529      85529      86580
##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
## 1      87097      87986      88522      89183      89880      89880      90779
##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
## 1      91357      92017      92577      93283      93772      93772      94560
##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
## 1      95200      95706      96346      97192      97653      97653      98381
##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
## 1      98913      99350      99935     100277     100744     100744     101414
##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
## 1     101975     102456     102880     103515     104002     104002     104745
##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
## 1     105378     106030     106670     107434     108017     108017     109088
##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1     109869     110465     111124     111746     112540     112540     113688
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1     114429     115299     116064     116779     117650     117650     118631
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1     119402     120434     121243     122013     122894     122894     124026
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1     124882     125561     126028     126361     126881     126881     127354
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1     127705     128036     128348     128740     129325     129325     129797
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1     130212     130701     131262     131833     132458     132458     133338
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1     134127     134967     135585     136587     137482     137482     138640
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1     139658     140688     141598     142671     143643     143643     144993
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1     146080     147121     148175     149207     150341     150341     151605
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1     152510     153411     154320     155232     156116     156116     156849
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1     157545     158251     158957     159980     161088     161088     162294
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1     163442     164318     165287     166156     166888     166888     167825
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1     168676     169504     170045     170558     171131     171131     171880
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1     172464     173089     173683     174216     174752     174752     175286
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1     175753     176254     176701     177072     177508     177508     177871
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1     178335     178672     178927     179193     179697     179697     179970
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
## 1     180226     180692     180945     181179     181179     181179     181410
##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
## 1     181714     182012     182350     182649     182899     182899     183311
##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
## 1     183592     183932     184151     184472     184815     184815     185181
##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
## 1     185776     186072     186323     186567     186953     186953     187281
##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
## 1     187685     187928     188157     188386     188762     188762     189059
##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
## 1     189284     189555     189888     190146     190426     190426     190748
##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
## 1     191038     191253     191476     191660     192129     192129     192470
##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
## 1     192786     193238     193474     193905     194323     194323     195042
##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1     195824     196894     198142     199526     201236     201236     204090
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1     206769     209409     212013     214568     217434     217434     221495
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1     224920     226701     226701     227433     229604     229604     233449
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
## 1     236961     240309     243470     246200     249114     249114     253436
##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1     256328     258467     260951     262948     264827     264827     267846
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1     269847     271981     274453     276739     279099     279099     281930
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1     284618     286894     289219     291168     293532     293532     295254
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
## 1     296879     298773     300455     302177     303790     303790     305793
##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
## 1     307471     308972     310637     312116     313504     313504     315418
##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1     316797     318069     319568     321370     322487     322487     323930
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
## 1     325221     326370     327803     328856     329824     329824     330945
##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
## 1     332026     333064     334049     335055     335991     335991     336980
##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
## 1     337890     338576     339269     340084     341091     341091     341839
##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
## 1     342558     343240     343932     344847     345451     345451     346176
##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
## 1     346813     347364     348088     348647     349210     349210     349990
##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
## 1     350628     351224     351756     352192     352881     352881     353626
##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
## 1     354215     354866     355490     356086     356655     356655     357338
##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
## 1     357964     358463     359019     359667     360266     360266     360914
##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
## 1     361709     362318     362915     363483     364076     364076     364675
##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
## 1     365237     365830     366238     366789     367218     367218     367811
##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
## 1     368392     368987     369455     370187     370787     370787     371571
##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
## 1     372209     372636     373210     373739     374280     374280     374989
##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
## 1     375468     375869     376378     376873     377450     377450     378105
##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
## 1     378816     379328     379816     380464     381023     381023     381749
##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
## 1     382341     382884     383445     384005     384663     384663     385398
##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1     386279     386878     387543     388183     388734     388734     389650
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1     390284     390935     391633     392276     392868     392868     393447
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1     393983     394470     395069     395687     396452     396452     397319
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
## 1     398055     398940     400113     401444     403144     403144     405425
##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1     407758     410821     413066     416223     419543     419543     425237
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
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##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
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## 1     473114     475604     478488     480945     482153     482153     484445
##   2022-02-21 2022-02-22     NA     NA
## 1     486752     488567 489940 489940
## 
## 
## [[1]][[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
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##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
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##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
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##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
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##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
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##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
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##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
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##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
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##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
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##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
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##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
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##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
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##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1          3          3          3          3          3          3          3
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1          3          3          4          5          5          5          5
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1          5          5          5          5          5          5          5
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
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##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
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##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
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##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
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##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
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##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
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##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
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##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
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##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
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##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
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##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
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##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
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##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
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##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
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##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
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##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
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##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
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##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1        596        602        608        616        621        623        631
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
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##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1        732        746        765        768        774        790        795
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1        801        812        823        831        847        857        871
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1        880        893        900        907        915        920        929
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
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##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
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##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
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##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
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##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
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##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
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##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
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##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
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##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
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##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
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##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1       1698       1700       1715       1716       1737       1737       1763
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1       1782       1789       1802       1832       1842       1842       1877
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1       1883       1896       1914       1919       1936       1936       1953
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1       1974       1982       1989       2009       2014       2014       2018
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1       2042       2051       2056       2061       2088       2088       2114
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1       2116       2125       2151       2156       2174       2174       2179
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1       2210       2216       2219       2233       2236       2236       2273
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1       2288       2297       2330       2340       2348       2348       2386
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1       2402       2406       2422       2435       2487       2487       2506
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1       2513       2564       2582       2591       2602       2602       2618
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1       2653       2658       2667       2680       2684       2684       2737
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1       2757       2765       2772       2799       2807       2807       2823
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1       2828       2830       2834       2873       2882       2882       2896
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1       2908       2919       2924       2936       2947       2947       2996
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1       3005       3010       3019       3023       3029       3029       3039
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1       3047       3049       3058       3059       3063       3063       3070
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
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##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
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##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
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##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
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##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
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##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
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##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
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##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
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##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1       3213       3215       3223       3227       3232       3232       3240
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1       3243       3245       3248       3249       3253       3253       3281
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1       3299       3309       3309       3322       3344       3344       3375
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
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##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1       3579       3609       3635       3663       3689       3689       3719
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1       3750       3781       3811       3835       3869       3869       3904
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1       3933       3956       3968       4001       4017       4017       4051
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
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##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
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##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1       4343       4363       4374       4397       4410       4410       4427
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
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##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
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##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
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##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
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##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
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##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
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##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
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##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
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##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
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##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
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##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
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##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
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##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
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##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
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##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
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##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1       5665       5676       5685       5696       5710       5710       5722
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1       5727       5740       5752       5767       5778       5778       5788
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1       5796       5805       5813       5822       5828       5828       5841
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
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##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1       5936       5950       5967       5979       5993       5993       6007
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
## 1       6017       6028       6035       6044       6052       6052       6067
##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
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##   2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20
## 1       6135       6143       6156       6169       6178       6178       6191
##   2022-02-21 2022-02-22   NA   NA
## 1       6201       6210 6222 6222
## 
## 
## [[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1   LIBYA LIBYA          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
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##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
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##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          0          0          0          0          0          0          0
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          0          0          0          0          0          0          0
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          0          0          0          0          0          0          0
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1          0          0          0          0          0          0          0
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1          0          0          0          0          0          0          0
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1          0          0          0          0          0          0          0
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1          0          1          0          0          0          0          0
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1          1          1          8          8          8          8          9
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1          9          9          9         11         11         11         11
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1         15         15         15         15         18         18         18
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1         18         18         18         18         18         22         22
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1         23         23         24         24         24         24         24
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1         28         28         28         28         28         28         35
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1         35         35         35         35         38         39         39
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1         40         40         40         41         41         50         52
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1         52         52         52         52         52         52         52
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1         57         58         59         59         59         62         63
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1         70         76         78         81         83         98        103
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1        116        132        138        140        142        171        196
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1        206        209        223        224        230        258        261
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1        269        295        306        307        307        340        341
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1        367        370        373        379        380        385        418
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1        441        479        489        501        504        510        553
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1        577        579        596        604        618        619        623
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1        625        633        640        652        660        691        701
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1        724        740        778        816        848        894        933
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1        969       1003       1018       1047       1053       1085       1085
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1       1096       1112       1152       1209       1310       1333       1410
##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
## 1       1459       1676       1746       1856       1910       2025       2081
##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
## 1       2126       2247       2329       2420       2506      12100      12183
##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
## 1      12762      13252      13498      13908      14207      14679      15068
##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
## 1      15384      15913      16430      16842      17508      17832      18128
##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
## 1      18518      18902      19361      19894      20334      20889      21429
##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1      22076      22410      22831      23130      23453      23791      24038
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
## 1      24466      25007      25301      25685      26062      26062      26889
##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1      27262      27832      28440      29057      29619      29965      30731
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1      31515      32253      32962      33550      34369      35030      35853
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1      36608      36995      37610      38110      38624      39243      40119
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
## 1      40780      41512      42098      42703      43259      43259      44133
##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1      44733      45371      46127      46793      47587      47587      48914
##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
## 1      49592      50304      50914      51585      52299      52299      53266
##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
## 1      53818      54209      54712      55304      56048      56048      56702
##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
## 1      57380      57837      58578      59222      59839      59839      60895
##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
## 1      61453      62144      62720      63231      63886      63886      64810
##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
## 1      65532      66076      66756      67661      68289      68289      68990
##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
## 1      69763      70412      71273      72107      73252      73252      74381
##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
## 1      75288      76244      77435      78268      79193      79193      80292
##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
## 1      81237      82229      83311      84245      85068      85068      86125
##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1      87197      88062      88930      89909      90952      90952      92250
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1      93342      94287      95406      96293      97357      97357      98706
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1      99586     100593     101348     102448     103312     103312     104539
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1     105422     106377     107386     108434     109262     109262     110143
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1     110965     111864     112731     113564     114305     114305     115357
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1     116120     117017     117991     118791     119492     119492     120240
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1     121116     122079     123003     123777     124712     124712     125842
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1     126878     128002     128928     129706     130625     130625     131778
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1     132697     133768     134923     136158     137073     137073     138312
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1     139361     140420     141413     142633     143697     143697     144964
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1     145882     146480     147147     147864     148288     148288     149238
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1     149836     150478     151189     151853     152328     152328     152921
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1     153644     154220     155117     155712     156344     156344     157010
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1     157658     158352     158891     159630     160113     160113     160643
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1     161160     161676     162188     162672     163191     163191     163716
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1     164196     164663     165058     165504     165931     165931     166340
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
## 1     166680     167043     167322     167629     167629     167629     168128
##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
## 1     168378     168785     169070     169364     169733     169733     169945
##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
## 1     170195     170489     170768     171006     171279     171279     171637
##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
## 1     171874     172117     172420     172637     172916     172916     173189
##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
## 1     173394     173636     173877     174223     174434     174434     174666
##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
## 1     174976     175226     175471     175687     175923     175923     176217
##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
## 1     176450     176649     176869     177106     177358     177358     177600
##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
## 1     177882     178191     178387     178621     178871     178871     179095
##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1     179398     179685     179970     180204     180484     180484     180860
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1     181203     181572     181987     182359     182785     182785     183436
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1     184053     184476     184476     184852     185468     185468     186355
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
## 1     187221     187960     188973     189964     190978     190978     192184
##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1     193144     194217     195639          0          0          0          0
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1          0          0          0          0          0          0          0
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1          0          0          0          0          0          0          0
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
## 1          0          0          0          0          0          0          0
##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
## 1          0          0          0          0          0          0          0
##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1          0          0          0          0          0          0          0
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
## 1          0          0          0          0          0          0          0
##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
## 1          0          0          0          0          0          0          0
##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
## 1          0          0          0          0          0          0          0
##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
## 1          0          0          0          0          0          0          0
##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
## 1          0          0          0          0          0          0          0
##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
## 1          0          0          0          0          0          0          0
##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
## 1          0          0          0          0          0          0          0
##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
## 1          0          0          0          0          0          0          0
##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
## 1          0          0          0          0          0          0          0
##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
## 1          0          0          0          0          0          0          0
##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
## 1          0          0          0          0          0          0          0
##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
## 1          0          0          0          0          0          0          0
##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
## 1          0          0          0          0          0          0          0
##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
## 1          0          0          0          0          0          0          0
##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
## 1          0          0          0          0          0          0          0
##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1          0          0          0          0          0          0          0
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1          0          0          0          0          0          0          0
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1          0          0          0          0          0          0          0
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
## 1          0          0          0          0          0          0          0
##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1          0          0          0          0          0          0          0
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
## 1          0          0          0          0          0          0          0
##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
## 1          0          0          0          0          0          0          0
##   2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20
## 1          0          0          0          0          0          0          0
##   2022-02-21 2022-02-22 NA NA
## 1          0          0  0  0

# read the time series data for all the cases
all.data <- covid19.data('ts-ALL')

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:28:20 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:28:21 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:28:23 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

# run on all the cases
tots.per.location(all.data,"United Arab Emirates")

## Processing confirmed cases

## UNITED ARAB EMIRATES  --  876624 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -109043  -50854    4140   52349  149017 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -150368.48    4808.54  -31.27   <2e-16 ***
## x.var          1351.68      10.89  124.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66390 on 762 degrees of freedom
## Multiple R-squared:  0.9529, Adjusted R-squared:  0.9528 
## F-statistic: 1.54e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4333 -0.8355  0.7090  1.3195  1.7317 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.3576941  0.1347218   54.61   <2e-16 ***
## x.var       0.0108042  0.0003051   35.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 762 degrees of freedom
## Multiple R-squared:  0.622,  Adjusted R-squared:  0.6215 
## F-statistic:  1254 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -353.12  -204.42   -73.22   183.61   277.25  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.075e+01  2.014e-04   53380   <2e-16 ***
## x.var       4.305e-03  3.423e-07   12578   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 234133452  on 763  degrees of freedom
## Residual deviance:  34657229  on 762  degrees of freedom
## AIC: 34667399
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing death cases

## UNITED ARAB EMIRATES  --  2298 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -300.7 -165.7   33.5  162.2  288.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -291.75151   12.36628  -23.59   <2e-16 ***
## x.var          3.64965    0.02801  130.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 170.7 on 762 degrees of freedom
## Multiple R-squared:  0.9571, Adjusted R-squared:  0.957 
## F-statistic: 1.698e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6049 -0.6012  0.5042  0.8253  1.4077 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.1519276  0.0923262   34.14   <2e-16 ***
## x.var       0.0078107  0.0002091   37.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.275 on 762 degrees of freedom
## Multiple R-squared:  0.6468, Adjusted R-squared:  0.6463 
## F-statistic:  1395 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.895   -6.372   -2.111    7.679   15.402  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.263e+00  3.395e-03  1550.4   <2e-16 ***
## x.var       3.728e-03  5.919e-06   629.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 563982  on 763  degrees of freedom
## Residual deviance:  88814  on 762  degrees of freedom
## AIC: 94801
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing recovered cases

## UNITED ARAB EMIRATES  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -224215 -189885  -69927  102926  476449 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 86714.72   14929.11   5.808 9.26e-09 ***
## x.var         179.97      33.81   5.323 1.35e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 206100 on 762 degrees of freedom
## Multiple R-squared:  0.03585,    Adjusted R-squared:  0.03458 
## F-statistic: 28.33 on 1 and 762 DF,  p-value: 1.346e-07
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.729  -5.156   1.541   4.762   7.282 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.7368053  0.3811539  28.169   <2e-16 ***
## x.var       -0.0082227  0.0008633  -9.525   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.262 on 762 degrees of freedom
## Multiple R-squared:  0.1064, Adjusted R-squared:  0.1052 
## F-statistic: 90.73 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -686.1  -613.5  -228.9   253.7   858.4  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.147e+01  2.075e-04   55286   <2e-16 ***
## x.var       1.172e-03  4.242e-07    2763   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 208136554  on 763  degrees of freedom
## Residual deviance: 200347322  on 762  degrees of freedom
## AIC: 200354112
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## [[1]]
## [[1]][[1]]
## [[1]][[1]][[1]]
## list()
## 
## [[1]][[1]][[2]]
##                geo.loc                 Long 2020-01-22 2020-01-23 2020-01-24
## 1 UNITED ARAB EMIRATES UNITED ARAB EMIRATES          0          0          0
##   2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31
## 1          0          0          0          0          4          4          4
##   2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07
## 1          4          5          5          5          5          5          5
##   2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14
## 1          7          7          8          8          8          8          8
##   2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21
## 1          8          9          9          9          9          9          9
##   2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28
## 1         13         13         13         13         13         13         19
##   2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06
## 1         21         21         21         27         27         29         29
##   2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13
## 1         45         45         45         74         74         85         85
##   2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20
## 1         85         98         98         98        113        140        140
##   2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27
## 1        153        153        198        248        333        333        405
##   2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03
## 1        468        570        611        664        814       1024       1264
##   2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10
## 1       1505       1799       2076       2359       2659       2990       3360
##   2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17
## 1       3736       4123       4521       4933       5365       5825       6302
##   2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24
## 1       6302       6781       7265       7755       8238       8756       9281
##   2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01
## 1       9813      10349      10839      11380      11929      12481      13038
##   2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08
## 1      13599      14163      14730      15192      15738      16240      16793
##   2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15
## 1      17417      18198      18878      19661      20386      21084      21831
##   2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22
## 1      22627      23358      24190      25063      26004      26898      27892
##   2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29
## 1      28704      29485      30307      31086      31969      32532      33170
##   2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05
## 1      33896      34557      35192      35788      36359      37018      37642
##   2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12
## 1      38268      38808      39376      39904      40507      40986      41499
##   2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19
## 1      41990      42294      42636      42982      43364      43752      44145
##   2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26
## 1      44533      44925      45303      45683      46133      46563      46973
##   2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03
## 1      47360      47797      48246      48667      49069      49469      50141
##   2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10
## 1      50857      51540      52068      52600      53045      53577      54050
##   2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17
## 1      54453      54854      55198      55573      55848      56129      56422
##   2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24
## 1      56711      56922      57193      57498      57734      57988      58249
##   2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31
## 1      58562      58913      59177      59546      59921      60223      60506
##   2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07
## 1      60760      60999      61163      61352      61606      61845      62061
##   2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14
## 1      62300      62525      62704      62966      63212      63489      63819
##   2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21
## 1      64102      64312      64541      64906      65341      65802      66193
##   2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28
## 1      66617      67007      67282      67621      68020      68511      68901
##   2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04
## 1      69328      69690      70231      70805      71540      72154      72766
##   2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11
## 1      73471      73984      74454      75098      75981      76911      77842
##   2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18
## 1      78849      79489      80266      80940      81782      82568      83433
##   2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25
## 1      84242      84916      85595      86447      87530      88532      89540
##   2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02
## 1      90618      91469      92095      93090      94190      95348      96529
##   2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09
## 1      97760      98801      99733     100794     101840     102929     104004
##   2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16
## 1     105133     106229     107293     108608     110039     111437     112849
##   2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23
## 1     114387     115602     116517     117594     119132     120710     122273
##   2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30
## 1     123764     125123     126234     127624     129024     130336     131508
##   2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06
## 1     132629     133907     135141     136149     137310     138599     139891
##   2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13
## 1     141032     142143     143289     144385     145599     146735     147961
##   2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20
## 1     149135     150345     151554     152809     154101     155254     156523
##   2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27
## 1     157785     158990     160055     161365     162662     163967     165250
##   2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04
## 1     166502     167753     168860     170149     171434     172751     174062
##   2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11
## 1     175276     176429     177577     178837     180150     181405     182601
##   2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18
## 1     183755     184949     186041     187267     188545     189866     191150
##   2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25
## 1     192404     193575     194652     195878     197124     198435     199665
##   2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01
## 1     200892     201836     202863     204369     206092     207822     209678
##   2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08
## 1     211641     213231     214732     216699     218766     221754     224704
##   2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15
## 1     227702     230578     232982     236225     239587     242969     246376
##   2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22
## 1     249808     253261     256732     260223     263729     267258     270810
##   2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29
## 1     274376     277955     281546     285147     289086     293052     297014
##   2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05
## 1     300661     303609     306339     309649     313626     316875     320126
##   2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12
## 1     323402     326495     329293     332603     336142     339667     342974
##   2021-02-13 2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19
## 1     345605     348772     351895     355131     358583     361877     365017
##   2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26
## 1     368175     370425     372530     375535     378637     381662     385160
##   2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05
## 1     388594     391524     394050     396771     399463     402205     405277
##   2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12
## 1     408236     410849     413332     415705     417909     419996     422246
##   2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19
## 1     424405     426397     428295     430313     432364     434465     436625
##   2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26
## 1     438638     440355     442226     444398     446594     448637     450765
##   2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02
## 1     453069     455197     457071     459360     461444     463759     465939
##   2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09
## 1     468023     470136     472148     474136     476019     478131     480006
##   2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16
## 1     481937     483747     485675     487697     489495     491423     493266
##   2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23
## 1     495224     497154     498957     500860     502791     504872     506845
##   2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30
## 1     508925     510738     512497     514591     516301     518262     520236
##   2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07
## 1     521948     523795     525567     527266     529220     530944     532710
##   2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14
## 1     534445     536017     537524     539138     540646     542158     543610
##   2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21
## 1     544931     546182     547411     548681     550029     551430     552920
##   2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28
## 1     554516     556107     557619     559291     561048     563215     565451
##   2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04
## 1     567263     569073     570836     572804     574958     576947     579009
##   2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11
## 1     581197     583071     585039     587244     589423     589423     593894
##   2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18
## 1     596017     597986     599823     601950     603961     606128     608070
##   2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25
## 1     610179     612029     613993     616160     618148     620309     622532
##   2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02
## 1     624814     626936     628976     631160     632907     634582     636245
##   2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09
## 1     637877     639476     641049     642601     644114     645653     647182
##   2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16
## 1     648702     650220     651762     653284     654813     656354     657884
##   2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23
## 1     659449     660978     662486     664027     665533     667080     668601
##   2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30
## 1     670108     671636     673185     674724     676251     677801     679321
##   2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06
## 1     680858     682377     683914     685462     686981     688489     690009
##   2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13
## 1     691554     692964     694285     695619     696906     698166     699381
##   2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20
## 1     700587     701776     702885     704000     705089     706166     707236
##   2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27
## 1     708302     709378     710438     711428     712411     713402     714396
##   2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03
## 1     715394     716381     717374     718370     719355     720330     721308
##   2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10
## 1     722292     723263     724240     725192     726025     726797     727541
##   2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17
## 1     728266     728886     729518     730135     730743     731307     731828
##   2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24
## 1     732299     732690     733003     733325     733643     733972     734275
##   2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01
## 1     734596     734894     735180     735457     735727     735992     736268
##   2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08
## 1     736524     736708     736897     737073     737229     737373     737509
##   2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15
## 1     737655     737766     737890     738026     738152     738268     738372
##   2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22
## 1     738487     738586     738690     738812     738924     739018     739106
##   2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29
## 1     739190     739284     739381     739471     739566     739654     739736
##   2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05
## 1     739824     739905     739983     740057     740136     740209     740289
##   2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12
## 1     740362     740432     740500     740572     740647     740729     740801
##   2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19
## 1     740879     740945     741006     741074     741148     741214     741291
##   2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26
## 1     741370     741433     741500     741570     741643     741720     741790
##   2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03
## 1     741858     741918     741976     742041     742109     742109     742163
##   2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10
## 1     742214     742328     742376     742438     742507     742567     742641
##   2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17
## 1     742719     742802     742894     743004     743152     743352     743586
##   2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24
## 1     743586     744137     744438     744890     745555     746557     747909
##   2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31
## 1     749530     751333     753065     754911     757145     759511     761937
##   2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07
## 1     764493     767093     769608     772189     774897     777584     780211
##   2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14
## 1     782866     785625     788187     790698     793314     795997     799065
##   2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21
## 1     802181     805248     808237     811029     813931     816945     819866
##   2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28
## 1     822886     825699     828328     830832     833201     835839     838384
##   2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04
## 1     840739     843030     845058     847142     849305     851537     853651
##   2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11
## 1     855642     857657     859361     860976     862514     864102     865576
##   2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18
## 1     866971     868237     869428     870358     871315     872210     873092
##   2022-02-19 2022-02-20 2022-02-21 2022-02-22     NA     NA
## 1     873882     874607     875258     875884 876624 876624
## 
## 
## [[1]][[2]]
##                geo.loc                 Long 2020-01-22 2020-01-23 2020-01-24
## 1 UNITED ARAB EMIRATES UNITED ARAB EMIRATES          0          0          0
##   2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31
## 1          0          0          0          0          0          0          0
##   2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07
## 1          0          0          0          0          0          0          0
##   2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14
## 1          0          0          0          0          0          0          0
##   2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21
## 1          0          0          0          0          0          0          0
##   2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28
## 1          0          0          0          0          0          0          0
##   2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06
## 1          0          0          0          0          0          0          0
##   2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13
## 1          0          0          0          0          0          0          0
##   2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20
## 1          0          0          0          0          0          0          2
##   2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27
## 1          2          2          2          2          2          2          2
##   2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03
## 1          2          3          5          6          8          8          9
##   2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10
## 1         10         10         11         12         12         14         16
##   2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17
## 1         20         22         25         28         33         35         37
##   2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24
## 1         37         41         43         46         52         56         64
##   2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01
## 1         71         76         82         89         98        105        111
##   2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08
## 1        119        126        137        146        157        165        174
##   2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15
## 1        185        198        201        203        206        208        210
##   2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22
## 1        214        220        224        227        233        237        241
##   2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29
## 1        244        245        248        253        255        258        260
##   2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05
## 1        262        264        266        269        270        273        274
##   2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12
## 1        275        276        281        283        284        286        287
##   2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19
## 1        288        289        291        293        295        298        300
##   2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26
## 1        301        302        303        305        307        308        310
##   2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03
## 1        311        313        314        315        316        317        318
##   2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10
## 1        321        323        324        326        327        328        330
##   2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17
## 1        331        333        334        335        335        335        337
##   2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24
## 1        338        339        340        341        342        342        343
##   2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31
## 1        343        344        345        347        347        349        351
##   2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07
## 1        351        351        351        351        353        354        356
##   2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14
## 1        356        357        357        358        358        358        359
##   2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21
## 1        361        364        364        366        367        369        370
##   2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28
## 1        372        375        376        377        378        378        379
##   2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04
## 1        379        382        384        384        387        387        387
##   2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11
## 1        388        388        390        391        393        398        398
##   2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18
## 1        399        399        399        401        402        402        403
##   2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25
## 1        404        404        405        405        406        407        409
##   2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02
## 1        411        412        413        416        419        421        424
##   2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09
## 1        426        426        429        435        436        438        442
##   2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16
## 1        443        445        446        448        450        452        455
##   2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23
## 1        459        463        466        470        472        474        475
##   2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30
## 1        475        477        480        482        485        488        490
##   2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06
## 1        495        496        497        503        505        508        510
##   2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13
## 1        514        514        515        518        520        523        528
##   2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20
## 1        528        530        534        538        542        544        547
##   2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27
## 1        548        552        554        559        563        564        567
##   2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04
## 1        569        570        572        576        580        585        586
##   2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11
## 1        589        592        594        596        598        602        607
##   2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18
## 1        609        617        618        622        626        629        630
##   2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25
## 1        634        637        639        642        645        647        653
##   2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01
## 1        655        657        660        662        665        669        671
##   2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08
## 1        674        679        682        685        689        694        697
##   2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15
## 1        702        708        711        717        723        726        733
##   2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22
## 1        740        745        751        756        762        766        776
##   2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29
## 1        783        792        798        805        811        819        826
##   2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05
## 1        838        850        859        866        878        888        902
##   2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12
## 1        914        921        930        947        956        974        986
##   2021-02-13 2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19
## 1       1001       1014       1027       1041       1055       1073       1093
##   2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26
## 1       1108       1125       1140       1145       1164       1182       1198
##   2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05
## 1       1213       1221       1238       1253       1269       1286       1296
##   2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12
## 1       1310       1322       1335       1345       1353       1369       1378
##   2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19
## 1       1388       1395       1402       1406       1414       1424       1428
##   2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26
## 1       1433       1438       1445       1451       1456       1466       1472
##   2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02
## 1       1477       1481       1486       1492       1497       1499       1502
##   2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09
## 1       1504       1510       1512       1516       1520       1523       1526
##   2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16
## 1       1529       1531       1533       1537       1541       1545       1547
##   2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23
## 1       1550       1554       1556       1559       1561       1565       1567
##   2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30
## 1       1569       1571       1573       1578       1580       1584       1587
##   2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07
## 1       1591       1593       1596       1598       1601       1604       1607
##   2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14
## 1       1610       1613       1615       1617       1619       1623       1626
##   2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21
## 1       1629       1631       1633       1637       1639       1642       1644
##   2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28
## 1       1648       1651       1654       1658       1661       1664       1668
##   2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04
## 1       1673       1677       1680       1684       1686       1689       1691
##   2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11
## 1       1696       1699       1702       1704       1710       1710       1720
##   2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18
## 1       1724       1726       1730       1734       1738       1741       1747
##   2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25
## 1       1752       1757       1763       1767       1773       1775       1782
##   2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02
## 1       1792       1796       1802       1807       1811       1819       1825
##   2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09
## 1       1831       1834       1839       1843       1847       1849       1853
##   2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16
## 1       1860       1866       1870       1876       1880       1885       1892
##   2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23
## 1       1896       1898       1900       1904       1907       1910       1913
##   2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30
## 1       1916       1920       1927       1929       1934       1939       1943
##   2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06
## 1       1949       1951       1956       1960       1965       1967       1969
##   2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13
## 1       1971       1975       1978       1982       1988       1992       1994
##   2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20
## 1       1997       2001       2003       2006       2009       2012       2014
##   2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27
## 1       2018       2020       2024       2026       2028       2031       2035
##   2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03
## 1       2036       2038       2039       2041       2043       2043       2044
##   2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10
## 1       2045       2046       2048       2050       2053       2057       2060
##   2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17
## 1       2062       2062       2064       2066       2068       2069       2071
##   2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24
## 1       2073       2075       2077       2078       2080       2083       2086
##   2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01
## 1       2089       2090       2094       2094       2095       2097       2100
##   2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08
## 1       2100       2102       2103       2104       2107       2109       2111
##   2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15
## 1       2113       2113       2114       2115       2116       2117       2118
##   2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22
## 1       2118       2120       2120       2122       2124       2126       2128
##   2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29
## 1       2129       2130       2131       2134       2135       2135       2136
##   2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05
## 1       2136       2136       2136       2137       2137       2137       2138
##   2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12
## 1       2139       2140       2142       2142       2142       2142       2142
##   2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19
## 1       2143       2143       2144       2144       2144       2144       2144
##   2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26
## 1       2144       2144       2144       2144       2145       2145       2145
##   2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03
## 1       2145       2146       2146       2147       2148       2148       2148
##   2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10
## 1       2148       2148       2149       2149       2149       2149       2151
##   2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17
## 1       2151       2151       2151       2151       2151       2151       2151
##   2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24
## 1       2151       2151       2152       2154       2154       2154       2155
##   2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31
## 1       2156       2158       2159       2160       2160       2162       2164
##   2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07
## 1       2165       2168       2169       2170       2170       2170       2170
##   2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14
## 1       2173       2174       2174       2177       2181       2182       2185
##   2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21
## 1       2188       2191       2195       2198       2200       2204       2207
##   2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28
## 1       2211       2214       2219       2224       2228       2232       2234
##   2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04
## 1       2239       2240       2243       2248       2251       2253       2258
##   2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11
## 1       2262       2264       2265       2269       2273       2278       2283
##   2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18
## 1       2284       2285       2287       2288       2289       2290       2292
##   2022-02-19 2022-02-20 2022-02-21 2022-02-22   NA   NA
## 1       2293       2294       2296       2297 2298 2298
## 
## 
## [[2]]
##                geo.loc                 Long 2020-01-22 2020-01-23 2020-01-24
## 1 UNITED ARAB EMIRATES UNITED ARAB EMIRATES          0          0          0
##   2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31
## 1          0          0          0          0          0          0          0
##   2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07
## 1          0          0          0          0          0          0          0
##   2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14
## 1          0          0          0          0          1          1          1
##   2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21
## 1          3          4          4          4          4          4          4
##   2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28
## 1          4          4          4          4          4          4          5
##   2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06
## 1          5          5          5          5          5          5          5
##   2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13
## 1          7          7          7         12         17         17         17
##   2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20
## 1         17         23         23         23         26         31         31
##   2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27
## 1         38         38         38         45         52         52         52
##   2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03
## 1         52         58         61         61         61         96        108
##   2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10
## 1        125        144        167        186        239        268        418
##   2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17
## 1        588        680        852        933       1034       1095       1188
##   2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24
## 1       1188       1286       1360       1443       1546       1637       1760
##   2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01
## 1       1887       1978       2090       2181       2329       2429       2543
##   2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08
## 1       2664       2763       2966       3153       3359       3572       3837
##   2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15
## 1       4295       4804       5381       6012       6523       6930       7328
##   2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22
## 1       7931       8512       9577      10791      11809      12755      13798
##   2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29
## 1      14495      15056      15657      15982      16371      16685      17097
##   2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05
## 1      17546      17932      18338      18726      19153      19572      20337
##   2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12
## 1      21061      21806      22275      22740      24017      25234      25946
##   2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19
## 1      26761      27462      28129      28861      29537      30241      30996
##   2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26
## 1      31754      32415      33046      33703      34405      35165      35469
##   2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03
## 1      35834      36411      37076      37566      38160      38664      39153
##   2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10
## 1      39857      40297      40721      41714      42282      43570      43969
##   2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17
## 1      44648      45140      45513      46025      46418      47412      48448
##   2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24
## 1      48917      49269      49621      49964      50354      50848      51235
##   2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31
## 1      51628      52182      52510      52905      53202      53626      53909
##   2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07
## 1      54255      54615      54863      55090      55385      55739      56015
##   2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14
## 1      56245      56568      56766      56961      57193      57372      57473
##   2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21
## 1      57571      57694      57794      57909      58022      58153      58296
##   2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28
## 1      58408      58488      58582      58754      59070      59472      59861
##   2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04
## 1      60202      60600      60931      61491      62029      62668      63158
##   2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11
## 1      63652      66095      66533      66943      67359      67945      68462
##   2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18
## 1      68983      69451      69981      70635      71456      72117      72790
##   2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25
## 1      73512      74273      75086      76025      76995      77937      78819
##   2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02
## 1      79676      80544      81462      82538      83724      84903      86071
##   2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09
## 1      87122      88123      89410      90556      91710      93479      94903
##   2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16
## 1      95973      97284      98555     100007     101659     103325     104943
##   2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23
## 1     106354     107516     108811     110313     111814     113364     115068
##   2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30
## 1     116894     118931     120750     122458     124647     126147     127607
##   2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06
## 1     128902     130508     132024     133490     134983     136118     136936
##   2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13
## 1     137608     138291     138959     139701     140442     141215     141883
##   2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20
## 1     142561     143252     143932     144647     145537     146469     147309
##   2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27
## 1     148080     148871     149578     150261     151044     151870     152708
##   2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04
## 1     153449     154185     154899     155667     156380     157035     157828
##   2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11
## 1     158498     159132     159711     160295     161084     161741     162435
##   2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18
## 1     163048     163679     164349     165023     165749     166541     167306
##   2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25
## 1     168129     168995     169840     171451     172984     174479     175865
##   2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01
## 1     177407     178672     179925     181400     183007     184442     186019
##   2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08
## 1     188100     189709     191455     193321     195520     199178     201396
##   2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15
## 1     203660     206114     208366     210561     213149     215820     218988
##   2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22
## 1     222106     225374     228364     231675     235421     239322     243267
##   2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29
## 1     247318     251484     255304     259194     263730     267024     269999
##   2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05
## 1     272769     276958     281410     285201     289276     293180     297040
##   2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12
## 1     301081     305759     309692     313060     316053     319787     323191
##   2021-02-13 2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19
## 1     326780     331839     336731     340365     343935     347366     351715
##   2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26
## 1     356013     359697     363052     366567     370381     375059     377537
##   2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05
## 1     379708     381225     382332     383998     385587     387278     389304
##   2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12
## 1     391205     392792     394649     396433     398126     399803     401539
##   2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19
## 1     403478     405647     408085     410736     413477     416105     418496
##   2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26
## 1     420736     422696     424840     427188     429573     431773     434035
##   2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02
## 1     436463     438706     440731     443153     445355     447790     450111
##   2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09
## 1     452321     454600     456747     458885     460841     463032     464971
##   2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16
## 1     466804     468456     470175     471906     473398     475012     476518
##   2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23
## 1     478063     479566     481326     483180     485078     486920     488664
##   2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30
## 1     490457     492109     493689     495589     497140     498943     500779
##   2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07
## 1     502460     504251     506020     507706     509658     511340     513068
##   2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14
## 1     514769     516329     517805     519405     520882     522356     523778
##   2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21
## 1     525080     526302     527519     528769     530085     531459     532910
##   2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28
## 1     534481     536050     537531     539161     540886     543023     545229
##   2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04
## 1     547008     548785     550525     552479     554589     556549     558584
##   2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11
## 1     560734     562576     564509     566677     568828     568828     573194
##   2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18
## 1     575288     577234     579045     581139     583115     585242     587160
##   2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25
## 1     589235     591061     592984     595086     597008     599131     601308
##   2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02
## 1     603541     605618     607606     609711     611442     612998     614636
##   2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09
## 1     616197     617767     619294     620812     622301     623826     625332
##   2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16
## 1     626800     628290     629809     631294     632775     634272     635759
##   2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23
## 1     637267     638771     640248     641750     643234     644753     646227
##   2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30
## 1     647682     649173     650683     652180     653675     655183     656680
##   2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06
## 1     658198     659664     661156     662660     664130          0          0
##   2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13
## 1          0          0          0          0          0          0          0
##   2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20
## 1          0          0          0          0          0          0          0
##   2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27
## 1          0          0          0          0          0          0          0
##   2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03
## 1          0          0          0          0          0          0          0
##   2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10
## 1          0          0          0          0          0          0          0
##   2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17
## 1          0          0          0          0          0          0          0
##   2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24
## 1          0          0          0          0          0          0          0
##   2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01
## 1          0          0          0          0          0          0          0
##   2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08
## 1          0          0          0          0          0          0          0
##   2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15
## 1          0          0          0          0          0          0          0
##   2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22
## 1          0          0          0          0          0          0          0
##   2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29
## 1          0          0          0          0          0          0          0
##   2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05
## 1          0          0          0          0          0          0          0
##   2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12
## 1          0          0          0          0          0          0          0
##   2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19
## 1          0          0          0          0          0          0          0
##   2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26
## 1          0          0          0          0          0          0          0
##   2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03
## 1          0          0          0          0          0          0          0
##   2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10
## 1          0          0          0          0          0          0          0
##   2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17
## 1          0          0          0          0          0          0          0
##   2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24
## 1          0          0          0          0          0          0          0
##   2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31
## 1          0          0          0          0          0          0          0
##   2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07
## 1          0          0          0          0          0          0          0
##   2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14
## 1          0          0          0          0          0          0          0
##   2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21
## 1          0          0          0          0          0          0          0
##   2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28
## 1          0          0          0          0          0          0          0
##   2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04
## 1          0          0          0          0          0          0          0
##   2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11
## 1          0          0          0          0          0          0          0
##   2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18
## 1          0          0          0          0          0          0          0
##   2022-02-19 2022-02-20 2022-02-21 2022-02-22 NA NA
## 1          0          0          0          0  0  0

# read the time series data for all the cases
all.data <- covid19.data('ts-ALL')

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:28:28 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:28:29 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:28:31 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

# run on all the cases
tots.per.location(all.data,"Egypt")

## Processing confirmed cases

## EGYPT  --  475341 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -29380 -13550  -3333  11883  67994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -41302.596   1292.543  -31.95   <2e-16 ***
## x.var          587.238      2.927  200.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17850 on 762 degrees of freedom
## Multiple R-squared:  0.9814, Adjusted R-squared:  0.9814 
## F-statistic: 4.024e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3213 -0.9065  0.4730  1.3987  2.4846 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0892644  0.1566383   45.26   <2e-16 ***
## x.var       0.0100880  0.0003548   28.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.163 on 762 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.5142 
## F-statistic: 808.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -287.37   -85.11    29.19    85.02   122.62  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.045e+01  2.584e-04   40449   <2e-16 ***
## x.var       3.584e-03  4.537e-07    7900   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86357718  on 763  degrees of freedom
## Residual deviance: 12540366  on 762  degrees of freedom
## AIC: 12550086
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing death cases

## EGYPT  --  23889 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1617.33  -659.42    12.67   579.52  2325.69 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2358.8457    58.5805  -40.27   <2e-16 ***
## x.var          33.1528     0.1327  249.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 808.8 on 762 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9879 
## F-statistic: 6.244e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1119 -0.8092  0.4447  1.1462  2.0326 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.690584   0.124532   37.67   <2e-16 ***
## x.var       0.009160   0.000282   32.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.719 on 762 degrees of freedom
## Multiple R-squared:  0.5806, Adjusted R-squared:   0.58 
## F-statistic:  1055 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -68.511  -25.348    8.246   17.654   31.614  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.570e+00  1.091e-03    6940   <2e-16 ***
## x.var       3.596e-03  1.914e-06    1879   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4952882  on 763  degrees of freedom
## Residual deviance:  773248  on 762  degrees of freedom
## AIC: 780827
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing recovered cases

## EGYPT  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -81092 -74351 -32312  59932 158261 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 54092.98    5390.76  10.034  < 2e-16 ***
## x.var          35.34      12.21   2.894  0.00391 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 74430 on 762 degrees of freedom
## Multiple R-squared:  0.01088,    Adjusted R-squared:  0.009577 
## F-statistic: 8.378 on 1 and 762 DF,  p-value: 0.003907
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.158  -4.766   1.492   4.502   6.607 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.1662402  0.3622580  28.064   <2e-16 ***
## x.var       -0.0078751  0.0008205  -9.598   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.002 on 762 degrees of freedom
## Multiple R-squared:  0.1079, Adjusted R-squared:  0.1067 
## F-statistic: 92.13 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -405.0  -385.3  -146.8   207.5   464.4  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.091e+01  2.935e-04 37191.7   <2e-16 ***
## x.var       5.241e-04  6.334e-07   827.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 73497472  on 763  degrees of freedom
## Residual deviance: 72810116  on 762  degrees of freedom
## AIC: 72816542
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## [[1]]
## [[1]][[1]]
## [[1]][[1]][[1]]
## list()
## 
## [[1]][[1]][[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1   EGYPT EGYPT          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          0          1          1          1
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          1          1          1          1          1          1          1
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          1          1          1          1          1          1          2
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          2          2          2          3         15         15         49
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1         55         59         60         67         80        109        110
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1        150        196        196        256        285        294        327
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1        366        402        456        495        536        576        609
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1        656        710        779        865        985       1070       1173
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1       1322       1450       1560       1699       1794       1939       2065
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1       2190       2350       2505       2673       2844       3032       3144
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1       3333       3490       3659       3891       4092       4319       4534
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1       4782       5042       5268       5537       5895       6193       6465
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1       6813       7201       7588       7981       8476       8964       9400
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1       9746      10093      10431      10829      11228      11719      12229
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1      12764      13484      14229      15003      15786      16513      17265
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1      17967      18756      19666      20793      22082      23449      24985
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1      26384      27536      28615      29767      31115      32612      34079
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1      35444      36829      38284      39726      41303      42980      44598
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1      46289      47856      49219      50437      52211      53758      55233
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1      56809      58141      59561      61130      62755      63923      65188
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1      66754      68311      69814      71299      72711      74035      75253
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1      76222      77279      78304      79254      80235      81158      82070
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1      83001      83930      84843      85771      86474      87172      87775
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1      88402      89078      89745      90413      91072      91583      92062
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1      92482      92947      93356      93757      94078      94316      94483
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1      94640      94752      94875      95006      95147      95314      95492
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1      95666      95834      95963      96108      96220      96336      96475
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1      96590      96753      96914      97025      97148      97237      97340
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1      97478      97619      97825      98062      98285      98497      98727
##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
## 1      98939      99115      99280      99425      99582      99712      99863
##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
## 1     100041     100228     100403     100557     100708     100856     101009
##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
## 1     101177     101340     101500     101641     101772     101900     102015
##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
## 1     102141     102254     102375     102513     102625     102736     102840
##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
## 1     102955     103079     103198     103317     103466     103575     103683
##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1     103781     103902     104035     104156     104262     104387     104516
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
## 1     104648     104787     104915     105033     105159     105297     105424
##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1     105547     105705     105883     106060     106230     106397     106540
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1     106707     106877     107030     107209     107376     107555     107736
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1     107925     108122     108329     108530     108754     108962     109201
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
## 1     109422     109654     109881     110095     110319     110547     110767
##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1     111009     111284     111613     111955     112318     112676     113027
##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
## 1     113381     113742     114107     114475     114832     115183     115541
##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
## 1     115911     116303     116724     117156     117583     118014     118432
##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
## 1     118847     119281     119702     120147     120611     121089     121575
##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
## 1     122086     122609     123153     123701     124280     124891     125555
##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
## 1     126273     127061     127972     128993     130126     131315     132541
##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
## 1     133900     135233     136644     138062     139471     140878     142187
##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
## 1     143464     144583     145590     146809     147810     148799     149792
##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
## 1     150753     151723     152719     153741     154620     155507     156397
##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1     157275     158174     158963     159715     160463     161143     161817
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1     162486     163129     163761     164282     164871     165418     165951
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1     166492     167013     167525     168057     168597     169106     169640
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1     170207     170780     171390     171993     172602     173202     173813
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1     174426     175059     175677     176333     176943     177543     178151
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1     178774     179407     180051     180640     181241     181829     182424
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1     183010     183591     184168     184755     185334     185922     186503
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1     187094     187716     188361     189000     189639     190280     190924
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1     191555     192195     192840     193482     194127     194771     195418
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1     196061     196709     197350     198011     198681     199364     200050
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1     200739     201432     202131     202843     203546     204256     204965
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1     205732     206510     207293     208082     208876     209677     210489
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1     211307     212130     212961     213798     214639     215484     216334
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1     217186     218041     218902     219774     220658     221570     222523
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1     223514     224517     225528     226531     227552     228584     229635
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1     230713     231803     232905     234015     235140     236272     237410
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
## 1     238560     239740     240927     242120     243317     244520     245721
##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
## 1     246909     248078     249238     250391     251539     252690     253835
##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
## 1     254984     256124     257275     258407     259540     260659     261666
##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
## 1     262650     263606     264557     265489     266350     267171     267972
##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
## 1     268754     269527     270292     271047     271780     272491     273182
##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
## 1     273795     274404     275010     275601     276190     276756     277288
##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
## 1     277797     278295     278761     279184     279596     280005     280394
##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
## 1     280770     281031     281282     281524     281722     281903     282082
##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1     282257     282421     282582     282737     282864     282985     283102
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1     283212     283320     283409     283490     283567     283636     283636
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1     283762     283813     283862     283906     283947     283985     284024
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
## 1     284059     284090     284128     284170     284215     284262     284311
##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1     284362     284415     284472     284523     284580     284641     284706
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1     284789     284875     284966     285061     285158     285257     285358
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1     285465     285577     285700     285831     285995     286168     286352
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
## 1     286541     286735     286938     287159     287393     287644     287899
##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
## 1     288162     288441     288732     289035     289353     289684     290027
##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1     290395     290773     291172     291585     292018     292476     292957
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
## 1     293448     293951     294482     295051     295639     296276     296929
##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
## 1     297608     298296     298988     299710     300278     300945     301625
##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
## 1     302327     303045     303783     304524     305269     306030     306798
##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
## 1     307569     308347     309135     309934     310745     311576     312413
##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
## 1     313259     314116     314977     315842     316711     317585     318456
##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
## 1     319339     320207     321084     321967     322852     323733     324619
##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
## 1     325508     326379     327286     328209     329136     330084     331017
##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
## 1     331968     332889     333840     334751     335673     336582     337485
##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
## 1     338414     339335     340269     341188     342097     343026     343026
##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
## 1     344907     345848     346808     347719     348611     349513     350397
##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
## 1     351267     352123     353024     353923     354836     355767     356718
##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
## 1     357629     358578     359516     360435     361368     362260     363162
##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
## 1     364033     364922     365831     366634     367456     368335     369198
##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
## 1     369198     370819     371698     372599     373509     374411     375330
##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
## 1     376233     377081     377960     378843     379654     380520     381343
##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1     382194     383003     383857     384728     385575     386358     387159
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1     387882     388651     389454     390294     391115     391945     392857
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1     393808     394740     395688     396699     397778     398879     400076
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
## 1     401308     402611     403990     405393     406926     408495     410098
##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1     411749     413558     415468     417453     419460     421478     423688
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
## 1     425911     428202     430480     432761     435052     437350     439651
##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
## 1     441923     444117     446308     448497     450676     452821     452821
##   2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20
## 1     457081     459198     461299     463370     465423     467448     469457
##   2022-02-21 2022-02-22     NA     NA
## 1     471460     473449 475341 475341
## 
## 
## [[1]][[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1   EGYPT EGYPT          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          0          0          0          0
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          0          0          0          0          0          0          0
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          0          0          0          0          0          0          0
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          0          0          0          0          0          0          1
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1          1          1          1          1          2          2          2
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1          2          4          6          6          8         10         14
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1         19         20         21         24         30         36         40
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1         41         46         52         58         66         71         78
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1         85         94        103        118        135        146        159
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1        164        178        183        196        205        224        239
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1        250        264        276        287        294        307        317
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1        337        359        380        392        406        415        429
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1        436        452        469        482        503        514        525
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1        533        544        556        571        592        612        630
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1        645        659        680        696        707        735        764
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1        783        797        816        845        879        913        959
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1       1005       1052       1088       1126       1166       1198       1237
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1       1271       1306       1342       1377       1422       1484       1575
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1       1672       1766       1850       1938       2017       2106       2193
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1       2278       2365       2450       2533       2620       2708       2789
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1       2872       2953       3034       3120       3201       3280       3343
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1       3422       3489       3564       3617       3702       3769       3858
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1       3935       4008       4067       4120       4188       4251       4302
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1       4352       4399       4440       4480       4518       4558       4606
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1       4652       4691       4728       4774       4805       4834       4865
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1       4888       4912       4930       4951       4971       4992       5009
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1       5035       5059       5085       5107       5124       5141       5160
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1       5173       5184       5197       5212       5231       5243       5262
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1       5280       5298       5317       5342       5362       5376       5399
##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
## 1       5421       5440       5461       5479       5495       5511       5530
##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
## 1       5541       5560       5577       5590       5607       5627       5648
##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
## 1       5661       5679       5696       5715       5733       5750       5770
##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
## 1       5787       5806       5822       5835       5853       5869       5883
##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
## 1       5901       5914       5930       5946       5956       5970       5981
##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1       5990       6001       6010       6017       6029       6040       6052
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
## 1       6062       6071       6077       6088       6099       6109       6120
##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1       6130       6142       6155       6166       6176       6187       6199
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1       6211       6222       6234       6247       6258       6266       6278
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1       6291       6305       6318       6329       6343       6355       6368
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
## 1       6380       6394       6405       6417       6429       6442       6453
##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1       6465       6481       6495       6508       6521       6535       6548
##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
## 1       6560       6573       6585       6596       6608       6621       6636
##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
## 1       6650       6666       6694       6713       6732       6750       6771
##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
## 1       6790       6813       6832       6854       6877       6898       6920
##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
## 1       6943       6966       6990       7015       7041       7069       7098
##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
## 1       7130       7167       7209       7260       7309       7352       7405
##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
## 1       7466       7520       7576       7631       7687       7741       7805
##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
## 1       7863       7918       7975       8029       8085       8142       8197
##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
## 1       8249       8304       8362       8421       8473       8527       8583
##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1       8638       8696       8747       8801       8853       8902       8959
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1       9012       9067       9115       9169       9217       9263       9316
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1       9360       9407       9460       9512       9560       9604       9651
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1       9699       9751       9804       9857       9899       9935       9994
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1      10050      10101      10150      10201      10250      10298      10353
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1      10404      10443      10495      10541      10590      10639      10688
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1      10736      10778      10822      10871      10916      10954      10995
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1      11038      11082      11128      11169      11214      11256      11300
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1      11340      11384      11431      11472      11512      11557      11598
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1      11637      11680      11720      11768      11804      11845      11882
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1      11914      11956      11995      12041      12084      12123      12163
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1      12210      12253      12290      12323      12362      12405      12445
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1      12487      12526      12570      12611      12653      12694      12738
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1      12778      12820      12866      12914      12959      12998      13049
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1      13107      13168      13219      13278      13339      13402      13469
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1      13531      13591      13655      13714      13779      13845      13904
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
## 1      13972      14033      14091      14150      14206      14269      14327
##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
## 1      14388      14441      14498      14559      14611      14670      14721
##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
## 1      14766      14807      14850      14904      14950      15001      15047
##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
## 1      15096      15136      15178      15222      15268      15309      15352
##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
## 1      15399      15437      15471      15510      15547      15582      15623
##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
## 1      15654      15691      15723      15760      15791      15829      15859
##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
## 1      15898      15935      15967      16002      16031      16062      16092
##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
## 1      16125      16148      16169      16194      16215      16242      16264
##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1      16284      16306      16332      16351      16368      16383      16396
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1      16403      16412      16418      16425      16431      16439      16439
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1      16452      16457      16465      16470      16477      16481      16487
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
## 1      16494      16498      16507      16514      16518      16524      16528
##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1      16535      16540      16550      16557      16562      16566      16575
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1      16582      16588      16597      16604      16609      16615      16619
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1      16625      16630      16638      16647      16654      16663      16671
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
## 1      16676      16683      16691      16701      16706      16714      16721
##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
## 1      16727      16736      16743      16755      16766      16776      16789
##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1      16801      16811      16824      16836      16847      16860      16871
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
## 1      16885      16895      16908      16921      16935      16951      16970
##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
## 1      16992      17016      17043      17074      17110      17149      17187
##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
## 1      17224      17263      17294      17331      17367      17399      17436
##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
## 1      17469      17508      17545      17584      17619      17658      17695
##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
## 1      17726      17765      17806      17846      17884      17926      17970
##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
## 1      18015      18058      18105      18151      18195      18242      18285
##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
## 1      18333      18375      18428      18483      18535      18592      18651
##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
## 1      18711      18769      18832      18889      18949      19011      19076
##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
## 1      19130      19186      19249      19309      19366      19435      19435
##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
## 1      19567      19636      19707      19780      19811      19872      19933
##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
## 1      19991      20052      20109      20172      20237      20305      20347
##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
## 1      20412      20474      20537      20594      20643      20682      20727
##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
## 1      20770      20821      20877      20919      20966      21015      21060
##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
## 1      21060      21155      21203      21234      21277      21315      21361
##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
## 1      21410      21457      21500      21510      21546      21571      21608
##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1      21639      21667      21695      21727      21752      21768      21797
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1      21817      21836      21863      21882      21909      21938      21964
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1      21995      22014      22042      22063      22097      22123      22148
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
## 1      22179      22205      22238      22260      22289      22330      22368
##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1      22396      22431      22460      22496      22522      22566      22604
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
## 1      22635      22683      22735      22780      22819      22877      22936
##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
## 1      22993      23053      23110      23172      23233      23292      23292
##   2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20
## 1      23409      23465      23519      23580      23632      23694      23752
##   2022-02-21 2022-02-22    NA    NA
## 1      23806      23857 23889 23889
## 
## 
## [[2]]
##   geo.loc  Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1   EGYPT EGYPT          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          0          0          0          0
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          0          0          0          0          0          0          0
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          0          0          0          0          1          1          1
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          1          1          1          1          1          1          1
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1          1          1         27         27         27         27         21
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1         27         32         32         32         39         41         56
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1         56         80         95        102        116        121        132
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1        150        157        179        201        216        241        247
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1        259        276        305        348        384        426        589
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1        589        589        589        596        646        701        732
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1        821        870        935       1004       1075       1114       1176
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1       1236       1304       1335       1381       1460       1522       1562
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1       1632       1730       1815       1887       1945       2002       2075
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1       2172       2326       2486       2626       2799       2950       3172
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1       3440       3742       3994       4217       4374       4628       4807
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1       4900       5027       5205       5359       5511       5693       6037
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1       6447       6827       7350       7756       8158       8538       8961
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1       9375       9786      10289      10691      11108      11529      11931
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1      12329      12730      13141      13528      13928      14327      14736
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1      15133      15535      15935      16338      16737      17140      17539
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1      17951      18460      18881      19288      19690      20103      20726
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1      21238      21718      22241      22753      23274      23876      24419
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1      24975      25544      26135      26691      27302      27868      28380
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1      28924      29473      30075      31066      31970      32903      33831
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1      34838      35959      37025      38236      39638      41137      42455
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1      44066      45569      47182      48898      50553      51672      52678
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1      53779      54888      55901      56890      57858      58835      59743
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1      60651      61562      62553      63462      64318      65118      65927
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1      66817      67717      68713      69612      70419      71302      72120
##   2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06
## 1      72929      73828      74626      75415      76305      77208      78108
##   2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13
## 1      79008      79886      80689      81597      82473      83261      84161
##   2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20
## 1      84969      85745      86549      87158      87958      88666      89532
##   2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27
## 1      90332      91143      91843      92644      93531      94374      95080
##   2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04
## 1      95586      96094      96494      96855      97143      97274      97355
##   2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11
## 1      97398      97449      97492      97524      97592      97643      97688
##   2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18
## 1      97743      97841      97920      98011      98089      98157      98247
##   2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25
## 1      98314      98413      98516      98624      98713      98813      98903
##   2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01
## 1      98981      99084      99174      99273      99353      99452      99555
##   2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08
## 1      99652      99765      99874     100006     100106     100239     100342
##   2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15
## 1     100439     100540     100662     100760     100847     100946     101046
##   2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1     101179     101288     101421     101564     101685     101783     101881
##   2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29
## 1     101981     102103     102201     102268     102390     102490     102596
##   2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06
## 1     102718     102816     102949     103082     103191     103324     103501
##   2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13
## 1     103703     103913     104074     104281     104499     104710     104875
##   2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20
## 1     105132     105450     105719     105919     106157     106481     106817
##   2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27
## 1     107162     107563     107961     108474     108985     109462     110015
##   2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03
## 1     110436     111040     111451     112105     112826     113480     113898
##   2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10
## 1     114601     115414     115975     116775     117529     118294     118900
##   2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17
## 1     119212     119635     120312     121072     121792     122291     122993
##   2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24
## 1     123491     124094     124605     125171     125603     126176     126497
##   2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31
## 1     127001     127433     127963     128440     128800     129293     129636
##   2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07
## 1     130107     130514     130912     131211     131632     132054     132375
##   2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14
## 1     132698     133098     133331     133707     134215     134638     134960
##   2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21
## 1     135349     135670     136081     136490     136889     137294     137837
##   2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28
## 1     138183     138683     139072     139494     139927     140460     140892
##   2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07
## 1     141347     141655     142155     142610     143143     143575     144019
##   2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14
## 1     144485     144917     145418     145923     146434     146803     147234
##   2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21
## 1     147767     148089     148424     148823     149256     149489     149934
##   2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28
## 1     150424     150924     151444     151765     152198     152642     153175
##   2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04
## 1     153630     154194     154694     155016     155448     155886     156219
##   2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11
## 1     156574     157006     157450     157889     158454     159054     159499
##   2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18
## 1     159999     160431     161031     161470     162170     162714     163479
##   2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25
## 1     163812     164368     164803     165348     166024     166457     167024
##   2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02
## 1     167900     168665     169308     170008     170773     171542     172342
##   2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09
## 1     172774     173341     174217     175117     175928     176363     176763
##   2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16
## 1     177440     178241     178805     179261     179817     180577     181478
##   2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23
## 1     182024     182693     183696     184373     185243     186223     186678
##   2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30
## 1     187446     187691     188567     189476     190254     191475     191475
##   2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06
## 1     192112     192823     193491     194291     195072     195871     196604
##   2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13
## 1     197281     197832     198632     199285     199840     200273     201038
##   2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20
## 1     201739     202650     203193     203802     204701     205157     205613
##   2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27
## 1     206053     206852     207411     208192     208957     209395     210052
##   2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04
## 1     210482     210805     211384     212059     212725     213628     214087
##   2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11
## 1     214852     215419     216217     216982     217324     217756     218412
##   2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18
## 1     219291     219525     220530     221516     222315     223213     223213
##   2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25
## 1     224299     225068     225869     226535     227068     227490     227612
##   2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01
## 1     227970     228624     228836     229167     229712     230368     230699
##   2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08
## 1     231259     232060     232179          0          0          0          0
##   2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15
## 1          0          0          0          0          0          0          0
##   2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22
## 1          0          0          0          0          0          0          0
##   2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29
## 1          0          0          0          0          0          0          0
##   2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05
## 1          0          0          0          0          0          0          0
##   2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12
## 1          0          0          0          0          0          0          0
##   2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19
## 1          0          0          0          0          0          0          0
##   2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26
## 1          0          0          0          0          0          0          0
##   2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03
## 1          0          0          0          0          0          0          0
##   2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10
## 1          0          0          0          0          0          0          0
##   2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17
## 1          0          0          0          0          0          0          0
##   2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24
## 1          0          0          0          0          0          0          0
##   2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31
## 1          0          0          0          0          0          0          0
##   2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07
## 1          0          0          0          0          0          0          0
##   2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14
## 1          0          0          0          0          0          0          0
##   2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21
## 1          0          0          0          0          0          0          0
##   2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28
## 1          0          0          0          0          0          0          0
##   2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05
## 1          0          0          0          0          0          0          0
##   2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12
## 1          0          0          0          0          0          0          0
##   2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19
## 1          0          0          0          0          0          0          0
##   2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26
## 1          0          0          0          0          0          0          0
##   2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02
## 1          0          0          0          0          0          0          0
##   2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09
## 1          0          0          0          0          0          0          0
##   2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16
## 1          0          0          0          0          0          0          0
##   2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23
## 1          0          0          0          0          0          0          0
##   2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30
## 1          0          0          0          0          0          0          0
##   2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06
## 1          0          0          0          0          0          0          0
##   2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13
## 1          0          0          0          0          0          0          0
##   2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20
## 1          0          0          0          0          0          0          0
##   2022-02-21 2022-02-22 NA NA
## 1          0          0  0  0

# read the time series data for all the cases
all.data <- covid19.data('ts-ALL')

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:28:35 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:28:37 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:28:39 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

# run on all the cases
tots.per.location(all.data,"Saudi Arabia")

## Processing confirmed cases

## SAUDI ARABIA  --  741864 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -96790 -43262   -777  40508  95179 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29277.735   3920.346   7.468 2.23e-13 ***
## x.var         858.096      8.879  96.643  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 54130 on 762 degrees of freedom
## Multiple R-squared:  0.9246, Adjusted R-squared:  0.9245 
## F-statistic:  9340 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3258 -1.0123  0.4311  1.8407  2.7443 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.9364532  0.1842118   43.08   <2e-16 ***
## x.var       0.0097343  0.0004172   23.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.543 on 762 degrees of freedom
## Multiple R-squared:  0.4167, Adjusted R-squared:  0.4159 
## F-statistic: 544.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -514.83  -130.47    61.66   127.43   230.74  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.166e+01  1.616e-04   72151   <2e-16 ***
## x.var       2.548e-03  3.001e-07    8490   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 117323596  on 763  degrees of freedom
## Residual deviance:  38501692  on 762  degrees of freedom
## AIC: 38511933
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing death cases

## SAUDI ARABIA  --  8990 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1720.6  -803.4   260.6   653.3  1391.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 178.8590    65.6926   2.723  0.00662 ** 
## x.var        13.7850     0.1488  92.651  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 907 on 762 degrees of freedom
## Multiple R-squared:  0.9185, Adjusted R-squared:  0.9184 
## F-statistic:  8584 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8430 -0.8891  0.3191  1.4740  2.0395 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3069326  0.1307682   32.94   <2e-16 ***
## x.var       0.0086457  0.0002962   29.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 762 degrees of freedom
## Multiple R-squared:  0.5279, Adjusted R-squared:  0.5273 
## F-statistic: 852.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -62.137  -26.057    6.603   19.457   31.179  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.398e+00  1.335e-03    5542   <2e-16 ***
## x.var       2.703e-03  2.456e-06    1100   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2049545  on 763  degrees of freedom
## Residual deviance:  711054  on 762  degrees of freedom
## AIC: 718163
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## Processing recovered cases

## SAUDI ARABIA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -196409 -187714    1971  166751  318505 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 196422.66   13243.15  14.832   <2e-16 ***
## x.var          -13.47      29.99  -0.449    0.654    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 182800 on 762 degrees of freedom
## Multiple R-squared:  0.0002644,  Adjusted R-squared:  -0.001048 
## F-statistic: 0.2015 on 1 and 762 DF,  p-value: 0.6536
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.335  -5.141   2.473   4.809   6.889 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.3438506  0.4006160   28.32   <2e-16 ***
## x.var       -0.0090845  0.0009073  -10.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.531 on 762 degrees of freedom
## Multiple R-squared:  0.1163, Adjusted R-squared:  0.1151 
## F-statistic: 100.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -626.83  -612.33     4.47   339.63   604.80  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.219e+01  1.645e-04 74089.9   <2e-16 ***
## x.var       -7.040e-05  3.751e-07  -187.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 177193250  on 763  degrees of freedom
## Residual deviance: 177158022  on 762  degrees of freedom
## AIC: 177164977
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## [[1]]
## [[1]][[1]]
## [[1]][[1]][[1]]
## list()
## 
## [[1]][[1]][[2]]
##        geo.loc         Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25
## 1 SAUDI ARABIA SAUDI ARABIA          0          0          0          0
##   2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01
## 1          0          0          0          0          0          0          0
##   2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08
## 1          0          0          0          0          0          0          0
##   2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15
## 1          0          0          0          0          0          0          0
##   2020-02-16 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22
## 1          0          0          0          0          0          0          0
##   2020-02-23 2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29
## 1          0          0          0          0          0          0          0
##   2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07
## 1          0          1          1          1          5          5          5
##   2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14
## 1         11         15         20         21         45         86        103
##   2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21
## 1        103        118        171        171        274        344        392
##   2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28
## 1        511        562        767        900       1012       1104       1203
##   2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04
## 1       1299       1453       1563       1720       1885       2039       2179
##   2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11
## 1       2402       2605       2795       2932       3287       3651       4033
##   2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18
## 1       4462       4934       5369       5862       6380       7142       8274
##   2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25
## 1       9362      10484      11631      12772      13930      15102      16299
##   2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02
## 1      17522      18811      20077      21402      22753      24097      25459
##   2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09
## 1      27011      28656      30251      31938      33731      35432      37136
##   2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16
## 1      39048      41014      42925      44830      46869      49176      52016
##   2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23
## 1      54752      57345      59854      62545      65077      67719      70161
##   2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30
## 1      72560      74795      76726      78541      80185      81766      83384
##   2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06
## 1      85261      87142      89011      91182      93157      95748      98869
##   2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13
## 1     101914     105283     108571     112288     116021     119942     123308
##   2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20
## 1     127541     132048     136315     141234     145991     150292     154233
##   2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27
## 1     157612     161005     164144     167267     170639     174577     178504
##   2020-06-28 2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04
## 1     182493     186436     190823     194225     197608     201801     205929
##   2020-07-05 2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11
## 1     209509     213716     217108     220144     223327     226486     229480
##   2020-07-12 2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18
## 1     232259     235111     237803     240474     243238     245851     248416
##   2020-07-19 2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25
## 1     250920     253349     255825     258156     260394     262772     264973
##   2020-07-26 2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01
## 1     266941     268934     270831     272590     274219     275905     277478
##   2020-08-02 2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08
## 1     278835     280093     281456     282824     284226     285793     287262
##   2020-08-09 2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15
## 1     288690     289947     291468     293037     294519     295902     297315
##   2020-08-16 2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22
## 1     298542     299914     301323     302686     303973     305186     306370
##   2020-08-23 2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29
## 1     307479     308654     309768     310836     311855     312924     313911
##   2020-08-30 2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05
## 1     314821     315772     316670     317486     318319     319141     319932
##   2020-09-06 2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12
## 1     320688     321456     322237     323012     323720     324407     325050
##   2020-09-13 2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19
## 1     325651     326258     326930     327551     328144     328720     329271
##   2020-09-20 2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26
## 1     329754     330246     330798     331359     331857     332329     332790
##   2020-09-27 2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03
## 1     333193     333648     334187     334605     335097     335578     335997
##   2020-10-04 2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10
## 1     336387     336766     337243     337711     338132     338539     338944
##   2020-10-11 2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17
## 1     339267     339615     340089     340590     341062     341495     341854
##   2020-10-18 2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24
## 1     342202     342583     342968     343373     343774     344157     344552
##   2020-10-25 2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31
## 1     344875     345232     345631     346047     346482     346880     347282
##   2020-11-01 2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07
## 1     347656     348037     348510     348936     349386     349822     350229
##   2020-11-08 2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14
## 1     350592     350984     351455     351849     352160     352601     352950
##   2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21
## 1     353255     353556     353918     354208     354527     354813     355034
##   2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28
## 1     355258     355489     355741     356067     356389     356691     356911
##   2020-11-29 2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05
## 1     357128     357360     357623     357872     358102     358336     358526
##   2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12
## 1     358713     358922     359115     359274     359415     359583     359749
##   2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19
## 1     359888     360013     360155     360335     360516     360690     360848
##   2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26
## 1     361010     361178     361359     361536     361725     361903     362066
##   2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02
## 1     362220     362339     362488     362601     362741     362878     362979
##   2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09
## 1     363061     363155     363259     363377     363485     363582     363692
##   2021-01-10 2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16
## 1     363809     363949     364096     364271     364440     364613     364753
##   2021-01-17 2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23
## 1     364929     365099     365325     365563     365775     365988     366185
##   2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30
## 1     366371     366584     366807     367023     367276     367543     367813
##   2021-01-31 2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06
## 1     368074     368329     368639     368945     369248     369575     369961
##   2021-02-07 2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13
## 1     370278     370634     370987     371356     371720     372073     372410
##   2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20
## 1     372732     373046     373368     373702     374029     374366     374691
##   2021-02-21 2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27
## 1     375006     375333     375668     376021     376377     376723     377061
##   2021-02-28 2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06
## 1     377383     377700     378002     378333     378708     379092     379474
##   2021-03-07 2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13
## 1     379831     380182     380572     380958     381348     381708     382059
##   2021-03-14 2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20
## 1     382407     382752     383106     383499     383880     384271     384653
##   2021-03-21 2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27
## 1     385020     385424     385834     386300     386782     387292     387794
##   2021-03-28 2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03
## 1     388325     388866     389422     390007     390597     391325     392009
##   2021-04-04 2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10
## 1     392682     393377     394169     394952     395854     396758     397636
##   2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17
## 1     398435     399277     400228     401157     402142     403106     404054
##   2021-04-18 2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24
## 1     404970     405940     407010     408038     409093     410191     411263
##   2021-04-25 2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01
## 1     412216     413174     414219     415281     416307     417363     418411
##   2021-05-02 2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08
## 1     419348     420301     421300     422316     423406     424445     425442
##   2021-05-09 2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15
## 1     426384     427370     428369     429389     430505     431432     432269
##   2021-05-16 2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22
## 1     433094     433980     435027     436239     437569     438705     439847
##   2021-05-23 2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29
## 1     440914     442071     443460     444780     445963     447178     448284
##   2021-05-30 2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05
## 1     449191     450436     451687     452956     454217     455418     456562
##   2021-06-06 2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12
## 1     457546     458707     459968     461242     461242     463703     464780
##   2021-06-13 2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19
## 1     465797     466906     468175     469414     470723     471959     473112
##   2021-06-20 2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26
## 1     474191     475403     476882     478135     479390     480702     482003
##   2021-06-27 2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03
## 1     483221     484539     486106     487592     489126     490464     491612
##   2021-07-04 2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10
## 1     492785     494032     495309     496516     497773     498906     500083
##   2021-07-11 2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17
## 1     501195     502439     503734     504960     506125     507423     508521
##   2021-07-18 2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24
## 1     509576     510869     512142     513284     514446     515693     516949
##   2021-07-25 2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31
## 1     518143     519395     520774     522108     522108     522108     525730
##   2021-08-01 2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07
## 1     526814     526814     526814     526814     526814     531935     531935
##   2021-08-08 2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14
## 1     531935     531935     531935     531935     531935     537374     537374
##   2021-08-15 2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21
## 1     537374     539129     539129     539129     540244     540244     541201
##   2021-08-22 2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28
## 1     541201     541994     541994     541994     541994     543318     543318
##   2021-08-29 2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04
## 1     543318     543318     543318     543318     543318     543318     543318
##   2021-09-05 2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11
## 1     545243     545243     545505     545505     545727     545829     545829
##   2021-09-12 2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18
## 1     545829     545829     545829     545829     546336     546411     546411
##   2021-09-19 2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25
## 1     546411     546612     546681     546735     546735     546735     546882
##   2021-09-26 2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02
## 1     546926     546985     547035     547035     547134     547134     547134
##   2021-10-03 2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09
## 1     547134     547134     547357     547402     547449     547497     547532
##   2021-10-10 2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16
## 1     547591     547649     547704     547761     547797     547845     547890
##   2021-10-17 2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23
## 1     547931     547969     548018     548065     548111     548162     548205
##   2021-10-24 2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30
## 1     548252     548303     548368     548423     548474     548530     548571
##   2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06
## 1     548617     548666     548711     548760     548805     548848     548890
##   2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13
## 1     548930     548973     549022     549060     549103     549148     549192
##   2021-11-14 2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20
## 1     549222     549260     549297     549339     549377     549412     549443
##   2021-11-21 2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27
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##   2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22
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## 
## 
## [[1]][[2]]
##        geo.loc         Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25
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##   2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01
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##   2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14
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##   2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21
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##   2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28
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##   2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04
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##   2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11
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##   2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18
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##   2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25
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##   2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02
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##   2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09
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##   2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21
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##   2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28
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##   2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12
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##   2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19
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##   2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26
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##   2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02
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##   2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09
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##   2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30
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##   2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20
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##   2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17
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##   2022-01-09 2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15
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##   2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22
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##   2022-01-23 2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29
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##   2022-01-30 2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05
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##   2022-02-06 2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12
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##   2022-02-13 2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19
## 1       8973       8974       8975       8977       8978       8981       8982
##   2022-02-20 2022-02-21 2022-02-22   NA   NA
## 1       8984       8986       8987 8990 8990
## 
## 
## [[2]]
##        geo.loc         Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25
## 1 SAUDI ARABIA SAUDI ARABIA          0          0          0          0
##   2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01
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##   2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08
## 1          0          0          0          0          0          0          0
##   2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15
## 1          0          0          0          0          0          0          0
##   2020-02-16 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22
## 1          0          0          0          0          0          0          0
##   2020-02-23 2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29
## 1          0          0          0          0          0          0          0
##   2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07
## 1          0          0          0          0          0          0          0
##   2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14
## 1          0          0          1          1          1          1          1
##   2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21
## 1          1          2          6          6          6          8         16
##   2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28
## 1         16         16         28         29         33         35         37
##   2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04
## 1         66        115        165        264        328        351        420
##   2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11
## 1        488        551        615        631        666        685        720
##   2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18
## 1        761        805        889        931        990       1049       1329
##   2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25
## 1       1398       1490       1640       1812       1925       2049       2215
##   2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02
## 1       2357       2531       2784       2953       3163       3555       3765
##   2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09
## 1       4134       4476       5431       6783       7798       9120      10144
##   2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16
## 1      11457      12737      15257      17622      19051      21869      23666
##   2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23
## 1      25722      28748      31634      33478      36040      39003      41236
##   2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30
## 1      43520      45668      48450      51022      54553      57013      58883
##   2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06
## 1      62442      64306      65790      68159      68965      70616      71791
##   2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13
## 1      72817      74524      76339      77954      80019      81029      82548
##   2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20
## 1      84720      87890      89540      91662      93915      95764      98917
##   2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27
## 1     101130     105175     109885     112797     117882     120471     122128
##   2020-06-28 2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04
## 1     124755     127118     130766     132760     137669     140614     143256
##   2020-07-05 2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11
## 1     145236     149634     154839     158050     161096     163026     165396
##   2020-07-12 2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18
## 1     167138     169842     177560     183048     187622     191161     194218
##   2020-07-19 2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25
## 1     197735     203259     207259     210398     213490     215731     217782
##   2020-07-26 2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01
## 1     220323     222936     225624     228569     231198     235658     237548
##   2020-08-02 2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08
## 1     240081     242055     243713     245314     247089     248948     250440
##   2020-08-09 2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15
## 1     252039     253478     255118     257269     260393     262959     264487
##   2020-08-16 2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22
## 1     266953     268385     272911     274091     275476     277067     278441
##   2020-08-23 2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29
## 1     280143     282888     283932     284945     286255     287403     288441
##   2020-08-30 2020-08-31 2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05
## 1     289667     290796     291514     292510     293964     295063     295842
##   2020-09-06 2020-09-07 2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12
## 1     296737     297623     298246     298966     299998     300933     301836
##   2020-09-13 2020-09-14 2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19
## 1     302870     303930     305022     306004     307207     308352     309430
##   2020-09-20 2020-09-21 2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26
## 1     310439     311499     312684     313786     314793     315636     316405
##   2020-09-27 2020-09-28 2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03
## 1     317005     317846     318542     319154     319746     320348     320974
##   2020-10-04 2020-10-05 2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10
## 1     321485     322055     322612     323208     323769     324282     324737
##   2020-10-11 2020-10-12 2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17
## 1     325330     325839     326339     326820     327327     327795     328165
##   2020-10-18 2020-10-19 2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24
## 1     328538     328895     329270     329715     330181     330578     330995
##   2020-10-25 2020-10-26 2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31
## 1     331330     331691     332117     332550     333005     333409     333842
##   2020-11-01 2020-11-02 2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07
## 1     334236     334672     335153     335594     336068     336533     336966
##   2020-11-08 2020-11-09 2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14
## 1     337386     337788     338281     338702     339114     339568     339947
##   2020-11-15 2020-11-16 2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21
## 1     340304     340668     341104     341515     341956     342404     342882
##   2020-11-22 2020-11-23 2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28
## 1     343371     343816     344311     344787     345215     345622     346023
##   2020-11-29 2020-11-30 2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05
## 1     346409     346802     347176     347513     347881     348238     348562
##   2020-12-06 2020-12-07 2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12
## 1     348879     349168     349414     349624     349872     350108     350347
##   2020-12-13 2020-12-14 2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19
## 1     350549     350792     350993     351192     351365     351573     351722
##   2020-12-20 2020-12-21 2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26
## 1     351878     352089     352249     352418     352608     352815     353004
##   2020-12-27 2020-12-28 2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02
## 1     353179     353353     353512     353682     353853     354081     354263
##   2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09
## 1     354443     354609     354755     354899     355037     355208     355382
##   2021-01-10 2021-01-11 2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16
## 1     355548     355706     355857     356013     356201     356382     356541
##   2021-01-17 2021-01-18 2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23
## 1     356687     356848     357004     357177     357337     357525     357728
##   2021-01-24 2021-01-25 2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30
## 1     357939     358137     358340     358545     358753     359006     359299
##   2021-01-31 2021-02-01 2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06
## 1     359573     359839     360110     360400     360697     360954     361237
##   2021-02-07 2021-02-08 2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13
## 1     361515     361813     362062     362368     362642     362947     363303
##   2021-02-14 2021-02-15 2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20
## 1     363585     363926     364297     364646     365017     365363     365745
##   2021-02-21 2021-02-22 2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27
## 1     366094     366412     366735     367015     367323     367691     368011
##   2021-02-28 2021-03-01 2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06
## 1     368305     368640     368926     369277     369613     369922     370300
##   2021-03-07 2021-03-08 2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13
## 1     370614     371032     371338     371583     371850     372217     372456
##   2021-03-14 2021-03-15 2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20
## 1     372703     372926     373130     373361     373601     373864     374135
##   2021-03-21 2021-03-22 2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27
## 1     374412     374799     375165     375471     375831     376203     376558
##   2021-03-28 2021-03-29 2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03
## 1     376947     377304     377714     378083     378469     378873     379312
##   2021-04-04 2021-04-05 2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10
## 1     379816     380305     380772     381189     381658     382198     382773
##   2021-04-11 2021-04-12 2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17
## 1     383321     384027     384635     385441     386102     387020     387795
##   2021-04-18 2021-04-19 2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24
## 1     388702     389598     390538     391362     392448     393671     394529
##   2021-04-25 2021-04-26 2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01
## 1     395557     396604     397587     398454     399509     400580     401544
##   2021-05-02 2021-05-03 2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08
## 1     402664     403702     404707     405607     406589     407650     408676
##   2021-05-09 2021-05-10 2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15
## 1     409740     410816     412102     413010     414139     415747     416759
##   2021-05-16 2021-05-17 2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22
## 1     417787     418914     419761     420671     421726     422706     423795
##   2021-05-23 2021-05-24 2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29
## 1     424690     425677     426589     427462     428502     429663     430937
##   2021-05-30 2021-05-31 2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05
## 1     432138     433413     434439     435520     436884     438206     439459
##   2021-06-06 2021-06-07 2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12
## 1     440644     441860     442782     443810     443810     446054     446960
##   2021-06-13 2021-06-14 2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19
## 1     448093     449241     450255     451187     452209     453259     454404
##   2021-06-20 2021-06-21 2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26
## 1     455618     457128     458048     459091     460338     461628     463004
##   2021-06-27 2021-06-28 2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03
## 1     464256     465546     466578     467633     469120     470328     471550
##   2021-07-04 2021-07-05 2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10
## 1     472939     474368     475448     476643     478127     479709     481225
##   2021-07-11 2021-07-12 2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17
## 1     482414     483937     484883     486011     486918     488883     489553
##   2021-07-18 2021-07-19 2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24
## 1     490696     492149     493240     494264     495650     496810     497965
##   2021-07-25 2021-07-26 2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31
## 1     499129     500428     501449     502528     502528     502528     506089
##   2021-08-01 2021-08-02 2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07
## 1     507374     507374     507374     507374          0          0          0
##   2021-08-08 2021-08-09 2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14
## 1          0          0          0          0          0          0          0
##   2021-08-15 2021-08-16 2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21
## 1          0          0          0          0          0          0          0
##   2021-08-22 2021-08-23 2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28
## 1          0          0          0          0          0          0          0
##   2021-08-29 2021-08-30 2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04
## 1          0          0          0          0          0          0          0
##   2021-09-05 2021-09-06 2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11
## 1          0          0          0          0          0          0          0
##   2021-09-12 2021-09-13 2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18
## 1          0          0          0          0          0          0          0
##   2021-09-19 2021-09-20 2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25
## 1          0          0          0          0          0          0          0
##   2021-09-26 2021-09-27 2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02
## 1          0          0          0          0          0          0          0
##   2021-10-03 2021-10-04 2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09
## 1          0          0          0          0          0          0          0
##   2021-10-10 2021-10-11 2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16
## 1          0          0          0          0          0          0          0
##   2021-10-17 2021-10-18 2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23
## 1          0          0          0          0          0          0          0
##   2021-10-24 2021-10-25 2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30
## 1          0          0          0          0          0          0          0
##   2021-10-31 2021-11-01 2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06
## 1          0          0          0          0          0          0          0
##   2021-11-07 2021-11-08 2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13
## 1          0          0          0          0          0          0          0
##   2021-11-14 2021-11-15 2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20
## 1          0          0          0          0          0          0          0
##   2021-11-21 2021-11-22 2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27
## 1          0          0          0          0          0          0          0
##   2021-11-28 2021-11-29 2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04
## 1          0          0          0          0          0          0          0
##   2021-12-05 2021-12-06 2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11
## 1          0          0          0          0          0          0          0
##   2021-12-12 2021-12-13 2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18
## 1          0          0          0          0          0          0          0
##   2021-12-19 2021-12-20 2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25
## 1          0          0          0          0          0          0          0
##   2021-12-26 2021-12-27 2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01
## 1          0          0          0          0          0          0          0
##   2022-01-02 2022-01-03 2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08
## 1          0          0          0          0          0          0          0
##   2022-01-09 2022-01-10 2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15
## 1          0          0          0          0          0          0          0
##   2022-01-16 2022-01-17 2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22
## 1          0          0          0          0          0          0          0
##   2022-01-23 2022-01-24 2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29
## 1          0          0          0          0          0          0          0
##   2022-01-30 2022-01-31 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05
## 1          0          0          0          0          0          0          0
##   2022-02-06 2022-02-07 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12
## 1          0          0          0          0          0          0          0
##   2022-02-13 2022-02-14 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19
## 1          0          0          0          0          0          0          0
##   2022-02-20 2022-02-21 2022-02-22 NA NA
## 1          0          0          0  0  0

# total for death cases for "ALL" the regions
tots.per.location(covid19.TS.deaths)

## AFGHANISTAN  --  7574 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1697.77  -628.48    84.86   628.06  1527.37 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1314.0427    62.4951  -21.03   <2e-16 ***
## x.var          11.9096     0.1415   84.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 862.9 on 762 degrees of freedom
## Multiple R-squared:  0.9028, Adjusted R-squared:  0.9027 
## F-statistic:  7080 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9643 -0.6932  0.1986  1.1297  2.0028 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.3947336  0.1078933   31.46   <2e-16 ***
## x.var       0.0093379  0.0002444   38.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.49 on 762 degrees of freedom
## Multiple R-squared:  0.6571, Adjusted R-squared:  0.6567 
## F-statistic:  1460 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -34.695  -15.625    2.273    8.268   30.594  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.032e+00  2.136e-03    2824   <2e-16 ***
## x.var       4.286e-03  3.634e-06    1179   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1991983  on 763  degrees of freedom
## Residual deviance:  241270  on 762  degrees of freedom
## AIC: 247883
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ALBANIA  --  3453 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -453.16 -153.48  -40.21  199.83  566.23 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -571.6261    19.3842  -29.49   <2e-16 ***
## x.var          5.3935     0.0439  122.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 267.6 on 762 degrees of freedom
## Multiple R-squared:  0.9519, Adjusted R-squared:  0.9519 
## F-statistic: 1.509e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0059 -0.6286  0.1344  1.0198  1.3052 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5444760  0.0820792   31.00   <2e-16 ***
## x.var       0.0094175  0.0001859   50.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.133 on 762 degrees of freedom
## Multiple R-squared:  0.7711, Adjusted R-squared:  0.7708 
## F-statistic:  2566 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -22.797  -19.175   -5.548   10.769   25.160  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.307e+00  3.110e-03  1706.1   <2e-16 ***
## x.var       4.195e-03  5.312e-06   789.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 967726  on 763  degrees of freedom
## Residual deviance: 189378  on 762  degrees of freedom
## AIC: 195383
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ALGERIA  --  6816 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -639.83 -107.65   10.88  194.76  593.34 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -599.24489   22.88188  -26.19   <2e-16 ***
## x.var          9.42030    0.05182  181.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 315.9 on 762 degrees of freedom
## Multiple R-squared:  0.9775, Adjusted R-squared:  0.9774 
## F-statistic: 3.304e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6645 -0.4977  0.3003  1.1205  1.3611 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2845375  0.1072485   39.95   <2e-16 ***
## x.var       0.0075997  0.0002429   31.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.481 on 762 degrees of freedom
## Multiple R-squared:  0.5623, Adjusted R-squared:  0.5617 
## F-statistic: 978.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -37.713   -9.587    2.227    8.004   16.370  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.392e+00  1.994e-03  3206.3   <2e-16 ***
## x.var       3.490e-03  3.516e-06   992.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1318085  on 763  degrees of freedom
## Residual deviance:  161752  on 762  degrees of freedom
## AIC: 168560
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANDORRA  --  151 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.953 -11.420   1.304  10.005  21.534 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 14.231451   0.888216   16.02   <2e-16 ***
## x.var        0.195364   0.002012   97.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.26 on 762 degrees of freedom
## Multiple R-squared:  0.9252, Adjusted R-squared:  0.9251 
## F-statistic:  9431 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6952 -0.3753  0.3780  0.5471  1.0103 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.4319161  0.0657256   37.00   <2e-16 ***
## x.var       0.0043886  0.0001489   29.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9075 on 762 degrees of freedom
## Multiple R-squared:  0.5329, Adjusted R-squared:  0.5322 
## F-statistic: 869.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.6316  -1.4674   0.4654   1.3989   3.0908  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.479e+00  9.937e-03   350.1   <2e-16 ***
## x.var       2.307e-03  1.873e-05   123.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 23233.9  on 763  degrees of freedom
## Residual deviance:  6896.2  on 762  degrees of freedom
## AIC: 11324
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANGOLA  --  1899 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -300.99 -158.68  -77.87  199.25  405.43 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -408.22645   14.31011  -28.53   <2e-16 ***
## x.var          2.80048    0.03241   86.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 197.6 on 762 degrees of freedom
## Multiple R-squared:  0.9074, Adjusted R-squared:  0.9073 
## F-statistic:  7466 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9421 -0.9446  0.0412  0.9584  1.5101 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2588902  0.0722923   17.41   <2e-16 ***
## x.var       0.0101976  0.0001637   62.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9981 on 762 degrees of freedom
## Multiple R-squared:  0.8358, Adjusted R-squared:  0.8356 
## F-statistic:  3879 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -16.260  -10.314    1.723    4.589    8.288  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.894e+00  5.376e-03   724.2   <2e-16 ***
## x.var       5.244e-03  8.826e-06   594.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 529284  on 763  degrees of freedom
## Residual deviance:  42489  on 762  degrees of freedom
## AIC: 47691
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANTARCTICA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## ANTIGUA AND BARBUDA  --  135 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.684 -17.525  -6.288  19.125  37.389 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -30.112576   1.460960  -20.61   <2e-16 ***
## x.var         0.168740   0.003309   51.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.17 on 762 degrees of freedom
## Multiple R-squared:  0.7734, Adjusted R-squared:  0.7731 
## F-statistic:  2601 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63707 -0.26312 -0.01099  0.26456  0.81598 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.731e-02  2.564e-02  -0.675      0.5    
## x.var        6.833e-03  5.806e-05 117.681   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.354 on 762 degrees of freedom
## Multiple R-squared:  0.9478, Adjusted R-squared:  0.9478 
## F-statistic: 1.385e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3776  -1.5671  -0.4391   1.0007   2.7706  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.0210750  0.0287942   0.732    0.464    
## x.var       0.0067558  0.0000453 149.134   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 37287.4  on 763  degrees of freedom
## Residual deviance:  1684.6  on 762  degrees of freedom
## AIC: 4863.7
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## ARGENTINA  --  125775 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -13734  -7133  -2896   8346  23059 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -23263.469    696.306  -33.41   <2e-16 ***
## x.var          204.572      1.577  129.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9614 on 762 degrees of freedom
## Multiple R-squared:  0.9567, Adjusted R-squared:  0.9566 
## F-statistic: 1.683e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9709 -1.0271  0.4056  1.4736  2.3531 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3853533  0.1354468   32.38   <2e-16 ***
## x.var       0.0127304  0.0003068   41.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.87 on 762 degrees of freedom
## Multiple R-squared:  0.6933, Adjusted R-squared:  0.6928 
## F-statistic:  1722 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -156.09  -122.72    11.28    65.98   102.35  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.821e+00  5.238e-04   16840   <2e-16 ***
## x.var       4.360e-03  8.884e-07    4907   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 37020606  on 763  degrees of freedom
## Residual deviance:  6505382  on 762  degrees of freedom
## AIC: 6513770
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ARMENIA  --  8378 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -793.1 -368.7 -119.4  325.7 1279.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.291e+03  3.911e+01  -33.02   <2e-16 ***
## x.var        1.167e+01  8.859e-02  131.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 540 on 762 degrees of freedom
## Multiple R-squared:  0.9579, Adjusted R-squared:  0.9579 
## F-statistic: 1.735e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7051 -0.7858  0.2892  1.2833  1.6410 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.0709140  0.1043362   29.43   <2e-16 ***
## x.var       0.0099093  0.0002363   41.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.441 on 762 degrees of freedom
## Multiple R-squared:  0.6977, Adjusted R-squared:  0.6973 
## F-statistic:  1758 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -32.689  -14.581   -4.414   14.756   22.822  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.006e+00  2.161e-03    2779   <2e-16 ***
## x.var       4.293e-03  3.676e-06    1168   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1955228  on 763  degrees of freedom
## Residual deviance:  237482  on 762  degrees of freedom
## AIC: 244010
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## AUSTRALIA  --  5062 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -692.23 -294.76  -11.83  167.78 2752.15 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -341.30717   38.50835  -8.863   <2e-16 ***
## x.var          3.47010    0.08722  39.787   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 531.7 on 762 degrees of freedom
## Multiple R-squared:  0.6751, Adjusted R-squared:  0.6746 
## F-statistic:  1583 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5512 -0.5542  0.1678  0.7414  1.7223 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2676639  0.0842500   38.78   <2e-16 ***
## x.var       0.0072700  0.0001908   38.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.163 on 762 degrees of freedom
## Multiple R-squared:  0.6558, Adjusted R-squared:  0.6553 
## F-statistic:  1452 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -18.422  -11.509   -5.334    8.825   30.587  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.976e+00  3.749e-03  1327.4   <2e-16 ***
## x.var       4.045e-03  6.443e-06   627.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 606447  on 763  degrees of freedom
## Residual deviance: 121163  on 762  degrees of freedom
## AIC: 127112
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## AUSTRIA  --  14661 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3086.75  -869.63   -20.32  1267.22  2183.48 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2205.9728   102.5846   -21.5   <2e-16 ***
## x.var          22.4901     0.2323    96.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1416 on 762 degrees of freedom
## Multiple R-squared:  0.9248, Adjusted R-squared:  0.9247 
## F-statistic:  9370 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4958 -0.6837  0.4273  1.0644  1.4756 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0272418  0.1068133   37.70   <2e-16 ***
## x.var       0.0093714  0.0002419   38.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.475 on 762 degrees of freedom
## Multiple R-squared:  0.6632, Adjusted R-squared:  0.6628 
## F-statistic:  1501 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -48.26  -35.20  -18.23   29.37   48.73  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.849e+00  1.471e-03    4656   <2e-16 ***
## x.var       4.040e-03  2.528e-06    1598   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4043301  on 763  degrees of freedom
## Residual deviance:  901555  on 762  degrees of freedom
## AIC: 908683
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## AZERBAIJAN  --  9307 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1285.08  -453.97   -50.67   505.12  1701.70 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1714.8221    48.8045  -35.14   <2e-16 ***
## x.var          13.1220     0.1105  118.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 673.8 on 762 degrees of freedom
## Multiple R-squared:  0.9487, Adjusted R-squared:  0.9486 
## F-statistic: 1.409e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2322 -0.7863  0.3101  1.0453  1.5320 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.6913653  0.0919845   29.26   <2e-16 ***
## x.var       0.0106042  0.0002083   50.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.27 on 762 degrees of freedom
## Multiple R-squared:  0.7727, Adjusted R-squared:  0.7724 
## F-statistic:  2591 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.289  -21.122   -6.347   15.505   26.718  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.770e+00  2.262e-03    2551   <2e-16 ***
## x.var       4.781e-03  3.774e-06    1267   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2402965  on 763  degrees of freedom
## Residual deviance:  287984  on 762  degrees of freedom
## AIC: 294442
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BAHAMAS  --  770 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -151.12  -79.95  -12.66   90.99  151.30 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -147.9738     6.8443  -21.62   <2e-16 ***
## x.var          1.0243     0.0155   66.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 94.5 on 762 degrees of freedom
## Multiple R-squared:  0.8514, Adjusted R-squared:  0.8512 
## F-statistic:  4367 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.91710 -0.29640 -0.08418  0.52224  1.31470 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.3432815  0.0558566   24.05   <2e-16 ***
## x.var       0.0081974  0.0001265   64.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7712 on 762 degrees of freedom
## Multiple R-squared:  0.8464, Adjusted R-squared:  0.8462 
## F-statistic:  4199 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.3042  -4.6952  -0.7996   3.0613   7.1135  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.920e+00  8.812e-03   331.3   <2e-16 ***
## x.var       5.200e-03  1.449e-05   358.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 190955  on 763  degrees of freedom
## Residual deviance:  14115  on 762  degrees of freedom
## AIC: 18802
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BAHRAIN  --  1444 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -268.69 -125.62  -27.69  137.00  355.88 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -285.67652   12.75233  -22.40   <2e-16 ***
## x.var          2.43961    0.02888   84.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 176.1 on 762 degrees of freedom
## Multiple R-squared:  0.9035, Adjusted R-squared:  0.9034 
## F-statistic:  7135 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5708 -0.7571  0.3428  0.7694  1.2539 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1016379  0.0741460   28.34   <2e-16 ***
## x.var       0.0086886  0.0001679   51.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.024 on 762 degrees of freedom
## Multiple R-squared:  0.7784, Adjusted R-squared:  0.7781 
## F-statistic:  2677 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -18.5182  -10.7238   -0.7184    2.6495   18.5159  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.336e+00  4.878e-03   888.9   <2e-16 ***
## x.var       4.437e-03  8.247e-06   538.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 434516  on 763  degrees of freedom
## Residual deviance:  65153  on 762  degrees of freedom
## AIC: 70594
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BANGLADESH  --  28995 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5108.2 -2521.4    46.8  2436.8  5467.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5512.5483   223.6043  -24.65   <2e-16 ***
## x.var          45.1952     0.5064   89.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3087 on 762 degrees of freedom
## Multiple R-squared:  0.9127, Adjusted R-squared:  0.9126 
## F-statistic:  7964 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6096 -0.7626  0.2087  1.3598  1.9944 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0164899  0.1232393   32.59   <2e-16 ***
## x.var       0.0105919  0.0002791   37.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.702 on 762 degrees of freedom
## Multiple R-squared:  0.654,  Adjusted R-squared:  0.6535 
## F-statistic:  1440 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -68.028  -31.860    2.619   15.833   50.300  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.171e+00  1.162e-03    6170   <2e-16 ***
## x.var       4.554e-03  1.956e-06    2328   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7761691  on 763  degrees of freedom
## Residual deviance:  767445  on 762  degrees of freedom
## AIC: 774909
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BARBADOS  --  311 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -74.71 -41.46 -13.63  36.69 135.01 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -58.670219   3.839082  -15.28   <2e-16 ***
## x.var         0.307946   0.008695   35.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 53.01 on 762 degrees of freedom
## Multiple R-squared:  0.6221, Adjusted R-squared:  0.6216 
## F-statistic:  1254 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9071 -0.4112  0.0794  0.3354  1.0000 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.967e-01  3.326e-02   11.93   <2e-16 ***
## x.var       6.896e-03  7.532e-05   91.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4592 on 762 degrees of freedom
## Multiple R-squared:  0.9167, Adjusted R-squared:  0.9166 
## F-statistic:  8383 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.2904  -1.6038   0.1049   1.7728   3.7712  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.0049683  0.0245534   0.202     0.84    
## x.var       0.0076415  0.0000379 201.631   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 74267.1  on 763  degrees of freedom
## Residual deviance:  3410.5  on 762  degrees of freedom
## AIC: 6961.1
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## BELARUS  --  6407 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -483.4 -360.5 -144.6  288.5  995.6 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -997.20561   30.81214  -32.36   <2e-16 ***
## x.var          8.38823    0.06979  120.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425.4 on 762 degrees of freedom
## Multiple R-squared:  0.9499, Adjusted R-squared:  0.9498 
## F-statistic: 1.445e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.761 -0.648  0.380  1.051  1.445 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.130655   0.099336   31.52   <2e-16 ***
## x.var       0.009135   0.000225   40.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.372 on 762 degrees of freedom
## Multiple R-squared:  0.6839, Adjusted R-squared:  0.6835 
## F-statistic:  1649 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -26.3490   -8.5422    0.8757    7.2989   10.6116  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.541e+00  2.655e-03  2087.2   <2e-16 ***
## x.var       4.479e-03  4.481e-06   999.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1377080  on 763  degrees of freedom
## Residual deviance:   96947  on 762  degrees of freedom
## AIC: 103287
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BELGIUM  --  30076 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4658.6 -2414.0   -81.8  2286.9  3776.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2252.4836   183.1537   12.30   <2e-16 ***
## x.var         40.1284     0.4148   96.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2529 on 762 degrees of freedom
## Multiple R-squared:  0.9247, Adjusted R-squared:  0.9246 
## F-statistic:  9358 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4737 -0.7328  0.5618  1.2489  2.1365 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.1074986  0.1468416   41.59   <2e-16 ***
## x.var       0.0074740  0.0003326   22.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.027 on 762 degrees of freedom
## Multiple R-squared:  0.3986, Adjusted R-squared:  0.3978 
## F-statistic:   505 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -117.823   -22.135     1.881    30.064    46.019  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.719e+00  7.152e-04   12191   <2e-16 ***
## x.var       2.405e-03  1.340e-06    1795   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4971854  on 763  degrees of freedom
## Residual deviance: 1481260  on 762  degrees of freedom
## AIC: 1489427
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BELIZE  --  648 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -94.811 -53.019   5.029  54.409 112.960 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.139e+02  4.123e+00  -27.62   <2e-16 ***
## x.var        9.228e-01  9.339e-03   98.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56.93 on 762 degrees of freedom
## Multiple R-squared:  0.9276, Adjusted R-squared:  0.9275 
## F-statistic:  9764 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3824 -0.7574 -0.2590  0.7646  1.7185 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.4769980  0.0670620   7.113 2.62e-12 ***
## x.var       0.0096608  0.0001519  63.606  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9259 on 762 degrees of freedom
## Multiple R-squared:  0.8415, Adjusted R-squared:  0.8413 
## F-statistic:  4046 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -10.705   -7.869   -1.463    3.518   11.401  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.253e+00  8.194e-03   397.0   <2e-16 ***
## x.var       4.589e-03  1.377e-05   333.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 180145  on 763  degrees of freedom
## Residual deviance:  36393  on 762  degrees of freedom
## AIC: 40802
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BENIN  --  163 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24.8727  -7.8211   0.1373   7.3686  24.4138 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -22.757581   0.809447  -28.11   <2e-16 ***
## x.var         0.255014   0.001833  139.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.18 on 762 degrees of freedom
## Multiple R-squared:  0.9621, Adjusted R-squared:  0.9621 
## F-statistic: 1.935e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6174 -0.6103  0.0776  0.5468  1.2550 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1269024  0.0549011   20.53   <2e-16 ***
## x.var       0.0065393  0.0001243   52.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.758 on 762 degrees of freedom
## Multiple R-squared:  0.784,  Adjusted R-squared:  0.7837 
## F-statistic:  2766 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.6736  -2.6653   0.4608   1.6570   3.3124  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.487e+00  1.331e-02   186.8   <2e-16 ***
## x.var       3.881e-03  2.305e-05   168.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 40300.8  on 763  degrees of freedom
## Residual deviance:  5903.2  on 762  degrees of freedom
## AIC: 9912
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BHUTAN  --  6 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.01833 -0.47251 -0.04552  0.44030  2.76031 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -9.660e-01  4.400e-02  -21.95   <2e-16 ***
## x.var        5.527e-03  9.966e-05   55.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6076 on 762 degrees of freedom
## Multiple R-squared:  0.8014, Adjusted R-squared:  0.8011 
## F-statistic:  3075 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.50119 -0.15171  0.02918  0.15567  0.40787 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.912e-01  1.559e-02  -25.09   <2e-16 ***
## x.var        2.535e-03  3.531e-05   71.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2153 on 762 degrees of freedom
## Multiple R-squared:  0.8712, Adjusted R-squared:  0.871 
## F-statistic:  5154 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8919  -0.5262  -0.3088   0.3023   0.9784  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.2426863  0.1532583  -21.16   <2e-16 ***
## x.var        0.0065341  0.0002424   26.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1340.80  on 763  degrees of freedom
## Residual deviance:  202.82  on 762  degrees of freedom
## AIC: 1253.5
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BOLIVIA  --  21406 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2221.83 -1002.57    26.23   996.04  1926.52 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1959.1015    83.7278   -23.4   <2e-16 ***
## x.var          32.5792     0.1896   171.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1156 on 762 degrees of freedom
## Multiple R-squared:  0.9748, Adjusted R-squared:  0.9748 
## F-statistic: 2.952e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.651 -1.127  0.344  1.467  2.491 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.9435575  0.1348353   29.25   <2e-16 ***
## x.var       0.0105508  0.0003054   34.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.862 on 762 degrees of freedom
## Multiple R-squared:  0.6104, Adjusted R-squared:  0.6099 
## F-statistic:  1194 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -74.067  -41.201    9.406   27.591   38.131  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.669e+00  1.060e-03    7237   <2e-16 ***
## x.var       3.443e-03  1.873e-06    1838   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5183900  on 763  degrees of freedom
## Residual deviance: 1232779  on 762  degrees of freedom
## AIC: 1240157
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BOSNIA AND HERZEGOVINA  --  15380 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2210.5  -835.8     5.7   826.9  2857.6 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2879.8281    83.0402  -34.68   <2e-16 ***
## x.var          22.1976     0.1881  118.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1147 on 762 degrees of freedom
## Multiple R-squared:  0.9481, Adjusted R-squared:  0.9481 
## F-statistic: 1.393e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6324 -0.7562  0.5139  0.9802  1.5566 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.9821239  0.0965664   30.88   <2e-16 ***
## x.var       0.0110215  0.0002187   50.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.333 on 762 degrees of freedom
## Multiple R-squared:  0.7692, Adjusted R-squared:  0.7689 
## F-statistic:  2539 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -39.99  -33.86  -11.54   21.31   45.34  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.314e+00  1.730e-03    3650   <2e-16 ***
## x.var       4.755e-03  2.889e-06    1646   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4180242  on 763  degrees of freedom
## Residual deviance:  618603  on 762  degrees of freedom
## AIC: 625397
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BOTSWANA  --  2614 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -668.13 -443.20   31.47  386.16  745.85 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -749.96241   31.54433  -23.77   <2e-16 ***
## x.var          4.11352    0.07144   57.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 435.5 on 762 degrees of freedom
## Multiple R-squared:  0.8131, Adjusted R-squared:  0.8129 
## F-statistic:  3315 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.59191 -0.45021  0.00616  0.59380  1.20700 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.5186435  0.0499943  -10.37   <2e-16 ***
## x.var        0.0130623  0.0001132  115.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6903 on 762 degrees of freedom
## Multiple R-squared:  0.9458, Adjusted R-squared:  0.9458 
## F-statistic: 1.331e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.372  -12.237   -7.949    6.298   24.814  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.037e+00  6.082e-03   499.3   <2e-16 ***
## x.var       7.011e-03  9.513e-06   737.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1008580  on 763  degrees of freedom
## Residual deviance:  117268  on 762  degrees of freedom
## AIC: 121892
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BRAZIL  --  646714 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -71077 -37894 -10148  41092  96015 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -97057.669   3356.629  -28.91   <2e-16 ***
## x.var         1042.985      7.602  137.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 46340 on 762 degrees of freedom
## Multiple R-squared:  0.9611, Adjusted R-squared:  0.961 
## F-statistic: 1.882e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.232 -1.031  0.672  1.769  2.567 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.5731480  0.1776565   37.00   <2e-16 ***
## x.var       0.0119768  0.0004024   29.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.453 on 762 degrees of freedom
## Multiple R-squared:  0.5376, Adjusted R-squared:  0.537 
## F-statistic:   886 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -347.52  -177.23    28.72    84.77   226.59  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.075e+01  2.115e-04   50855   <2e-16 ***
## x.var       3.948e-03  3.650e-07   10816   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 167986154  on 763  degrees of freedom
## Residual deviance:  25184526  on 762  degrees of freedom
## AIC: 25194353
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BRUNEI  --  112 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.451 -21.070  -0.759  17.366  47.396 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -24.07768    1.77483  -13.57   <2e-16 ***
## x.var         0.11623    0.00402   28.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.5 on 762 degrees of freedom
## Multiple R-squared:  0.5232, Adjusted R-squared:  0.5225 
## F-statistic:   836 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5610 -0.5719  0.1010  0.5712  1.1976 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.1803060  0.0545437  -3.306 0.000992 ***
## x.var        0.0053832  0.0001235  43.577  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7531 on 762 degrees of freedom
## Multiple R-squared:  0.7136, Adjusted R-squared:  0.7133 
## F-statistic:  1899 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.7283  -1.6323   0.1875   1.6463   5.0655  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.274e+00  5.189e-02  -43.82   <2e-16 ***
## x.var        9.513e-03  7.768e-05  122.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 35279.1  on 763  degrees of freedom
## Residual deviance:  4475.4  on 762  degrees of freedom
## AIC: 7054.9
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BULGARIA  --  35298 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5383.5 -2136.7  -576.8  2338.6  6987.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7035.5021   222.3968  -31.64   <2e-16 ***
## x.var          48.1298     0.5037   95.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3071 on 762 degrees of freedom
## Multiple R-squared:  0.923,  Adjusted R-squared:  0.9229 
## F-statistic:  9130 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5176 -0.8182  0.4642  0.8962  1.7795 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.9129666  0.0959318   30.36   <2e-16 ***
## x.var       0.0123388  0.0002173   56.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.325 on 762 degrees of freedom
## Multiple R-squared:  0.8089, Adjusted R-squared:  0.8086 
## F-statistic:  3225 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -53.87  -47.89  -12.10   31.95   62.88  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.728e+00  1.300e-03    5174   <2e-16 ***
## x.var       5.258e-03  2.134e-06    2464   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9613745  on 763  degrees of freedom
## Residual deviance: 1229611  on 762  degrees of freedom
## AIC: 1236753
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BURKINA FASO  --  375 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -53.271 -24.011  -5.167  18.756  91.039 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -30.029664   2.312931  -12.98   <2e-16 ***
## x.var         0.419213   0.005238   80.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31.93 on 762 degrees of freedom
## Multiple R-squared:  0.8937, Adjusted R-squared:  0.8935 
## F-statistic:  6404 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5899 -0.3434  0.2427  0.4880  1.0595 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.2880584  0.0598468   38.23   <2e-16 ***
## x.var       0.0053902  0.0001355   39.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8263 on 762 degrees of freedom
## Multiple R-squared:  0.6748, Adjusted R-squared:  0.6744 
## F-statistic:  1581 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.7253  -1.0445   0.0885   1.5058   3.6925  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.194e+00  9.717e-03   328.7   <2e-16 ***
## x.var       3.603e-03  1.704e-05   211.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 58502.6  on 763  degrees of freedom
## Residual deviance:  5559.7  on 762  degrees of freedom
## AIC: 10179
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## BURMA  --  19349 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6911.6 -2450.9   -34.1  3429.3  5267.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5296.5867   262.6224  -20.17   <2e-16 ***
## x.var          29.4748     0.5948   49.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3626 on 762 degrees of freedom
## Multiple R-squared:  0.7632, Adjusted R-squared:  0.7629 
## F-statistic:  2456 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0676 -1.0425 -0.2151  0.9326  2.3079 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.6339165  0.0929594   6.819 1.86e-11 ***
## x.var       0.0147960  0.0002105  70.277  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.283 on 762 degrees of freedom
## Multiple R-squared:  0.8663, Adjusted R-squared:  0.8662 
## F-statistic:  4939 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -72.61  -28.86  -18.30   14.59   57.59  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.128e+00  2.208e-03    2323   <2e-16 ***
## x.var       6.835e-03  3.467e-06    1972   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6958325  on 763  degrees of freedom
## Residual deviance:  686551  on 762  degrees of freedom
## AIC: 692616
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BURUNDI  --  38 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.383  -8.893   1.369   7.361  15.254 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.034941   0.629775  -17.52   <2e-16 ***
## x.var         0.060431   0.001426   42.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.695 on 762 degrees of freedom
## Multiple R-squared:  0.702,  Adjusted R-squared:  0.7016 
## F-statistic:  1795 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74475 -0.36579 -0.03615  0.32228  0.93857 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.446e-01  2.997e-02  -14.83   <2e-16 ***
## x.var        5.670e-03  6.789e-05   83.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4138 on 762 degrees of freedom
## Multiple R-squared:  0.9015, Adjusted R-squared:  0.9014 
## F-statistic:  6976 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.6291  -1.0370  -0.7531   0.1548   4.8507  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.1976811  0.0503440  -23.79   <2e-16 ***
## x.var        0.0070307  0.0000787   89.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 14967.8  on 763  degrees of freedom
## Residual deviance:  1847.2  on 762  degrees of freedom
## AIC: 4243.8
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CABO VERDE  --  401 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.585 -23.299  -3.611  22.102  67.929 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -68.54478    1.86308  -36.79   <2e-16 ***
## x.var         0.61596    0.00422  145.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.72 on 762 degrees of freedom
## Multiple R-squared:  0.9655, Adjusted R-squared:  0.9654 
## F-statistic: 2.131e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6246 -0.7645  0.2267  0.6904  1.2195 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1292263  0.0587900   19.21   <2e-16 ***
## x.var       0.0079897  0.0001332   60.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8117 on 762 degrees of freedom
## Multiple R-squared:  0.8253, Adjusted R-squared:  0.8251 
## F-statistic:  3601 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.5228  -5.2106   0.6763   2.5357   5.9400  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.055e+00  9.435e-03   323.8   <2e-16 ***
## x.var       4.306e-03  1.604e-05   268.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 106340  on 763  degrees of freedom
## Residual deviance:  15425  on 762  degrees of freedom
## AIC: 19879
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CAMBODIA  --  3023 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1058.23  -596.51    75.95   625.09   900.40 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -904.7690    47.2880  -19.13   <2e-16 ***
## x.var          4.3737     0.1071   40.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 652.9 on 762 degrees of freedom
## Multiple R-squared:  0.6864, Adjusted R-squared:  0.686 
## F-statistic:  1668 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4776 -1.0442  0.3753  1.2535  2.4780 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.492456   0.111260  -22.40   <2e-16 ***
## x.var        0.014420   0.000252   57.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.536 on 762 degrees of freedom
## Multiple R-squared:  0.8112, Adjusted R-squared:  0.811 
## F-statistic:  3275 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -36.976  -13.422   -6.673   -2.904   24.052  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.3843883  0.0084116   164.6   <2e-16 ***
## x.var       0.0094692  0.0000126   751.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1312331  on 763  degrees of freedom
## Residual deviance:  156401  on 762  degrees of freedom
## AIC: 159351
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## CAMEROON  --  1920 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -365.25  -74.14   55.62  112.00  240.93 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -243.69810   11.04520  -22.06   <2e-16 ***
## x.var          2.76832    0.02502  110.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 152.5 on 762 degrees of freedom
## Multiple R-squared:  0.9414, Adjusted R-squared:  0.9413 
## F-statistic: 1.225e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4594 -0.4920  0.2902  0.8507  1.6348 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.9841918  0.0901172   33.12   <2e-16 ***
## x.var       0.0075422  0.0002041   36.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.244 on 762 degrees of freedom
## Multiple R-squared:  0.6418, Adjusted R-squared:  0.6414 
## F-statistic:  1366 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -18.3926   -4.4260    0.1825    4.6682   12.2239  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.888e+00  4.022e-03  1215.3   <2e-16 ***
## x.var       3.860e-03  6.969e-06   553.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 420508  on 763  degrees of freedom
## Residual deviance:  48847  on 762  degrees of freedom
## AIC: 54619
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CANADA  --  36255 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2892.3 -1668.0  -260.4  1823.3  3360.2 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -477.958    139.066  -3.437  0.00062 ***
## x.var         47.348      0.315 150.327  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1920 on 762 degrees of freedom
## Multiple R-squared:  0.9674, Adjusted R-squared:  0.9673 
## F-statistic: 2.26e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1014 -0.7640  0.5554  1.1958  2.1733 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.7137188  0.1433064   39.87   <2e-16 ***
## x.var       0.0082475  0.0003246   25.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.979 on 762 degrees of freedom
## Multiple R-squared:  0.4587, Adjusted R-squared:  0.458 
## F-statistic: 645.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -105.677   -23.882     1.412    23.988    50.171  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.473e+00  7.611e-04   11133   <2e-16 ***
## x.var       2.897e-03  1.385e-06    2092   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6265217  on 763  degrees of freedom
## Residual deviance: 1361330  on 762  degrees of freedom
## AIC: 1369423
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CENTRAL AFRICAN REPUBLIC  --  113 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.1518 -10.4045  -0.7386  10.7981  24.3375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.035445   0.948363   6.364 3.39e-10 ***
## x.var       0.154741   0.002148  72.043  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.09 on 762 degrees of freedom
## Multiple R-squared:  0.872,  Adjusted R-squared:  0.8718 
## F-statistic:  5190 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0546 -0.8245  0.1704  0.8172  1.6724 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.3432942  0.0765312   17.55   <2e-16 ***
## x.var       0.0058306  0.0001733   33.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.057 on 762 degrees of freedom
## Multiple R-squared:  0.5976, Adjusted R-squared:  0.597 
## F-statistic:  1132 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.6391  -2.3965   0.7111   2.2508   3.8426  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.067e+00  1.191e-02   257.4   <2e-16 ***
## x.var       2.515e-03  2.217e-05   113.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 23929.7  on 763  degrees of freedom
## Residual deviance:  9890.4  on 762  degrees of freedom
## AIC: 13791
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHAD  --  190 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.656 -17.710   2.969  14.075  33.586 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.370939   1.374721   9.726   <2e-16 ***
## x.var        0.270977   0.003114  87.032   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.98 on 762 degrees of freedom
## Multiple R-squared:  0.9086, Adjusted R-squared:  0.9085 
## F-statistic:  7574 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5986 -0.6711  0.3610  0.8705  1.4447 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0536247  0.0813082   25.26   <2e-16 ***
## x.var       0.0056181  0.0001842   30.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.123 on 762 degrees of freedom
## Multiple R-squared:  0.5498, Adjusted R-squared:  0.5492 
## F-statistic: 930.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -10.050   -2.392    1.183    1.933    4.299  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.685e+00  8.818e-03   417.8   <2e-16 ***
## x.var       2.447e-03  1.648e-05   148.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 36867  on 763  degrees of freedom
## Residual deviance: 12905  on 762  degrees of freedom
## AIC: 17339
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHILE  --  41571 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3795.5 -1463.5  -149.8  1129.6  3913.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3834.9006   145.4042  -26.37   <2e-16 ***
## x.var          63.6552     0.3293  193.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2008 on 762 degrees of freedom
## Multiple R-squared:   0.98,  Adjusted R-squared:   0.98 
## F-statistic: 3.736e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.329 -1.017  0.372  1.540  2.475 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7038546  0.1447868   32.49   <2e-16 ***
## x.var       0.0104141  0.0003279   31.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.999 on 762 degrees of freedom
## Multiple R-squared:  0.5696, Adjusted R-squared:  0.5691 
## F-statistic:  1009 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -102.36   -47.85    22.30    28.00    42.99  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.338e+00  7.584e-04   10994   <2e-16 ***
## x.var       3.445e-03  1.340e-06    2570   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9699399  on 763  degrees of freedom
## Residual deviance: 1976493  on 762  degrees of freedom
## AIC: 1984426
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHINA  --  4972 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3631.9  -284.8   142.9   466.5   794.2 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3645.3537    52.9070   68.90   <2e-16 ***
## x.var          2.2578     0.1198   18.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 730.5 on 762 degrees of freedom
## Multiple R-squared:  0.3178, Adjusted R-squared:  0.3169 
## F-statistic:   355 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0660 -0.0867  0.0758  0.2262  0.3980 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.9553928  0.0381925  208.30   <2e-16 ***
## x.var       0.0010162  0.0000865   11.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5273 on 762 degrees of freedom
## Multiple R-squared:  0.1533, Adjusted R-squared:  0.1522 
## F-statistic:   138 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -84.757   -4.227    2.425    7.334   12.042  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.216e+00  1.134e-03  7246.3   <2e-16 ***
## x.var       5.020e-04  2.452e-06   204.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 200493  on 763  degrees of freedom
## Residual deviance: 158427  on 762  degrees of freedom
## AIC: 166209
## 
## Number of Fisher Scoring iterations: 4
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.95456  -0.07461   0.03087   0.11753   0.20709  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.193e+00  1.422e-02   576.1   <2e-16 ***
## x.var       5.603e-04  3.221e-05    17.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03854589)
## 
##     Null deviance: 107.395  on 763  degrees of freedom
## Residual deviance:  96.975  on 762  degrees of freedom
## AIC: 13362
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## COLOMBIA  --  138364 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -10468  -8228  -5820   8089  23922 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -24146.346    735.003  -32.85   <2e-16 ***
## x.var          224.761      1.665  135.02   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10150 on 762 degrees of freedom
## Multiple R-squared:  0.9599, Adjusted R-squared:  0.9598 
## F-statistic: 1.823e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4253 -1.0728  0.4401  1.6119  2.4399 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6772563  0.1477640   31.65   <2e-16 ***
## x.var       0.0124672  0.0003347   37.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.04 on 762 degrees of freedom
## Multiple R-squared:  0.6455, Adjusted R-squared:  0.6451 
## F-statistic:  1388 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -161.32  -123.48    14.65    58.23   113.27  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.015e+00  4.851e-04   18585   <2e-16 ***
## x.var       4.224e-03  8.273e-07    5107   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 38987573  on 763  degrees of freedom
## Residual deviance:  6353567  on 762  degrees of freedom
## AIC: 6362080
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COMOROS  --  160 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -59.819 -20.659  -2.541  22.762  60.930 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -32.424341   2.209240  -14.68   <2e-16 ***
## x.var         0.290186   0.005004   57.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.5 on 762 degrees of freedom
## Multiple R-squared:  0.8153, Adjusted R-squared:  0.8151 
## F-statistic:  3363 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2596 -0.5756 -0.1735  0.5116  1.5910 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.1934276  0.0536500   3.605 0.000332 ***
## x.var       0.0080465  0.0001215  66.221  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7407 on 762 degrees of freedom
## Multiple R-squared:  0.852,  Adjusted R-squared:  0.8518 
## F-statistic:  4385 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -7.163  -5.037  -3.828   3.188   9.909  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.295e+00  1.378e-02   166.6   <2e-16 ***
## x.var       4.316e-03  2.341e-05   184.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 64000  on 763  degrees of freedom
## Residual deviance: 21085  on 762  degrees of freedom
## AIC: 24756
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## CONGO (BRAZZAVILLE)  --  377 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -61.890 -30.389   0.134  17.573  78.906 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -43.066292   2.636988  -16.33   <2e-16 ***
## x.var         0.473612   0.005972   79.30   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.41 on 762 degrees of freedom
## Multiple R-squared:  0.8919, Adjusted R-squared:  0.8918 
## F-statistic:  6288 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.26086 -0.43501  0.05458  0.65674  1.45829 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8056734  0.0621722   29.04   <2e-16 ***
## x.var       0.0064111  0.0001408   45.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8584 on 762 degrees of freedom
## Multiple R-squared:  0.7312, Adjusted R-squared:  0.7309 
## F-statistic:  2073 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.5967  -2.4749   0.3628   2.1045   7.1058  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.085e+00  9.837e-03   313.6   <2e-16 ***
## x.var       3.910e-03  1.701e-05   229.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 72398.3  on 763  degrees of freedom
## Residual deviance:  8092.7  on 762  degrees of freedom
## AIC: 12612
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CONGO (KINSHASA)  --  1335 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -140.703  -34.229   -3.924   51.707  136.231 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -138.16066    4.53647  -30.46   <2e-16 ***
## x.var          1.92925    0.01027  187.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 62.63 on 762 degrees of freedom
## Multiple R-squared:  0.9788, Adjusted R-squared:  0.9788 
## F-statistic: 3.526e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1918 -0.5536  0.2398  0.8826  1.2315 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.7540231  0.0820584   33.56   <2e-16 ***
## x.var       0.0074203  0.0001859   39.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.133 on 762 degrees of freedom
## Multiple R-squared:  0.6766, Adjusted R-squared:  0.6762 
## F-statistic:  1594 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -16.6642   -7.2545   -0.4905    5.8207   10.6211  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.721e+00  4.529e-03  1042.3   <2e-16 ***
## x.var       3.602e-03  7.944e-06   453.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 292367  on 763  degrees of freedom
## Residual deviance:  48737  on 762  degrees of freedom
## AIC: 54304
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COSTA RICA  --  7969 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -802.54 -478.27  -98.19  421.08 1558.24 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.570e+03  4.265e+01  -36.82   <2e-16 ***
## x.var        1.199e+01  9.659e-02  124.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 588.8 on 762 degrees of freedom
## Multiple R-squared:  0.9529, Adjusted R-squared:  0.9528 
## F-statistic: 1.541e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.58597 -1.12164 -0.00579  1.31844  1.97898 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9129848  0.0975333   19.61   <2e-16 ***
## x.var       0.0118068  0.0002209   53.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.347 on 762 degrees of freedom
## Multiple R-squared:  0.7894, Adjusted R-squared:  0.7892 
## F-statistic:  2857 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -33.452  -25.825    4.774   12.152   19.235  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.674e+00  2.370e-03    2394   <2e-16 ***
## x.var       4.788e-03  3.954e-06    1211   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2219893  on 763  degrees of freedom
## Residual deviance:  284739  on 762  degrees of freedom
## AIC: 290938
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COTE D'IVOIRE  --  791 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -132.59  -88.85   15.71   86.80  141.53 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -135.77902    6.38167  -21.28   <2e-16 ***
## x.var          1.07093    0.01445   74.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 88.11 on 762 degrees of freedom
## Multiple R-squared:  0.8781, Adjusted R-squared:  0.878 
## F-statistic:  5490 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4397 -0.2910  0.1755  0.5914  1.2718 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9448917  0.0653961   29.74   <2e-16 ***
## x.var       0.0073853  0.0001481   49.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9029 on 762 degrees of freedom
## Multiple R-squared:  0.7654, Adjusted R-squared:  0.7651 
## F-statistic:  2486 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.7846  -2.7241   0.2216   2.2152   5.6184  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.340e+00  7.747e-03   431.1   <2e-16 ***
## x.var       4.676e-03  1.298e-05   360.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 180304  on 763  degrees of freedom
## Residual deviance:  10846  on 762  degrees of freedom
## AIC: 15765
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## CROATIA  --  14942 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2408.19  -879.77    77.53   794.68  2670.61 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2690.4682    88.3579  -30.45   <2e-16 ***
## x.var          19.8620     0.2001   99.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1220 on 762 degrees of freedom
## Multiple R-squared:  0.9282, Adjusted R-squared:  0.9281 
## F-statistic:  9851 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1587 -0.8317  0.2422  0.8719  1.8009 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5013925  0.0899435   27.81   <2e-16 ***
## x.var       0.0115308  0.0002037   56.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.242 on 762 degrees of freedom
## Multiple R-squared:  0.8079, Adjusted R-squared:  0.8076 
## F-statistic:  3204 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -39.53  -32.84  -14.93   26.25   40.60  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.087e+00  1.891e-03    3219   <2e-16 ***
## x.var       4.917e-03  3.139e-06    1566   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3951401  on 763  degrees of freedom
## Residual deviance:  675067  on 762  degrees of freedom
## AIC: 681644
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CUBA  --  8494 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2718.84 -1820.67    89.25  1762.84  2762.64 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2443.8718   133.1772  -18.35   <2e-16 ***
## x.var          12.2437     0.3016   40.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1839 on 762 degrees of freedom
## Multiple R-squared:  0.6838, Adjusted R-squared:  0.6834 
## F-statistic:  1648 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.21575 -0.32312 -0.09827  0.51643  1.55048 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.603389   0.057826   27.73   <2e-16 ***
## x.var       0.010935   0.000131   83.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7984 on 762 degrees of freedom
## Multiple R-squared:  0.9015, Adjusted R-squared:  0.9013 
## F-statistic:  6971 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -54.558  -15.027   -6.973    1.119   45.204  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.066e+00  4.437e-03   691.1   <2e-16 ***
## x.var       8.534e-03  6.740e-06  1266.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3300360  on 763  degrees of freedom
## Residual deviance:  268859  on 762  degrees of freedom
## AIC: 274575
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CYPRUS  --  833 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -131.82  -49.53  -10.89   45.69  172.29 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -157.66103    5.18690  -30.40   <2e-16 ***
## x.var          1.07116    0.01175   91.18   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 71.61 on 762 degrees of freedom
## Multiple R-squared:  0.916,  Adjusted R-squared:  0.9159 
## F-statistic:  8314 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7963 -0.3464  0.1015  0.5494  0.9100 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2722122  0.0479332   26.54   <2e-16 ***
## x.var       0.0083183  0.0001086   76.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6618 on 762 degrees of freedom
## Multiple R-squared:  0.8851, Adjusted R-squared:  0.885 
## F-statistic:  5871 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -7.476  -5.716  -3.267   4.419   7.160  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.898e+00  8.777e-03   330.2   <2e-16 ***
## x.var       5.293e-03  1.439e-05   367.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 207374  on 763  degrees of freedom
## Residual deviance:  19782  on 762  degrees of freedom
## AIC: 24465
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CZECHIA  --  38403 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8275.8 -2747.9  -744.5  3541.1  7456.6 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7303.7951   311.5465  -23.44   <2e-16 ***
## x.var          62.6861     0.7056   88.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4301 on 762 degrees of freedom
## Multiple R-squared:  0.912,  Adjusted R-squared:  0.9118 
## F-statistic:  7892 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0845 -0.8793  0.3181  1.1589  1.8353 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.3503982  0.1119003   29.94   <2e-16 ***
## x.var       0.0122356  0.0002534   48.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.545 on 762 degrees of freedom
## Multiple R-squared:  0.7536, Adjusted R-squared:  0.7533 
## F-statistic:  2331 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -90.28  -70.23  -33.78   45.01  100.64  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.592e+00  9.595e-04    7913   <2e-16 ***
## x.var       4.423e-03  1.623e-06    2725   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12678186  on 763  degrees of freedom
## Residual deviance:  3211959  on 762  degrees of freedom
## AIC: 3219390
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## DENMARK  --  4463 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -526.33 -250.01    0.81  200.85  820.31 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -248.12216   22.02860  -11.26   <2e-16 ***
## x.var          5.09269    0.04989  102.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 304.1 on 762 degrees of freedom
## Multiple R-squared:  0.9319, Adjusted R-squared:  0.9318 
## F-statistic: 1.042e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3845 -0.5817  0.5191  0.8888  1.4843 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0211175  0.1003161   40.08   <2e-16 ***
## x.var       0.0069888  0.0002272   30.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.385 on 762 degrees of freedom
## Multiple R-squared:  0.5539, Adjusted R-squared:  0.5533 
## F-statistic: 946.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.796   -8.017   -3.657    6.491   23.329  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.924e+00  2.584e-03  2292.3   <2e-16 ***
## x.var       3.300e-03  4.602e-06   717.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 728870  on 763  degrees of freedom
## Residual deviance: 134553  on 762  degrees of freedom
## AIC: 140977
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## DIAMOND PRINCESS  --  13 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.8759 -0.7297  0.4320  1.5619  2.7554 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.6915901  0.1815327   53.39   <2e-16 ***
## x.var       0.0063567  0.0004111   15.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.506 on 762 degrees of freedom
## Multiple R-squared:  0.2388, Adjusted R-squared:  0.2378 
## F-statistic:   239 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.13701 -0.10685  0.07559  0.26316  0.44252 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.107e+00  3.443e-02   61.20   <2e-16 ***
## x.var       1.026e-03  7.799e-05   13.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4754 on 762 degrees of freedom
## Multiple R-squared:  0.1852, Adjusted R-squared:  0.1841 
## F-statistic: 173.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.4720  -0.2050   0.1424   0.4699   0.8051  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.2872909  0.0219191  104.35   <2e-16 ***
## x.var       0.0005258  0.0000473   11.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 909.21  on 763  degrees of freedom
## Residual deviance: 785.18  on 762  degrees of freedom
## AIC: 4001.6
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## DJIBOUTI  --  189 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.039 -10.079   2.935  12.241  29.404 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -15.701602   1.274579  -12.32   <2e-16 ***
## x.var         0.288441   0.002887   99.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.6 on 762 degrees of freedom
## Multiple R-squared:  0.9291, Adjusted R-squared:  0.929 
## F-statistic:  9984 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0774 -0.5537  0.1240  0.5894  1.4424 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.5961824  0.0655190   24.36   <2e-16 ***
## x.var       0.0060913  0.0001484   41.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9046 on 762 degrees of freedom
## Multiple R-squared:  0.6886, Adjusted R-squared:  0.6882 
## F-statistic:  1685 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.2350  -2.2091   0.2449   2.1953   4.7660  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.998e+00  1.106e-02   271.2   <2e-16 ***
## x.var       3.370e-03  1.962e-05   171.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 43099.6  on 763  degrees of freedom
## Residual deviance:  8790.7  on 762  degrees of freedom
## AIC: 13035
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## DOMINICA  --  57 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.4491 -10.0228  -0.4595   8.3442  29.0689 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.998844   0.846088  -14.18   <2e-16 ***
## x.var         0.052678   0.001916   27.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.68 on 762 degrees of freedom
## Multiple R-squared:  0.4979, Adjusted R-squared:  0.4973 
## F-statistic: 755.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.78214 -0.86107  0.03507  0.87637  1.44561 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.0994714  0.0718881  -15.29   <2e-16 ***
## x.var        0.0049855  0.0001628   30.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9925 on 762 degrees of freedom
## Multiple R-squared:  0.5517, Adjusted R-squared:  0.5511 
## F-statistic: 937.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8686  -1.1533  -0.3302  -0.0859   3.7124  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -6.7811707  0.1312931  -51.65   <2e-16 ***
## x.var        0.0147858  0.0001875   78.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 19491.0  on 763  degrees of freedom
## Residual deviance:  1748.7  on 762  degrees of freedom
## AIC: 2701.2
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## DOMINICAN REPUBLIC  --  4360 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -713.65 -334.68   47.02  325.75  450.44 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.16644   24.31594   0.377    0.706    
## x.var        6.62890    0.05507 120.368   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 335.7 on 762 degrees of freedom
## Multiple R-squared:   0.95,  Adjusted R-squared:   0.95 
## F-statistic: 1.449e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6152 -0.6642  0.4723  1.0843  1.6291 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.207146   0.113462   37.08   <2e-16 ***
## x.var       0.007419   0.000257   28.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.567 on 762 degrees of freedom
## Multiple R-squared:  0.5224, Adjusted R-squared:  0.5218 
## F-statistic: 833.5 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -41.169  -16.407    3.818   13.359   17.392  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.588e+00  1.978e-03  3331.6   <2e-16 ***
## x.var       2.797e-03  3.619e-06   772.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 920640  on 763  degrees of freedom
## Residual deviance: 256142  on 762  degrees of freedom
## AIC: 262828
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ECUADOR  --  35172 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3588.5 -1701.8  -345.3  1435.6  5505.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3574.641    159.379  -22.43   <2e-16 ***
## x.var          53.433      0.361  148.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2201 on 762 degrees of freedom
## Multiple R-squared:  0.9664, Adjusted R-squared:  0.9663 
## F-statistic: 2.191e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6290 -0.7095  0.3241  1.5350  1.9948 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.1491258  0.1361228   37.83   <2e-16 ***
## x.var       0.0092281  0.0003083   29.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 762 degrees of freedom
## Multiple R-squared:  0.5404, Adjusted R-squared:  0.5398 
## F-statistic:   896 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -89.379  -27.609    7.337   24.630   52.356  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.093e+00  8.465e-04    9560   <2e-16 ***
## x.var       3.535e-03  1.490e-06    2373   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7875975  on 763  degrees of freedom
## Residual deviance: 1240904  on 762  degrees of freedom
## AIC: 1248845
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EGYPT  --  23889 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1617.33  -659.42    12.67   579.52  2325.69 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2358.8457    58.5805  -40.27   <2e-16 ***
## x.var          33.1528     0.1327  249.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 808.8 on 762 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9879 
## F-statistic: 6.244e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1119 -0.8092  0.4447  1.1462  2.0326 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.690584   0.124532   37.67   <2e-16 ***
## x.var       0.009160   0.000282   32.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.719 on 762 degrees of freedom
## Multiple R-squared:  0.5806, Adjusted R-squared:   0.58 
## F-statistic:  1055 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -68.511  -25.348    8.246   17.654   31.614  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.570e+00  1.091e-03    6940   <2e-16 ***
## x.var       3.596e-03  1.914e-06    1879   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4952882  on 763  degrees of freedom
## Residual deviance:  773248  on 762  degrees of freedom
## AIC: 780827
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EL SALVADOR  --  4054 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -229.37 -143.26  -49.15   76.90  581.85 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -587.86920   13.27698  -44.28   <2e-16 ***
## x.var          6.02133    0.03007  200.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 183.3 on 762 degrees of freedom
## Multiple R-squared:  0.9814, Adjusted R-squared:  0.9813 
## F-statistic: 4.01e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2686 -0.9019  0.1378  1.2214  1.8319 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.6037965  0.1004084   25.93   <2e-16 ***
## x.var       0.0096349  0.0002274   42.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.386 on 762 degrees of freedom
## Multiple R-squared:  0.702,  Adjusted R-squared:  0.7016 
## F-statistic:  1795 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -26.009  -15.685    3.312    7.092   14.869  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.537e+00  2.837e-03  1951.8   <2e-16 ***
## x.var       4.031e-03  4.879e-06   826.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 983971  on 763  degrees of freedom
## Residual deviance: 144368  on 762  degrees of freedom
## AIC: 150447
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EQUATORIAL GUINEA  --  182 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.453  -7.374  -2.316   7.116  37.627 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.914745   0.960574  -1.993   0.0466 *  
## x.var        0.246292   0.002176 113.208   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.26 on 762 degrees of freedom
## Multiple R-squared:  0.9439, Adjusted R-squared:  0.9438 
## F-statistic: 1.282e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1771 -0.5601  0.1040  0.8018  1.6498 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.6328945  0.0723434   22.57   <2e-16 ***
## x.var       0.0059798  0.0001638   36.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9988 on 762 degrees of freedom
## Multiple R-squared:  0.6361, Adjusted R-squared:  0.6356 
## F-statistic:  1332 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.1090  -1.7021   0.2707   1.5275   5.2364  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.231e+00  1.049e-02   308.0   <2e-16 ***
## x.var       2.876e-03  1.911e-05   150.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 35471  on 763  degrees of freedom
## Residual deviance: 10132  on 762  degrees of freedom
## AIC: 14352
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ERITREA  --  103 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.946 -12.199  -3.388   9.182  43.418 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -21.291972   1.069369  -19.91   <2e-16 ***
## x.var         0.107402   0.002422   44.34   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.76 on 762 degrees of freedom
## Multiple R-squared:  0.7207, Adjusted R-squared:  0.7204 
## F-statistic:  1966 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4388 -0.1279  0.0898  0.3016  1.0767 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.0842041  0.0383709  -28.26   <2e-16 ***
## x.var        0.0075313  0.0000869   86.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5298 on 762 degrees of freedom
## Multiple R-squared:  0.9079, Adjusted R-squared:  0.9078 
## F-statistic:  7510 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6329  -1.2816  -0.6451   0.3797   2.4823  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.566e+00  4.638e-02  -33.76   <2e-16 ***
## x.var        8.384e-03  7.063e-05  118.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 27540.4  on 763  degrees of freedom
## Residual deviance:  1251.6  on 762  degrees of freedom
## AIC: 3390.9
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ESTONIA  --  2188 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -410.58 -164.85   26.56  185.85  439.28 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -442.34082   16.09725  -27.48   <2e-16 ***
## x.var          3.06130    0.03646   83.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 222.3 on 762 degrees of freedom
## Multiple R-squared:  0.9025, Adjusted R-squared:  0.9023 
## F-statistic:  7051 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5282 -0.4564  0.1566  0.7385  1.1690 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9615700  0.0663085   29.58   <2e-16 ***
## x.var       0.0089938  0.0001502   59.89   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9155 on 762 degrees of freedom
## Multiple R-squared:  0.8248, Adjusted R-squared:  0.8245 
## F-statistic:  3586 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -14.586  -10.352   -4.546    3.883   19.427  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.014e+00  5.099e-03   787.2   <2e-16 ***
## x.var       5.201e-03  8.382e-06   620.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 604234  on 763  degrees of freedom
## Residual deviance:  75637  on 762  degrees of freedom
## AIC: 81049
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ESWATINI  --  1390 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -286.13 -105.67   21.49   89.14  296.36 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -298.52562   10.03208  -29.76   <2e-16 ***
## x.var          2.16267    0.02272   95.18   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 138.5 on 762 degrees of freedom
## Multiple R-squared:  0.9224, Adjusted R-squared:  0.9223 
## F-statistic:  9060 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.81344 -0.99598  0.00191  0.91068  1.52747 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9347213  0.0714655   13.08   <2e-16 ***
## x.var       0.0103379  0.0001619   63.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9867 on 762 degrees of freedom
## Multiple R-squared:  0.8426, Adjusted R-squared:  0.8424 
## F-statistic:  4079 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -15.644  -10.039   -4.340    5.331   15.622  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.814e+00  5.821e-03   655.2   <2e-16 ***
## x.var       4.994e-03  9.638e-06   518.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 421151  on 763  degrees of freedom
## Residual deviance:  59819  on 762  degrees of freedom
## AIC: 64779
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ETHIOPIA  --  7446 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -737.1 -407.0 -183.7  490.6 1349.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.360e+03  3.716e+01  -36.61   <2e-16 ***
## x.var        1.100e+01  8.417e-02  130.68   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 513.1 on 762 degrees of freedom
## Multiple R-squared:  0.9573, Adjusted R-squared:  0.9572 
## F-statistic: 1.708e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9748 -1.2892  0.2569  1.2045  2.1107 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1337189  0.1058905   20.15   <2e-16 ***
## x.var       0.0113664  0.0002398   47.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.462 on 762 degrees of freedom
## Multiple R-squared:  0.7467, Adjusted R-squared:  0.7464 
## F-statistic:  2246 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -32.156  -24.440    3.256    9.794   21.291  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.726e+00  2.377e-03    2409   <2e-16 ***
## x.var       4.596e-03  3.994e-06    1151   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1950214  on 763  degrees of freedom
## Residual deviance:  234695  on 762  degrees of freedom
## AIC: 240906
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## FIJI  --  826 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -313.96 -155.05   22.09  173.86  250.74 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -223.11640   12.82035  -17.40   <2e-16 ***
## x.var          1.05237    0.02904   36.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 177 on 762 degrees of freedom
## Multiple R-squared:  0.6329, Adjusted R-squared:  0.6324 
## F-statistic:  1314 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2734 -0.8439  0.1971  0.9565  1.6031 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.6137506  0.0824194  -19.58   <2e-16 ***
## x.var        0.0106079  0.0001867   56.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.138 on 762 degrees of freedom
## Multiple R-squared:  0.8091, Adjusted R-squared:  0.8088 
## F-statistic:  3229 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -16.788   -6.738   -2.357   -1.254   15.794  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -6.290e-01  1.901e-02  -33.08   <2e-16 ***
## x.var        1.031e-02  2.818e-05  365.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 336502  on 763  degrees of freedom
## Residual deviance:  43872  on 762  degrees of freedom
## AIC: 46691
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## FINLAND  --  2351 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -194.61 -116.63  -28.14   76.33  699.11 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -152.14374   11.26791  -13.50   <2e-16 ***
## x.var          2.36130    0.02552   92.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 155.6 on 762 degrees of freedom
## Multiple R-squared:  0.9183, Adjusted R-squared:  0.9182 
## F-statistic:  8561 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8396 -0.5118  0.3802  0.6546  1.4285 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5551556  0.0859255   41.38   <2e-16 ***
## x.var       0.0063220  0.0001946   32.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.186 on 762 degrees of freedom
## Multiple R-squared:  0.5807, Adjusted R-squared:  0.5801 
## F-statistic:  1055 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -18.6423   -2.8663   -0.1487    4.0417    7.4494  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.000e+00  3.993e-03  1252.2   <2e-16 ***
## x.var       3.501e-03  7.038e-06   497.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 323623  on 763  degrees of freedom
## Residual deviance:  32943  on 762  degrees of freedom
## AIC: 38825
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## FRANCE  --  138502 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16688.0  -8060.6   -241.3   7422.6  17019.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3057.772    671.829  -4.551  6.2e-06 ***
## x.var         195.665      1.522 128.592  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9276 on 762 degrees of freedom
## Multiple R-squared:  0.9559, Adjusted R-squared:  0.9559 
## F-statistic: 1.654e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3814 -0.6759  0.6579  1.1356  2.1109 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.1863956  0.1496037   48.04   <2e-16 ***
## x.var       0.0081257  0.0003388   23.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.066 on 762 degrees of freedom
## Multiple R-squared:  0.4301, Adjusted R-squared:  0.4294 
## F-statistic: 575.1 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -208.348   -43.780    -3.989    55.766   103.266  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.851e+00  3.797e-04   25941   <2e-16 ***
## x.var       2.948e-03  6.890e-07    4279   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 25953651  on 763  degrees of freedom
## Residual deviance:  5357864  on 762  degrees of freedom
## AIC: 5367085
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GABON  --  303 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.080 -21.334  -5.194  18.870  45.870 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -43.205681   1.878272  -23.00   <2e-16 ***
## x.var         0.408781   0.004254   96.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.93 on 762 degrees of freedom
## Multiple R-squared:  0.9238, Adjusted R-squared:  0.9237 
## F-statistic:  9234 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9098 -0.3476  0.2142  0.4952  1.2269 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.5301123  0.0561623   27.24   <2e-16 ***
## x.var       0.0065456  0.0001272   51.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7754 on 762 degrees of freedom
## Multiple R-squared:  0.7765, Adjusted R-squared:  0.7763 
## F-statistic:  2648 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.2569  -1.3308   0.2676   1.6112   3.1166  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.731e+00  1.128e-02   242.1   <2e-16 ***
## x.var       4.189e-03  1.927e-05   217.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 64346.5  on 763  degrees of freedom
## Residual deviance:  5426.1  on 762  degrees of freedom
## AIC: 9780.6
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GAMBIA  --  365 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -57.205 -23.244   1.102  25.559  55.641 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -53.299761   2.168712  -24.58   <2e-16 ***
## x.var         0.548246   0.004912  111.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29.94 on 762 degrees of freedom
## Multiple R-squared:  0.9424, Adjusted R-squared:  0.9423 
## F-statistic: 1.246e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.45972 -0.95201 -0.08487  0.79767  1.84751 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9221502  0.0716999   12.86   <2e-16 ***
## x.var       0.0082048  0.0001624   50.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9899 on 762 degrees of freedom
## Multiple R-squared:  0.7701, Adjusted R-squared:  0.7698 
## F-statistic:  2553 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.6927  -6.8790   0.7016   2.8268   5.6616  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.147e+00  9.386e-03   335.3   <2e-16 ***
## x.var       4.024e-03  1.615e-05   249.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 94424  on 763  degrees of freedom
## Residual deviance: 18104  on 762  degrees of freedom
## AIC: 22442
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GEORGIA  --  15981 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -1991  -1616   -773   1458   4139 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3358.1370   130.8714  -25.66   <2e-16 ***
## x.var          19.8951     0.2964   67.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1807 on 762 degrees of freedom
## Multiple R-squared:  0.8553, Adjusted R-squared:  0.8551 
## F-statistic:  4505 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8673 -0.9181 -0.1976  0.8935  2.1707 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.8486867  0.0828137   10.25   <2e-16 ***
## x.var       0.0139530  0.0001876   74.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.143 on 762 degrees of freedom
## Multiple R-squared:  0.879,  Adjusted R-squared:  0.8788 
## F-statistic:  5534 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -37.723  -24.603   -1.826   11.547   28.707  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.175e+00  2.413e-03    2145   <2e-16 ***
## x.var       6.207e-03  3.849e-06    1612   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4296983  on 763  degrees of freedom
## Residual deviance:  353978  on 762  degrees of freedom
## AIC: 359964
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GERMANY  --  122160 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24656.3  -6634.9   -262.9  10324.9  18211.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -18397.52     847.56  -21.71   <2e-16 ***
## x.var          185.88       1.92   96.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11700 on 762 degrees of freedom
## Multiple R-squared:  0.9248, Adjusted R-squared:  0.9247 
## F-statistic:  9376 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2708 -0.7608  0.6714  1.2331  2.0394 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.7977487  0.1400486   41.40   <2e-16 ***
## x.var       0.0100648  0.0003172   31.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.934 on 762 degrees of freedom
## Multiple R-squared:  0.5692, Adjusted R-squared:  0.5686 
## F-statistic:  1007 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -137.88   -84.13   -47.08    68.36   140.63  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.948e+00  5.136e-04   17423   <2e-16 ***
## x.var       4.057e-03  8.822e-07    4598   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 32256554  on 763  degrees of freedom
## Residual deviance:  6196759  on 762  degrees of freedom
## AIC: 6205450
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GHANA  --  1442 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -195.140  -42.613    6.651   55.051  197.310 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -199.34782    5.81302  -34.29   <2e-16 ***
## x.var          2.03736    0.01317  154.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80.26 on 762 degrees of freedom
## Multiple R-squared:  0.9692, Adjusted R-squared:  0.9691 
## F-statistic: 2.395e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8726 -0.6618  0.1648  0.7850  1.4726 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.4009015  0.0784199   30.62   <2e-16 ***
## x.var       0.0079947  0.0001776   45.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.083 on 762 degrees of freedom
## Multiple R-squared:  0.7267, Adjusted R-squared:  0.7263 
## F-statistic:  2026 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -14.7460   -6.4656    0.7906    3.8605   10.7462  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.451e+00  4.882e-03   911.7   <2e-16 ***
## x.var       4.035e-03  8.394e-06   480.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 324734  on 763  degrees of freedom
## Residual deviance:  40401  on 762  degrees of freedom
## AIC: 45863
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GREECE  --  25538 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3901.0 -1607.3  -439.8  1291.0  5287.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4949.2924   159.6991  -30.99   <2e-16 ***
## x.var          32.9837     0.3617   91.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2205 on 762 degrees of freedom
## Multiple R-squared:  0.9161, Adjusted R-squared:  0.9159 
## F-statistic:  8316 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5194 -0.6147  0.2166  0.8327  1.5693 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.9571236  0.0869008   34.03   <2e-16 ***
## x.var       0.0114757  0.0001968   58.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.2 on 762 degrees of freedom
## Multiple R-squared:  0.8169, Adjusted R-squared:  0.8167 
## F-statistic:  3400 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -45.06  -35.23  -16.35   26.49   49.22  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.252e+00  1.614e-03    3875   <2e-16 ***
## x.var       5.396e-03  2.636e-06    2047   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6666014  on 763  degrees of freedom
## Residual deviance:  800401  on 762  degrees of freedom
## AIC: 807328
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GRENADA  --  214 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -91.392 -47.804  -1.408  44.972  90.380 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -56.58275    3.99589  -14.16   <2e-16 ***
## x.var         0.25379    0.00905   28.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.17 on 762 degrees of freedom
## Multiple R-squared:  0.5079, Adjusted R-squared:  0.5072 
## F-statistic: 786.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1996 -0.9695 -0.1233  1.1858  2.0293 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.4831355  0.0879201  -16.87   <2e-16 ***
## x.var        0.0074546  0.0001991   37.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.214 on 762 degrees of freedom
## Multiple R-squared:  0.6478, Adjusted R-squared:  0.6473 
## F-statistic:  1401 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -10.1352   -2.7428   -1.0004   -0.3484   11.6045  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.913e+00  5.108e-02   -76.6   <2e-16 ***
## x.var        1.296e-02  7.385e-05   175.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 93149  on 763  degrees of freedom
## Residual deviance: 13144  on 762  degrees of freedom
## AIC: 14829
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## GUATEMALA  --  16861 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1479.7  -921.4  -251.7   825.5  2723.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2748.2543    78.8118  -34.87   <2e-16 ***
## x.var          24.8881     0.1785  139.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1088 on 762 degrees of freedom
## Multiple R-squared:  0.9623, Adjusted R-squared:  0.9622 
## F-statistic: 1.944e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8262 -1.1042  0.1545  1.4412  2.2661 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2309526  0.1223312   26.41   <2e-16 ***
## x.var       0.0110227  0.0002771   39.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.689 on 762 degrees of freedom
## Multiple R-squared:  0.675,  Adjusted R-squared:  0.6746 
## F-statistic:  1583 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -50.556  -27.326    9.387   13.495   20.988  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.768e+00  1.478e-03    4578   <2e-16 ***
## x.var       4.288e-03  2.515e-06    1705   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4135072  on 763  degrees of freedom
## Residual deviance:  475111  on 762  degrees of freedom
## AIC: 482094
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUINEA  --  440 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -80.176 -59.290   8.288  43.336  80.720 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -80.41503    3.69124  -21.79   <2e-16 ***
## x.var         0.61628    0.00836   73.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 50.96 on 762 degrees of freedom
## Multiple R-squared:  0.877,  Adjusted R-squared:  0.8769 
## F-statistic:  5434 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0657 -0.2833  0.1224  0.6280  1.0475 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.4597829  0.0575463   25.37   <2e-16 ***
## x.var       0.0072137  0.0001303   55.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7945 on 762 degrees of freedom
## Multiple R-squared:  0.8008, Adjusted R-squared:  0.8005 
## F-statistic:  3063 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.7182  -1.5563  -0.0623   1.5369   5.9288  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.7153776  0.0104265   260.4   <2e-16 ***
## x.var       0.0047753  0.0000174   274.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 106559.2  on 763  degrees of freedom
## Residual deviance:   7322.8  on 762  degrees of freedom
## AIC: 11790
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## GUINEA-BISSAU  --  166 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.514 -13.440   3.072  12.376  23.077 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -20.805401   1.049627  -19.82   <2e-16 ***
## x.var         0.220553   0.002377   92.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.49 on 762 degrees of freedom
## Multiple R-squared:  0.9187, Adjusted R-squared:  0.9186 
## F-statistic:  8607 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.73538 -0.39773 -0.00138  0.59861  1.11093 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1472019  0.0548245   20.93   <2e-16 ***
## x.var       0.0061913  0.0001242   49.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7569 on 762 degrees of freedom
## Multiple R-squared:  0.7654, Adjusted R-squared:  0.7651 
## F-statistic:  2486 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.3275  -1.4395   0.2591   1.3976   2.6844  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.275e+00  1.462e-02   155.7   <2e-16 ***
## x.var       3.971e-03  2.521e-05   157.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 34390.3  on 763  degrees of freedom
## Residual deviance:  4040.1  on 762  degrees of freedom
## AIC: 7939.2
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUYANA  --  1215 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -204.63 -111.73  -50.84  139.56  258.74 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -256.46245   10.25212  -25.02   <2e-16 ***
## x.var          1.58941    0.02322   68.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.5 on 762 degrees of freedom
## Multiple R-squared:  0.8601, Adjusted R-squared:  0.8599 
## F-statistic:  4686 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7356 -0.2495  0.0722  0.3514  1.0316 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.290292   0.046808   27.57   <2e-16 ***
## x.var       0.008906   0.000106   84.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6463 on 762 degrees of freedom
## Multiple R-squared:  0.9026, Adjusted R-squared:  0.9024 
## F-statistic:  7058 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -9.6953  -5.2606   0.4658   2.7641   4.5423  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.8975030  0.0080088   361.8   <2e-16 ***
## x.var       0.0058519  0.0000129   453.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 313882  on 763  degrees of freedom
## Residual deviance:  12689  on 762  degrees of freedom
## AIC: 17570
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## HAITI  --  820 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -165.67  -42.14   24.14   52.86  153.01 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -98.42037    5.28017  -18.64   <2e-16 ***
## x.var         1.10703    0.01196   92.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 72.9 on 762 degrees of freedom
## Multiple R-squared:  0.9183, Adjusted R-squared:  0.9182 
## F-statistic:  8569 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.67635 -0.55142  0.02282  0.87388  1.62578 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1114915  0.0809568   26.08   <2e-16 ***
## x.var       0.0074324  0.0001834   40.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.118 on 762 degrees of freedom
## Multiple R-squared:  0.6832, Adjusted R-squared:  0.6828 
## F-statistic:  1643 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -11.867   -3.725    0.416    3.373    6.967  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.960e+00  6.382e-03   620.5   <2e-16 ***
## x.var       3.875e-03  1.105e-05   350.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 171805  on 763  degrees of freedom
## Residual deviance:  22677  on 762  degrees of freedom
## AIC: 27721
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HOLY SEE  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## HONDURAS  --  10684 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1009.9  -430.9  -145.7   540.3  1757.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1774.3545    50.8897  -34.87   <2e-16 ***
## x.var          16.8818     0.1153  146.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 702.6 on 762 degrees of freedom
## Multiple R-squared:  0.9657, Adjusted R-squared:  0.9657 
## F-statistic: 2.145e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1062 -0.7513  0.3416  1.1289  1.8731 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4719937  0.1114641   31.15   <2e-16 ***
## x.var       0.0099091  0.0002525   39.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.539 on 762 degrees of freedom
## Multiple R-squared:  0.6691, Adjusted R-squared:  0.6687 
## F-statistic:  1541 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -41.394  -26.533    8.392   12.234   17.394  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.461e+00  1.751e-03    3690   <2e-16 ***
## x.var       4.177e-03  2.993e-06    1396   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2774872  on 763  degrees of freedom
## Residual deviance:  347582  on 762  degrees of freedom
## AIC: 354417
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HUNGARY  --  43562 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7959.4 -3061.9   167.3  3139.2  8707.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8772.2037   311.7702  -28.14   <2e-16 ***
## x.var          65.0760     0.7061   92.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4305 on 762 degrees of freedom
## Multiple R-squared:  0.9177, Adjusted R-squared:  0.9176 
## F-statistic:  8494 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1031 -0.8667  0.4419  1.1738  1.5690 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4691948  0.1060784   32.70   <2e-16 ***
## x.var       0.0119607  0.0002403   49.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.465 on 762 degrees of freedom
## Multiple R-squared:  0.7648, Adjusted R-squared:  0.7645 
## F-statistic:  2478 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -76.85  -59.03  -32.14   34.56   99.61  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.287e+00  1.041e-03    7003   <2e-16 ***
## x.var       4.897e-03  1.729e-06    2833   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13152021  on 763  degrees of freedom
## Residual deviance:  2452072  on 762  degrees of freedom
## AIC: 2459524
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ICELAND  --  60 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.458 -3.566 -1.130  2.759 16.508 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2799126  0.3177527   4.028 6.19e-05 ***
## x.var       0.0566340  0.0007197  78.695  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.387 on 762 degrees of freedom
## Multiple R-squared:  0.8904, Adjusted R-squared:  0.8903 
## F-statistic:  6193 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.65466 -0.27530  0.08677  0.41338  0.72417 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.433e+00  4.046e-02   35.41   <2e-16 ***
## x.var       3.758e-03  9.164e-05   41.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5586 on 762 degrees of freedom
## Multiple R-squared:  0.6882, Adjusted R-squared:  0.6878 
## F-statistic:  1682 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.0804  -0.9650  -0.2441   0.7682   2.5380  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.964e+00  2.038e-02   96.35   <2e-16 ***
## x.var       2.630e-03  3.767e-05   69.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7277.5  on 763  degrees of freedom
## Residual deviance: 1917.8  on 762  degrees of freedom
## AIC: 5381.6
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## INDIA  --  512924 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -97719 -24524  -8406  37328  92287 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -93096.420   3345.825  -27.82   <2e-16 ***
## x.var          809.125      7.578  106.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 46190 on 762 degrees of freedom
## Multiple R-squared:  0.9374, Adjusted R-squared:  0.9373 
## F-statistic: 1.14e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3301 -1.0963  0.5624  1.8145  2.5478 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.6971175  0.1638667   34.77   <2e-16 ***
## x.var       0.0129171  0.0003711   34.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.262 on 762 degrees of freedom
## Multiple R-squared:  0.6139, Adjusted R-squared:  0.6133 
## F-statistic:  1211 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -290.946  -194.878    -6.452    94.423   249.698  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.017e+01  2.651e-04   38374   <2e-16 ***
## x.var       4.390e-03  4.491e-07    9775   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 141533035  on 763  degrees of freedom
## Residual deviance:  20128618  on 762  degrees of freedom
## AIC: 20138059
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## INDONESIA  --  147025 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -30041 -21746   -604  19130  36443 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -36677.347   1538.699  -23.84   <2e-16 ***
## x.var          234.755      3.485   67.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21240 on 762 degrees of freedom
## Multiple R-squared:  0.8562, Adjusted R-squared:  0.856 
## F-statistic:  4538 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3085 -0.5297  0.4296  1.1872  1.5489 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7236277  0.1215796   38.85   <2e-16 ***
## x.var       0.0119368  0.0002754   43.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.679 on 762 degrees of freedom
## Multiple R-squared:  0.7115, Adjusted R-squared:  0.7111 
## F-statistic:  1879 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -187.58   -70.77   -12.46    23.22   149.75  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.046e+00  6.329e-04   12713   <2e-16 ***
## x.var       5.634e-03  1.026e-06    5490   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 46964485  on 763  degrees of freedom
## Residual deviance:  3768202  on 762  degrees of freedom
## AIC: 3776615
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## IRAN  --  135726 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -9456  -5289  -1951   5252  17367 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17572.256    482.884  -36.39   <2e-16 ***
## x.var          205.080      1.094  187.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6667 on 762 degrees of freedom
## Multiple R-squared:  0.9788, Adjusted R-squared:  0.9788 
## F-statistic: 3.516e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0473 -0.4467  0.4516  1.1133  1.3050 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.8085880  0.1247940   54.56   <2e-16 ***
## x.var       0.0085241  0.0002826   30.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.723 on 762 degrees of freedom
## Multiple R-squared:  0.5441, Adjusted R-squared:  0.5435 
## F-statistic: 909.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -151.47   -71.08    11.57    43.22    85.26  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.222e+00  4.630e-04   19920   <2e-16 ***
## x.var       3.821e-03  8.037e-07    4754   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 30898055  on 763  degrees of freedom
## Residual deviance:  3612101  on 762  degrees of freedom
## AIC: 3621149
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## IRAQ  --  24917 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2993.8  -722.3   -89.4   835.8  2350.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1898.0728    93.6429  -20.27   <2e-16 ***
## x.var          37.6680     0.2121  177.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1293 on 762 degrees of freedom
## Multiple R-squared:  0.9764, Adjusted R-squared:  0.9764 
## F-statistic: 3.154e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9286 -0.9702  0.1053  1.4874  2.1625 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.5167816  0.1259273   35.87   <2e-16 ***
## x.var       0.0098057  0.0002852   34.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.739 on 762 degrees of freedom
## Multiple R-squared:  0.608,  Adjusted R-squared:  0.6075 
## F-statistic:  1182 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -83.24  -41.23   10.05   25.58   47.67  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.909e+00  9.552e-04    8281   <2e-16 ***
## x.var       3.320e-03  1.699e-06    1954   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5867985  on 763  degrees of freedom
## Residual deviance: 1447782  on 762  degrees of freedom
## AIC: 1455426
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## IRELAND  --  6460 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -779.5 -329.3 -128.2  400.5  892.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -35.975     34.000  -1.058     0.29    
## x.var          8.827      0.077 114.623   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 469.4 on 762 degrees of freedom
## Multiple R-squared:  0.9452, Adjusted R-squared:  0.9451 
## F-statistic: 1.314e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9802 -0.6255  0.5654  0.9378  1.8783 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6311657  0.1185735   39.06   <2e-16 ***
## x.var       0.0071225  0.0002686   26.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.637 on 762 degrees of freedom
## Multiple R-squared:   0.48,  Adjusted R-squared:  0.4793 
## F-statistic: 703.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -46.558   -7.742   -0.771    7.840   25.071  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.837e+00  1.737e-03  3936.8   <2e-16 ***
## x.var       2.844e-03  3.170e-06   897.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1157908  on 763  degrees of freedom
## Residual deviance:  259388  on 762  degrees of freedom
## AIC: 266321
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ISRAEL  --  10075 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1024.35  -444.21   -82.69   425.83  1279.78 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1200.5820    47.0234  -25.53   <2e-16 ***
## x.var          14.2114     0.1065  133.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 649.2 on 762 degrees of freedom
## Multiple R-squared:  0.959,  Adjusted R-squared:  0.9589 
## F-statistic: 1.781e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2194 -0.6923  0.6228  1.1232  1.5327 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.6784370  0.1072444    34.3   <2e-16 ***
## x.var       0.0093276  0.0002429    38.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.481 on 762 degrees of freedom
## Multiple R-squared:  0.6593, Adjusted R-squared:  0.6589 
## F-statistic:  1475 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -42.263  -29.944   -1.424   12.793   38.876  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.568e+00  1.750e-03    3752   <2e-16 ***
## x.var       3.801e-03  3.042e-06    1250   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2378897  on 763  degrees of freedom
## Residual deviance:  496725  on 762  degrees of freedom
## AIC: 503559
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ITALY  --  153764 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21290.4 -10189.7   -390.7   8438.7  21881.3 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1760.932    854.281  -2.061   0.0396 *  
## x.var         216.863      1.935 112.084   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11790 on 762 degrees of freedom
## Multiple R-squared:  0.9428, Adjusted R-squared:  0.9427 
## F-statistic: 1.256e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.9134 -0.5457  0.6626  0.9713  1.8253 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.6915413  0.1429046   53.82   <2e-16 ***
## x.var       0.0073963  0.0003237   22.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.973 on 762 degrees of freedom
## Multiple R-squared:  0.4066, Adjusted R-squared:  0.4059 
## F-statistic: 522.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -222.179   -54.550    -7.003    63.656   116.275  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.001e+01  3.539e-04   28285   <2e-16 ***
## x.var       2.879e-03  6.446e-07    4467   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 28231156  on 763  degrees of freedom
## Residual deviance:  5897957  on 762  degrees of freedom
## AIC: 5907347
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## JAMAICA  --  2798 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -469.7 -289.6 -109.0  383.6  641.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -645.64869   24.97881  -25.85   <2e-16 ***
## x.var          3.80196    0.05657   67.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 344.9 on 762 degrees of freedom
## Multiple R-squared:  0.8556, Adjusted R-squared:  0.8554 
## F-statistic:  4516 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6205 -0.5195 -0.0316  0.6691  1.2612 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.0035833  0.0545377   18.40   <2e-16 ***
## x.var       0.0108236  0.0001235   87.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.753 on 762 degrees of freedom
## Multiple R-squared:  0.9097, Adjusted R-squared:  0.9096 
## F-statistic:  7678 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -18.4068   -9.1493    0.3927    4.8699    8.8476  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.483e+00  5.572e-03   625.1   <2e-16 ***
## x.var       6.260e-03  8.876e-06   705.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 803067  on 763  degrees of freedom
## Residual deviance:  44710  on 762  degrees of freedom
## AIC: 49921
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## JAPAN  --  22585 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3786.2 -1265.8   165.9  1578.5  4117.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4149.0814   142.2649  -29.16   <2e-16 ***
## x.var          32.1250     0.3222   99.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1964 on 762 degrees of freedom
## Multiple R-squared:  0.9288, Adjusted R-squared:  0.9287 
## F-statistic:  9941 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5104 -0.5032  0.5180  0.7845  1.2924 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3061638  0.0894495   48.14   <2e-16 ***
## x.var       0.0092841  0.0002026   45.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.235 on 762 degrees of freedom
## Multiple R-squared:  0.7338, Adjusted R-squared:  0.7334 
## F-statistic:  2100 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -54.08  -26.17  -18.11   25.56   49.53  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.695e+00  1.433e-03    4671   <2e-16 ***
## x.var       4.739e-03  2.395e-06    1979   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5872524  on 763  degrees of freedom
## Residual deviance:  732235  on 762  degrees of freedom
## AIC: 739598
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## JORDAN  --  13751 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2638.93  -895.69    52.03   962.75  2882.91 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2904.5943    94.1398  -30.85   <2e-16 ***
## x.var          21.6862     0.2132  101.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1300 on 762 degrees of freedom
## Multiple R-squared:  0.9314, Adjusted R-squared:  0.9313 
## F-statistic: 1.035e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2902 -1.2198 -0.2829  1.4319  2.4712 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.0486023  0.1016408   10.32   <2e-16 ***
## x.var       0.0140976  0.0002302   61.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.403 on 762 degrees of freedom
## Multiple R-squared:  0.8311, Adjusted R-squared:  0.8309 
## F-statistic:  3750 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -54.37  -39.66  -16.25   21.14   52.45  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.206e+00  1.793e-03    3461   <2e-16 ***
## x.var       4.872e-03  2.982e-06    1634   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4504766  on 763  degrees of freedom
## Residual deviance:  954061  on 762  degrees of freedom
## AIC: 960255
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## KAZAKHSTAN  --  18882 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4120.7 -2193.7  -148.4  2285.4  4251.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4279.0896   169.1895  -25.29   <2e-16 ***
## x.var          28.0915     0.3832   73.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2336 on 762 degrees of freedom
## Multiple R-squared:  0.8758, Adjusted R-squared:  0.8757 
## F-statistic:  5374 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4765 -0.7525  0.0444  1.0282  2.2481 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.7134582  0.1033858   26.25   <2e-16 ***
## x.var       0.0117392  0.0002342   50.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.427 on 762 degrees of freedom
## Multiple R-squared:  0.7674, Adjusted R-squared:  0.7671 
## F-statistic:  2513 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -54.642  -29.553    1.292   11.095   34.174  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.032e+00  1.777e-03    3394   <2e-16 ***
## x.var       5.481e-03  2.895e-06    1893   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5435878  on 763  degrees of freedom
## Residual deviance:  378051  on 762  degrees of freedom
## AIC: 384838
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KENYA  --  5638 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -739.5 -332.7  -59.6  336.0 1097.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1106.7801    32.0128  -34.57   <2e-16 ***
## x.var           8.9793     0.0725  123.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 442 on 762 degrees of freedom
## Multiple R-squared:  0.9527, Adjusted R-squared:  0.9526 
## F-statistic: 1.534e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2479 -0.8223  0.3138  1.0776  1.5250 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5968486  0.0941545   27.58   <2e-16 ***
## x.var       0.0101720  0.0002132   47.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.3 on 762 degrees of freedom
## Multiple R-squared:  0.7491, Adjusted R-squared:  0.7488 
## F-statistic:  2275 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -31.952  -20.077    0.667   11.677   15.823  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.531e+00  2.625e-03    2107   <2e-16 ***
## x.var       4.586e-03  4.412e-06    1039   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1582983  on 763  degrees of freedom
## Residual deviance:  185178  on 762  degrees of freedom
## AIC: 191418
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KIRIBATI  --  11 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5075 -0.3122 -0.1168  0.0785 10.4802 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.2595191  0.0760757  -3.411 0.000681 ***
## x.var        0.0010241  0.0001723   5.944 4.24e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.05 on 762 degrees of freedom
## Multiple R-squared:  0.04431,    Adjusted R-squared:  0.04305 
## F-statistic: 35.33 on 1 and 762 DF,  p-value: 4.24e-09
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14131 -0.08695 -0.03260  0.02175  2.34018 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7.211e-02  1.943e-02  -3.711 0.000222 ***
## x.var        2.849e-04  4.401e-05   6.474 1.71e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2683 on 762 degrees of freedom
## Multiple R-squared:  0.05214,    Adjusted R-squared:  0.05089 
## F-statistic: 41.91 on 1 and 762 DF,  p-value: 1.708e-10
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.722   0.000   0.000   0.000   1.884  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -144.70045   14.61712  -9.899   <2e-16 ***
## x.var          0.19316    0.01925  10.034   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 831.310  on 763  degrees of freedom
## Residual deviance:  24.766  on 762  degrees of freedom
## AIC: 81.774
## 
## Number of Fisher Scoring iterations: 12
## 
## --------------------------------------------------------------------------------

## KOREA, SOUTH  --  7689 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -875.2 -602.8 -284.4  433.3 3299.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1009.4490    63.2283  -15.96   <2e-16 ***
## x.var           7.0668     0.1432   49.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 873 on 762 degrees of freedom
## Multiple R-squared:  0.7617, Adjusted R-squared:  0.7614 
## F-statistic:  2435 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0835 -0.3075  0.2433  0.5416  0.9514 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.8775548  0.0685307   56.58   <2e-16 ***
## x.var       0.0071013  0.0001552   45.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9462 on 762 degrees of freedom
## Multiple R-squared:  0.7331, Adjusted R-squared:  0.7328 
## F-statistic:  2093 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -17.563   -5.731   -2.118    7.297   15.882  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.889e+00  3.320e-03  1472.8   <2e-16 ***
## x.var       5.146e-03  5.468e-06   941.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1274297  on 763  degrees of freedom
## Residual deviance:   64450  on 762  degrees of freedom
## AIC: 70839
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## KOSOVO  --  3105 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -310.84 -160.86   23.51  146.24  461.45 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -466.50177   14.03174  -33.25   <2e-16 ***
## x.var          5.05305    0.03178  159.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 193.7 on 762 degrees of freedom
## Multiple R-squared:  0.9707, Adjusted R-squared:  0.9707 
## F-statistic: 2.528e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.02554 -0.78960  0.03076  1.19071  1.69508 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.4386603  0.0941931   25.89   <2e-16 ***
## x.var       0.0096210  0.0002133   45.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.3 on 762 degrees of freedom
## Multiple R-squared:  0.7275, Adjusted R-squared:  0.7271 
## F-statistic:  2034 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -24.5810  -20.2561    0.2171   11.5813   18.3294  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.434e+00  3.029e-03  1794.0   <2e-16 ***
## x.var       3.935e-03  5.231e-06   752.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 860926  on 763  degrees of freedom
## Residual deviance: 171088  on 762  degrees of freedom
## AIC: 177046
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KUWAIT  --  2533 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -285.20 -103.72  -25.81   91.41  373.66 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -320.50235   12.26787  -26.12   <2e-16 ***
## x.var          4.10079    0.02778  147.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 169.4 on 762 degrees of freedom
## Multiple R-squared:  0.9662, Adjusted R-squared:  0.9662 
## F-statistic: 2.178e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4966 -0.7802  0.3739  1.1988  1.6240 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.8708550  0.1030209   27.87   <2e-16 ***
## x.var       0.0085715  0.0002333   36.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.422 on 762 degrees of freedom
## Multiple R-squared:  0.6391, Adjusted R-squared:  0.6386 
## F-statistic:  1350 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -24.223   -9.497    1.863    6.257   13.479  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.401e+00  3.181e-03  1697.6   <2e-16 ***
## x.var       3.701e-03  5.554e-06   666.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 630565  on 763  degrees of freedom
## Residual deviance: 100186  on 762  degrees of freedom
## AIC: 106150
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KYRGYZSTAN  --  2948 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -447.75 -132.20   -3.02  112.53  792.87 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -185.53275   16.55001  -11.21   <2e-16 ***
## x.var          4.29738    0.03748  114.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 228.5 on 762 degrees of freedom
## Multiple R-squared:  0.9452, Adjusted R-squared:  0.9451 
## F-statistic: 1.314e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2690 -1.0454  0.0304  1.2074  2.8483 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5992336  0.1114581   23.32   <2e-16 ***
## x.var       0.0093018  0.0002524   36.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.539 on 762 degrees of freedom
## Multiple R-squared:  0.6405, Adjusted R-squared:  0.6401 
## F-statistic:  1358 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.578  -14.477    3.612    8.786   28.340  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.805e+00  2.766e-03  2098.5   <2e-16 ***
## x.var       3.235e-03  4.942e-06   654.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 688511  on 763  degrees of freedom
## Residual deviance: 196016  on 762  degrees of freedom
## AIC: 201988
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LAOS  --  616 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -128.11  -84.89  -20.88   47.21  415.51 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -90.54537    8.35177  -10.84   <2e-16 ***
## x.var         0.38094    0.01892   20.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 115.3 on 762 degrees of freedom
## Multiple R-squared:  0.3474, Adjusted R-squared:  0.3465 
## F-statistic: 405.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0647 -0.9761 -0.1867  0.9718  2.1730 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.6346229  0.0848714  -19.26   <2e-16 ***
## x.var        0.0078210  0.0001922   40.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.172 on 762 degrees of freedom
## Multiple R-squared:  0.6848, Adjusted R-squared:  0.6844 
## F-statistic:  1655 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -10.1809   -1.0773   -0.2149   -0.0320    5.3026  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -9.7652206  0.0760658  -128.4   <2e-16 ***
## x.var        0.0216917  0.0001057   205.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 154833.9  on 763  degrees of freedom
## Residual deviance:   2460.9  on 762  degrees of freedom
## AIC: 4010.2
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LATVIA  --  5145 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -883.96 -407.64  -35.89  416.11 1071.32 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.078e+03  3.814e+01  -28.27   <2e-16 ***
## x.var        6.896e+00  8.637e-02   79.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 526.5 on 762 degrees of freedom
## Multiple R-squared:  0.8932, Adjusted R-squared:  0.8931 
## F-statistic:  6374 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.01287 -0.65687  0.03708  0.67436  1.57155 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1735891  0.0686535   17.09   <2e-16 ***
## x.var       0.0116567  0.0001555   74.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9479 on 762 degrees of freedom
## Multiple R-squared:  0.8806, Adjusted R-squared:  0.8804 
## F-statistic:  5620 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -23.663  -16.153   -7.535    9.879   23.421  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.515e+00  3.696e-03  1221.5   <2e-16 ***
## x.var       5.639e-03  5.992e-06   941.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1464625  on 763  degrees of freedom
## Residual deviance:  194885  on 762  degrees of freedom
## AIC: 200453
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LEBANON  --  10027 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2079.53  -845.25    -9.49  1014.39  2168.81 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2185.340     87.859  -24.87   <2e-16 ***
## x.var          16.529      0.199   83.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1213 on 762 degrees of freedom
## Multiple R-squared:  0.9006, Adjusted R-squared:  0.9004 
## F-statistic:  6900 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.66778 -0.66363 -0.01589  1.05111  1.56554 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0974195  0.0820737   25.55   <2e-16 ***
## x.var       0.0118825  0.0001859   63.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.133 on 762 degrees of freedom
## Multiple R-squared:  0.8428, Adjusted R-squared:  0.8426 
## F-statistic:  4086 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -47.77  -32.25  -20.83   21.22   52.10  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.970e+00  2.033e-03    2936   <2e-16 ***
## x.var       4.823e-03  3.387e-06    1424   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3465799  on 763  degrees of freedom
## Residual deviance:  782015  on 762  degrees of freedom
## AIC: 788401
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## LESOTHO  --  696 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -155.55  -64.88  -13.87   72.75  161.58 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -162.64920    6.02348  -27.00   <2e-16 ***
## x.var          1.06704    0.01364   78.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 83.16 on 762 degrees of freedom
## Multiple R-squared:  0.8892, Adjusted R-squared:  0.8891 
## F-statistic:  6118 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8446 -0.7086  0.0037  0.7942  1.4850 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.0972387  0.0639811    1.52    0.129    
## x.var       0.0103395  0.0001449   71.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8834 on 762 degrees of freedom
## Multiple R-squared:  0.8698, Adjusted R-squared:  0.8696 
## F-statistic:  5091 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -11.403   -6.102   -3.441    2.993   11.418  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.758e+00  9.125e-03   302.3   <2e-16 ***
## x.var       5.485e-03  1.486e-05   369.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 219765  on 763  degrees of freedom
## Residual deviance:  27537  on 762  degrees of freedom
## AIC: 31722
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIBERIA  --  290 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -85.904 -31.987   9.953  29.422  68.770 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -36.823753   2.879028  -12.79   <2e-16 ***
## x.var         0.414965   0.006521   63.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39.75 on 762 degrees of freedom
## Multiple R-squared:  0.8416, Adjusted R-squared:  0.8414 
## F-statistic:  4050 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.29394 -0.35519  0.04773  0.67864  1.33691 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8459429  0.0635097   29.07   <2e-16 ***
## x.var       0.0060541  0.0001438   42.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8769 on 762 degrees of freedom
## Multiple R-squared:  0.6992, Adjusted R-squared:  0.6988 
## F-statistic:  1771 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.2443  -2.9523  -0.4546   2.8544   5.1798  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.981e+00  1.042e-02   286.1   <2e-16 ***
## x.var       3.872e-03  1.804e-05   214.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 65454.5  on 763  degrees of freedom
## Residual deviance:  9590.5  on 762  degrees of freedom
## AIC: 14032
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIBYA  --  6222 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -584.69 -421.01  -85.95  425.88 1224.63 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.234e+03  3.296e+01  -37.43   <2e-16 ***
## x.var        9.165e+00  7.465e-02  122.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 455.1 on 762 degrees of freedom
## Multiple R-squared:  0.9519, Adjusted R-squared:  0.9518 
## F-statistic: 1.507e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.34108 -1.32919  0.01945  1.28877  1.85501 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.4858960  0.0959052   15.49   <2e-16 ***
## x.var       0.0120448  0.0002172   55.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.324 on 762 degrees of freedom
## Multiple R-squared:  0.8014, Adjusted R-squared:  0.8011 
## F-statistic:  3075 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -27.8914  -21.8888    0.0592    9.2914   21.6681  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.331e+00  2.770e-03    1925   <2e-16 ***
## x.var       4.892e-03  4.603e-06    1063   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1726887  on 763  degrees of freedom
## Residual deviance:  221273  on 762  degrees of freedom
## AIC: 227181
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIECHTENSTEIN  --  76 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.239  -6.753  -1.990   8.406  20.482 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.862341   0.779915  -15.21   <2e-16 ***
## x.var         0.120928   0.001766   68.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.77 on 762 degrees of freedom
## Multiple R-squared:  0.8602, Adjusted R-squared:   0.86 
## F-statistic:  4687 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2560 -0.5880 -0.1456  0.5828  1.4843 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0302370  0.0528706  -0.572    0.568    
## x.var        0.0070192  0.0001197  58.618   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.73 on 762 degrees of freedom
## Multiple R-squared:  0.8185, Adjusted R-squared:  0.8183 
## F-statistic:  3436 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -4.914  -3.439  -1.948   2.212   5.504  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.623e+00  2.006e-02   80.91   <2e-16 ***
## x.var       4.040e-03  3.448e-05  117.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 25356.8  on 763  degrees of freedom
## Residual deviance:  8463.3  on 762  degrees of freedom
## AIC: 11643
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## LITHUANIA  --  8316 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1517.8  -527.9   115.2   504.0  1649.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1661.2932    54.7230  -30.36   <2e-16 ***
## x.var          11.5738     0.1239   93.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 755.5 on 762 degrees of freedom
## Multiple R-squared:  0.9196, Adjusted R-squared:  0.9195 
## F-statistic:  8720 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.57658 -0.65437  0.04963  0.74896  1.76929 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8614314  0.0791793   23.51   <2e-16 ***
## x.var       0.0115347  0.0001793   64.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.093 on 762 degrees of freedom
## Multiple R-squared:  0.8445, Adjusted R-squared:  0.8443 
## F-statistic:  4137 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -33.313  -23.614   -9.594   15.494   32.791  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.366e+00  2.606e-03    2059   <2e-16 ***
## x.var       5.169e-03  4.289e-06    1205   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2382297  on 763  degrees of freedom
## Residual deviance:  393723  on 762  degrees of freedom
## AIC: 399804
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LUXEMBOURG  --  988 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -194.085  -81.603    7.381   80.327  185.972 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -92.39000    7.21569  -12.80   <2e-16 ***
## x.var         1.53768    0.01634   94.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 99.62 on 762 degrees of freedom
## Multiple R-squared:  0.9207, Adjusted R-squared:  0.9206 
## F-statistic:  8853 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2342 -0.4808  0.3445  0.7835  1.0118 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.8814794  0.0768501   37.49   <2e-16 ***
## x.var       0.0067835  0.0001741   38.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.061 on 762 degrees of freedom
## Multiple R-squared:  0.6659, Adjusted R-squared:  0.6655 
## F-statistic:  1519 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -15.550   -7.357   -4.055    7.095   12.987  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.616e+00  4.877e-03   946.5   <2e-16 ***
## x.var       3.443e-03  8.621e-06   399.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 242061  on 763  degrees of freedom
## Residual deviance:  55606  on 762  degrees of freedom
## AIC: 61097
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MS ZAANDAM  --  2 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.40824 -0.15762  0.09174  0.34110  0.59046 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.317e+00  3.625e-02   36.33   <2e-16 ***
## x.var       1.307e-03  8.209e-05   15.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5004 on 762 degrees of freedom
## Multiple R-squared:  0.2497, Adjusted R-squared:  0.2487 
## F-statistic: 253.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.77355 -0.08658  0.05039  0.18737  0.32434 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.233e-01  1.991e-02   36.33   <2e-16 ***
## x.var       7.181e-04  4.509e-05   15.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2749 on 762 degrees of freedom
## Multiple R-squared:  0.2497, Adjusted R-squared:  0.2487 
## F-statistic: 253.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.69173  -0.11436   0.08146   0.26857   0.44753  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.3077364  0.0578081   5.323 1.02e-07 ***
## x.var       0.0007232  0.0001226   5.897 3.69e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 266.76  on 763  degrees of freedom
## Residual deviance: 231.72  on 762  degrees of freedom
## AIC: 2049.6
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MADAGASCAR  --  1350 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -248.46  -67.08   -4.85   99.20  215.72 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -217.56475    8.30959  -26.18   <2e-16 ***
## x.var          1.84140    0.01882   97.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 114.7 on 762 degrees of freedom
## Multiple R-squared:  0.9263, Adjusted R-squared:  0.9262 
## F-statistic:  9573 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3999 -1.1387  0.3092  0.7955  1.8556 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2805442  0.0859675   14.90   <2e-16 ***
## x.var       0.0096493  0.0001947   49.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.187 on 762 degrees of freedom
## Multiple R-squared:  0.7632, Adjusted R-squared:  0.7629 
## F-statistic:  2456 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -13.7880   -8.6008   -0.9422    3.6548   13.2384  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.037e+00  5.645e-03   715.2   <2e-16 ***
## x.var       4.462e-03  9.534e-06   467.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 329617  on 763  degrees of freedom
## Residual deviance:  49515  on 762  degrees of freedom
## AIC: 54499
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALAWI  --  2610 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -620.03 -214.47   -6.78  215.78  590.10 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -594.128     21.194  -28.03   <2e-16 ***
## x.var          4.026      0.048   83.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 292.6 on 762 degrees of freedom
## Multiple R-squared:  0.9023, Adjusted R-squared:  0.9021 
## F-statistic:  7034 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0326 -0.9954  0.1396  0.9102  1.6204 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.20668    0.07507   16.07   <2e-16 ***
## x.var        0.01087    0.00017   63.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.036 on 762 degrees of freedom
## Multiple R-squared:  0.8428, Adjusted R-squared:  0.8426 
## F-statistic:  4086 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.869  -12.613   -5.327    7.992   17.541  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.212e+00  4.539e-03   928.0   <2e-16 ***
## x.var       5.307e-03  7.437e-06   713.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 806784  on 763  degrees of freedom
## Residual deviance: 100270  on 762  degrees of freedom
## AIC: 105616
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALAYSIA  --  32488 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10429.1  -6582.7    -15.8   7259.9   9269.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -9122.01     494.41  -18.45   <2e-16 ***
## x.var          44.60       1.12   39.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6826 on 762 degrees of freedom
## Multiple R-squared:  0.6755, Adjusted R-squared:  0.6751 
## F-statistic:  1586 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.61685 -0.20417  0.05539  0.41570  1.48053 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9324493  0.0603688   32.01   <2e-16 ***
## x.var       0.0124436  0.0001367   91.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8335 on 762 degrees of freedom
## Multiple R-squared:  0.9158, Adjusted R-squared:  0.9156 
## F-statistic:  8283 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -104.909   -22.690   -12.924    -2.986    87.709  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.936e+00  2.524e-03    1559   <2e-16 ***
## x.var       9.140e-03  3.799e-06    2406   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12433207  on 763  degrees of freedom
## Residual deviance:   899466  on 762  degrees of freedom
## AIC: 905882
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALDIVES  --  293 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -69.87 -26.59  10.99  24.64  60.34 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -60.780280   2.486906  -24.44   <2e-16 ***
## x.var         0.435418   0.005632   77.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.34 on 762 degrees of freedom
## Multiple R-squared:  0.8869, Adjusted R-squared:  0.8868 
## F-statistic:  5976 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5286 -0.4853  0.1697  0.5307  0.9245 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.771865   0.046791   16.50   <2e-16 ***
## x.var       0.007721   0.000106   72.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.646 on 762 degrees of freedom
## Multiple R-squared:  0.8745, Adjusted R-squared:  0.8743 
## F-statistic:  5308 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.3358  -2.9690  -0.7998   0.5541   7.2261  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.177e+00  1.310e-02   166.2   <2e-16 ***
## x.var       5.042e-03  2.165e-05   232.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 81982.3  on 763  degrees of freedom
## Residual deviance:  8663.9  on 762  degrees of freedom
## AIC: 12718
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALI  --  722 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -101.816  -22.818    3.132   21.338   85.299 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -74.048953   3.013908  -24.57   <2e-16 ***
## x.var         1.041233   0.006826  152.54   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.61 on 762 degrees of freedom
## Multiple R-squared:  0.9683, Adjusted R-squared:  0.9682 
## F-statistic: 2.327e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8497 -0.5823  0.4520  0.7316  1.2712 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.3840847  0.0759758   31.38   <2e-16 ***
## x.var       0.0069491  0.0001721   40.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.049 on 762 degrees of freedom
## Multiple R-squared:  0.6816, Adjusted R-squared:  0.6811 
## F-statistic:  1631 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -12.4511   -3.9167   -0.8495    4.6365    8.5470  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.1095947  0.0061543   667.8   <2e-16 ***
## x.var       0.0035952  0.0000108   332.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 156319  on 763  degrees of freedom
## Residual deviance:  25065  on 762  degrees of freedom
## AIC: 30196
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALTA  --  601 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -103.097  -45.598    5.956   44.125  101.968 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -95.084542   4.167397  -22.82   <2e-16 ***
## x.var         0.891173   0.009439   94.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57.54 on 762 degrees of freedom
## Multiple R-squared:  0.9213, Adjusted R-squared:  0.9212 
## F-statistic:  8915 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.64876 -0.61393 -0.07771  0.78032  1.37167 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9645282  0.0630188   15.30   <2e-16 ***
## x.var       0.0088862  0.0001427   62.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8701 on 762 degrees of freedom
## Multiple R-squared:  0.8357, Adjusted R-squared:  0.8355 
## F-statistic:  3876 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -10.232   -8.412   -4.047    5.883   11.602  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.4955358  0.0076769   455.3   <2e-16 ***
## x.var       0.0042093  0.0000131   321.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 172221  on 763  degrees of freedom
## Residual deviance:  43228  on 762  degrees of freedom
## AIC: 47808
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MARSHALL ISLANDS  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## MAURITANIA  --  977 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -123.63  -43.74   15.68   39.44  127.57 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.289e+02  4.319e+00  -29.85   <2e-16 ***
## x.var        1.361e+00  9.782e-03  139.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 59.63 on 762 degrees of freedom
## Multiple R-squared:  0.9621, Adjusted R-squared:  0.9621 
## F-statistic: 1.935e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.41420 -0.86495  0.09777  0.99599  1.72509 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8522826  0.0846605   21.88   <2e-16 ***
## x.var       0.0082635  0.0001917   43.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.169 on 762 degrees of freedom
## Multiple R-squared:  0.7091, Adjusted R-squared:  0.7087 
## F-statistic:  1857 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -13.2435   -4.6203    0.2882    2.7945    9.0166  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.090e+00  5.896e-03   693.7   <2e-16 ***
## x.var       3.978e-03  1.016e-05   391.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 217298  on 763  degrees of freedom
## Residual deviance:  29732  on 762  degrees of freedom
## AIC: 34818
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MAURITIUS  --  786 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -226.90 -144.59  -31.91   94.90  447.04 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -154.60764   13.22221  -11.69   <2e-16 ***
## x.var          0.69811    0.02995   23.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 182.6 on 762 degrees of freedom
## Multiple R-squared:  0.4163, Adjusted R-squared:  0.4155 
## F-statistic: 543.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2271 -0.7756 -0.1162  0.8117  1.4690 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.4892138  0.0614681   7.959 6.29e-15 ***
## x.var       0.0066939  0.0001392  48.083  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8487 on 762 degrees of freedom
## Multiple R-squared:  0.7521, Adjusted R-squared:  0.7518 
## F-statistic:  2312 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -9.9689  -3.0315   0.5713   5.1560  12.4969  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.578e+00  2.949e-02   -87.4   <2e-16 ***
## x.var        1.250e-02  4.279e-05   292.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 238358  on 763  degrees of freedom
## Residual deviance:  22381  on 762  degrees of freedom
## AIC: 25964
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MEXICO  --  316941 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -24209 -12557  -1954  10743  34739 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -35231.006   1085.291  -32.46   <2e-16 ***
## x.var          491.778      2.458  200.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14980 on 762 degrees of freedom
## Multiple R-squared:  0.9813, Adjusted R-squared:  0.9813 
## F-statistic: 4.003e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.6022 -1.1550  0.5873  1.8408  2.5550 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.9228986  0.1731431   34.21   <2e-16 ***
## x.var       0.0119171  0.0003921   30.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.391 on 762 degrees of freedom
## Multiple R-squared:  0.5479, Adjusted R-squared:  0.5473 
## F-statistic: 923.5 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -271.39  -145.56    17.57    81.37   170.93  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.026e+01  2.837e-04   36169   <2e-16 ***
## x.var       3.603e-03  4.976e-07    7240   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 77566800  on 763  degrees of freedom
## Residual deviance: 15458090  on 762  degrees of freedom
## AIC: 15467399
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MICRONESIA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## MOLDOVA  --  11157 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -860.6 -574.2 -167.5  493.6 1685.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1700.5513    50.2410  -33.85   <2e-16 ***
## x.var          15.0707     0.1138  132.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 693.7 on 762 degrees of freedom
## Multiple R-squared:  0.9584, Adjusted R-squared:  0.9583 
## F-statistic: 1.754e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9799 -0.7897  0.5284  1.2227  1.3666 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4366847  0.1053134   32.63   <2e-16 ***
## x.var       0.0097001  0.0002385   40.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.454 on 762 degrees of freedom
## Multiple R-squared:  0.6846, Adjusted R-squared:  0.6842 
## F-statistic:  1654 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -36.596  -16.109   -5.935   14.086   30.356  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.227e+00  1.922e-03    3241   <2e-16 ***
## x.var       4.340e-03  3.262e-06    1331   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2525341  on 763  degrees of freedom
## Residual deviance:  285886  on 762  degrees of freedom
## AIC: 292644
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MONACO  --  51 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.458  -2.937   1.283   4.720   8.198 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7.8133848  0.4191702  -18.64   <2e-16 ***
## x.var        0.0689540  0.0009494   72.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.787 on 762 degrees of freedom
## Multiple R-squared:  0.8738, Adjusted R-squared:  0.8736 
## F-statistic:  5275 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.95018 -0.42133 -0.00811  0.50234  0.80417 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.3337034  0.0377060    8.85   <2e-16 ***
## x.var       0.0053021  0.0000854   62.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5206 on 762 degrees of freedom
## Multiple R-squared:  0.8349, Adjusted R-squared:  0.8347 
## F-statistic:  3855 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7709  -2.1737  -0.5294   1.0539   3.3542  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.327e-01  2.848e-02   29.24   <2e-16 ***
## x.var       4.351e-03  4.831e-05   90.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12977  on 763  degrees of freedom
## Residual deviance:  2710  on 762  degrees of freedom
## AIC: 5710.8
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MONGOLIA  --  2163 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -664.50 -362.62  -46.12  400.57  653.83 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -594.65046   30.41860  -19.55   <2e-16 ***
## x.var          2.86700    0.06889   41.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 420 on 762 degrees of freedom
## Multiple R-squared:  0.6944, Adjusted R-squared:  0.694 
## F-statistic:  1732 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4436 -1.1626  0.2933  1.0461  2.2258 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.2395263  0.0918957  -24.37   <2e-16 ***
## x.var        0.0136932  0.0002081   65.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.269 on 762 degrees of freedom
## Multiple R-squared:  0.8503, Adjusted R-squared:  0.8501 
## F-statistic:  4329 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -26.621   -9.658   -4.371    1.895   14.725  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.053e-01  1.050e-02   86.24   <2e-16 ***
## x.var       9.551e-03  1.571e-05  607.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 834343  on 763  degrees of freedom
## Residual deviance:  72670  on 762  degrees of freedom
## AIC: 75711
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## MONTENEGRO  --  2670 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -328.64 -165.92   14.05  132.47  510.19 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -514.1473    14.0838  -36.51   <2e-16 ***
## x.var          3.9572     0.0319  124.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 194.5 on 762 degrees of freedom
## Multiple R-squared:  0.9528, Adjusted R-squared:  0.9528 
## F-statistic: 1.539e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.13551 -0.78177  0.00641  1.02083  1.44889 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.4936070  0.0755616   19.77   <2e-16 ***
## x.var       0.0105230  0.0001711   61.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.043 on 762 degrees of freedom
## Multiple R-squared:  0.8323, Adjusted R-squared:  0.832 
## F-statistic:  3781 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -18.485  -14.804   -3.040    7.905   18.276  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.586e+00  4.102e-03    1118   <2e-16 ***
## x.var       4.760e-03  6.849e-06     695   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 749509  on 763  degrees of freedom
## Residual deviance: 113991  on 762  degrees of freedom
## AIC: 119485
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MOROCCO  --  15938 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2031.56  -969.33    30.94  1035.38  2429.66 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2454.3783    83.5673  -29.37   <2e-16 ***
## x.var          24.7154     0.1893  130.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1154 on 762 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9572 
## F-statistic: 1.705e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1040 -0.6680  0.2613  1.1656  1.8265 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.6076648  0.1070642   33.70   <2e-16 ***
## x.var       0.0103394  0.0002425   42.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.478 on 762 degrees of freedom
## Multiple R-squared:  0.7047, Adjusted R-squared:  0.7043 
## F-statistic:  1818 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -50.750  -44.606   -0.163   19.431   47.851  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.926e+00  1.410e-03    4910   <2e-16 ***
## x.var       4.063e-03  2.422e-06    1677   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4365655  on 763  degrees of freedom
## Residual deviance:  895943  on 762  degrees of freedom
## AIC: 903034
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MOZAMBIQUE  --  2191 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -467.22 -251.73  -14.14  235.09  555.40 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -558.81178   20.69941  -27.00   <2e-16 ***
## x.var          3.41321    0.04688   72.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 285.8 on 762 degrees of freedom
## Multiple R-squared:  0.8743, Adjusted R-squared:  0.8741 
## F-statistic:  5301 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7479 -0.7453  0.1986  0.7863  1.3792 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.2641319  0.0633749   4.168 3.43e-05 ***
## x.var       0.0119660  0.0001435  83.366  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.875 on 762 degrees of freedom
## Multiple R-squared:  0.9012, Adjusted R-squared:  0.9011 
## F-statistic:  6950 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -22.604  -11.085   -7.633    6.911   18.091  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.586e+00  5.573e-03   643.4   <2e-16 ***
## x.var       5.959e-03  8.951e-06   665.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 740430  on 763  degrees of freedom
## Residual deviance:  84530  on 762  degrees of freedom
## AIC: 89393
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NAMIBIA  --  4002 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1120.5  -745.5   135.0   613.5  1104.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1110.6379    51.1446  -21.72   <2e-16 ***
## x.var           6.1169     0.1158   52.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 706.1 on 762 degrees of freedom
## Multiple R-squared:  0.7854, Adjusted R-squared:  0.7851 
## F-statistic:  2789 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1723 -0.8226  0.2540  0.8118  1.6047 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0804571  0.0714984  -1.125    0.261    
## x.var        0.0132517  0.0001619  81.834   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9872 on 762 degrees of freedom
## Multiple R-squared:  0.8978, Adjusted R-squared:  0.8977 
## F-statistic:  6697 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -33.888  -10.486   -6.578   -4.354   34.041  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.469e+00  4.946e-03   701.4   <2e-16 ***
## x.var       6.959e-03  7.745e-06   898.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1470198  on 763  degrees of freedom
## Residual deviance:  151707  on 762  degrees of freedom
## AIC: 156606
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NEPAL  --  11930 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2966.7 -1619.2   164.7  1614.1  3147.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3167.9105   123.7992  -25.59   <2e-16 ***
## x.var          20.1848     0.2804   71.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1709 on 762 degrees of freedom
## Multiple R-squared:  0.8718, Adjusted R-squared:  0.8716 
## F-statistic:  5182 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6978 -1.2556  0.2197  1.1144  1.8843 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1088711  0.0970698   11.42   <2e-16 ***
## x.var       0.0138164  0.0002198   62.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.34 on 762 degrees of freedom
## Multiple R-squared:  0.8383, Adjusted R-squared:  0.8381 
## F-statistic:  3949 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -61.127  -28.784   -7.749    4.262   50.031  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.572e+00  2.170e-03    2568   <2e-16 ***
## x.var       5.663e-03  3.516e-06    1611   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4319428  on 763  degrees of freedom
## Residual deviance:  590398  on 762  degrees of freedom
## AIC: 596474
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NETHERLANDS  --  22095 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2284.3 -1362.7  -411.4  1364.0  2835.5 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 582.8779   113.3968    5.14 3.49e-07 ***
## x.var        30.6397     0.2568  119.30  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1566 on 762 degrees of freedom
## Multiple R-squared:  0.9492, Adjusted R-squared:  0.9491 
## F-statistic: 1.423e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2151 -0.6465  0.5899  1.0054  1.9185 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.8980562  0.1345046   43.85   <2e-16 ***
## x.var       0.0072048  0.0003046   23.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.857 on 762 degrees of freedom
## Multiple R-squared:  0.4233, Adjusted R-squared:  0.4226 
## F-statistic: 559.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -92.521  -18.539    0.381   19.235   42.052  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.235e+00  8.833e-04    9323   <2e-16 ***
## x.var       2.657e-03  1.630e-06    1630   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3839049  on 763  degrees of freedom
## Residual deviance:  911851  on 762  degrees of freedom
## AIC: 919787
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NEW ZEALAND  --  56 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5169  -6.9246   0.2551   5.7937  12.9879 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.38427    0.48585   15.20   <2e-16 ***
## x.var        0.04676    0.00110   42.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.708 on 762 degrees of freedom
## Multiple R-squared:  0.7032, Adjusted R-squared:  0.7028 
## F-statistic:  1805 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.98387 -0.25860  0.03533  0.58305  0.98543 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.769230   0.053867   32.84   <2e-16 ***
## x.var       0.003204   0.000122   26.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7437 on 762 degrees of freedom
## Multiple R-squared:  0.475,  Adjusted R-squared:  0.4743 
## F-statistic: 689.5 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -5.030  -1.114   0.405   1.189   1.917  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.409e+00  1.776e-02  135.62   <2e-16 ***
## x.var       1.915e-03  3.437e-05   55.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6025.0  on 763  degrees of freedom
## Residual deviance: 2754.5  on 762  degrees of freedom
## AIC: 6299
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NICARAGUA  --  224 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -56.623 -25.223   0.906  26.942  46.963 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28.048414   2.191623   12.80   <2e-16 ***
## x.var        0.302474   0.004964   60.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.26 on 762 degrees of freedom
## Multiple R-squared:  0.8297, Adjusted R-squared:  0.8295 
## F-statistic:  3713 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5771 -0.7341  0.1472  0.9983  1.4979 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.2073212  0.0831661   26.54   <2e-16 ***
## x.var       0.0056891  0.0001884   30.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.148 on 762 degrees of freedom
## Multiple R-squared:  0.5449, Adjusted R-squared:  0.5443 
## F-statistic: 912.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -11.2852   -3.0301    0.6547    3.4845    5.1629  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.011e+00  7.715e-03   519.9   <2e-16 ***
## x.var       2.202e-03  1.464e-05   150.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 44549  on 763  degrees of freedom
## Residual deviance: 20362  on 762  degrees of freedom
## AIC: 24978
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NIGER  --  306 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.423 -15.251   1.868  17.450  32.482 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.574914   1.424870  -8.123 1.82e-15 ***
## x.var         0.389769   0.003227 120.779  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.67 on 762 degrees of freedom
## Multiple R-squared:  0.9504, Adjusted R-squared:  0.9503 
## F-statistic: 1.459e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6356 -0.4768  0.3034  0.6346  1.1807 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.2885843  0.0672381   34.04   <2e-16 ***
## x.var       0.0055087  0.0001523   36.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9283 on 762 degrees of freedom
## Multiple R-squared:  0.632,  Adjusted R-squared:  0.6315 
## F-statistic:  1309 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -9.0633  -1.7891  -0.4627   1.7231   5.0975  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.521e+00  8.835e-03   398.5   <2e-16 ***
## x.var       3.087e-03  1.591e-05   194.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 52525.9  on 763  degrees of freedom
## Residual deviance:  9719.5  on 762  degrees of freedom
## AIC: 14360
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NIGERIA  --  3142 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -243.339 -110.241    0.085  101.334  274.759 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -158.5610     9.6673   -16.4   <2e-16 ***
## x.var          4.5500     0.0219   207.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 133.5 on 762 degrees of freedom
## Multiple R-squared:  0.9827, Adjusted R-squared:  0.9826 
## F-statistic: 4.318e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9148 -0.7445  0.3211  1.1010  1.8403 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4275825  0.1097043   31.24   <2e-16 ***
## x.var       0.0079870  0.0002485   32.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.515 on 762 degrees of freedom
## Multiple R-squared:  0.5756, Adjusted R-squared:  0.575 
## F-statistic:  1033 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -30.367   -8.868    1.945    7.784   15.356  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.935e+00  2.624e-03    2262   <2e-16 ***
## x.var       3.142e-03  4.711e-06     667   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 644200  on 763  degrees of freedom
## Residual deviance: 136412  on 762  degrees of freedom
## AIC: 142653
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NORTH MACEDONIA  --  8958 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1117.11  -445.92    52.67   337.87  1576.42 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1589.4253    42.7845  -37.15   <2e-16 ***
## x.var          13.0056     0.0969  134.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 590.7 on 762 degrees of freedom
## Multiple R-squared:  0.9594, Adjusted R-squared:  0.9594 
## F-statistic: 1.801e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6698 -0.6762  0.4497  1.0298  1.3333 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.070247   0.095382   32.19   <2e-16 ***
## x.var       0.009992   0.000216   46.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.317 on 762 degrees of freedom
## Multiple R-squared:  0.7374, Adjusted R-squared:  0.737 
## F-statistic:  2139 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -31.302  -21.418   -6.615   15.290   30.278  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.921e+00  2.169e-03    2730   <2e-16 ***
## x.var       4.559e-03  3.650e-06    1249   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2305644  on 763  degrees of freedom
## Residual deviance:  290753  on 762  degrees of freedom
## AIC: 297312
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NORWAY  --  1598 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -139.49  -79.76   -0.56   43.57  360.35 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -88.6392     7.2393  -12.24   <2e-16 ***
## x.var         1.7360     0.0164  105.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 99.95 on 762 degrees of freedom
## Multiple R-squared:  0.9364, Adjusted R-squared:  0.9363 
## F-statistic: 1.121e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7247 -0.5227  0.3940  0.6417  1.3481 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4074176  0.0842228   40.46   <2e-16 ***
## x.var       0.0061008  0.0001908   31.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.163 on 762 degrees of freedom
## Multiple R-squared:  0.5731, Adjusted R-squared:  0.5725 
## F-statistic:  1023 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -17.2184   -2.4471   -0.0899    3.0583    6.7713  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.826e+00  4.458e-03  1082.4   <2e-16 ***
## x.var       3.328e-03  7.927e-06   419.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 231577  on 763  degrees of freedom
## Residual deviance:  27411  on 762  degrees of freedom
## AIC: 33106
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## OMAN  --  4238 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -631.66 -225.62  -82.99  278.87  755.11 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -720.41433   26.72243  -26.96   <2e-16 ***
## x.var          6.92483    0.06052  114.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 368.9 on 762 degrees of freedom
## Multiple R-squared:  0.945,  Adjusted R-squared:  0.9449 
## F-statistic: 1.309e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.261 -0.967  0.249  1.187  1.773 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5799144  0.1014767   25.42   <2e-16 ***
## x.var       0.0098774  0.0002298   42.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.401 on 762 degrees of freedom
## Multiple R-squared:  0.7079, Adjusted R-squared:  0.7076 
## F-statistic:  1847 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -28.524  -19.234    2.231    9.250   20.703  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.586e+00  2.721e-03  2052.9   <2e-16 ***
## x.var       4.155e-03  4.654e-06   892.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1172769  on 763  degrees of freedom
## Residual deviance:  181540  on 762  degrees of freedom
## AIC: 187670
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PAKISTAN  --  30114 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2998.2 -1737.6    97.5  1387.7  3930.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3977.1235   125.5860  -31.67   <2e-16 ***
## x.var          47.0970     0.2844  165.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1734 on 762 degrees of freedom
## Multiple R-squared:  0.973,  Adjusted R-squared:  0.9729 
## F-statistic: 2.742e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2112 -0.8217  0.4991  1.2490  2.2168 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6635184  0.1319362   35.35   <2e-16 ***
## x.var       0.0097793  0.0002988   32.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.822 on 762 degrees of freedom
## Multiple R-squared:  0.5843, Adjusted R-squared:  0.5838 
## F-statistic:  1071 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -76.872  -33.893    7.542   18.672   40.928  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.766e+00  9.614e-04    8078   <2e-16 ***
## x.var       3.801e-03  1.671e-06    2275   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7273668  on 763  degrees of freedom
## Residual deviance: 1036929  on 762  degrees of freedom
## AIC: 1044653
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PALAU  --  6 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2675 -0.1650 -0.0624  0.0402  5.7266 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.363e-01  4.103e-02  -3.322 0.000937 ***
## x.var        5.377e-04  9.294e-05   5.786 1.05e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5665 on 762 degrees of freedom
## Multiple R-squared:  0.04208,    Adjusted R-squared:  0.04083 
## F-statistic: 33.48 on 1 and 762 DF,  p-value: 1.053e-08
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10040 -0.06192 -0.02344  0.01504  1.84329 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5.110e-02  1.470e-02  -3.477 0.000536 ***
## x.var        2.017e-04  3.329e-05   6.060 2.14e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2029 on 762 degrees of freedom
## Multiple R-squared:  0.04598,    Adjusted R-squared:  0.04473 
## F-statistic: 36.72 on 1 and 762 DF,  p-value: 2.138e-09
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.275   0.000   0.000   0.000   1.614  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -151.92037   21.09201  -7.203 5.90e-13 ***
## x.var          0.20182    0.02777   7.268 3.65e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 444.99  on 763  degrees of freedom
## Residual deviance:  17.12  on 762  degrees of freedom
## AIC: 61.812
## 
## Number of Fisher Scoring iterations: 13
## 
## --------------------------------------------------------------------------------

## PANAMA  --  8047 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1080.6  -468.4  -220.0   452.9  1331.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -577.7960    48.3061  -11.96   <2e-16 ***
## x.var         12.6087     0.1094  115.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 666.9 on 762 degrees of freedom
## Multiple R-squared:  0.9457, Adjusted R-squared:  0.9457 
## F-statistic: 1.328e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4473 -0.7781  0.4683  1.2547  1.6515 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0197099  0.1137660   35.33   <2e-16 ***
## x.var       0.0087254  0.0002577   33.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.571 on 762 degrees of freedom
## Multiple R-squared:  0.6008, Adjusted R-squared:  0.6003 
## F-statistic:  1147 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -47.929  -28.682    1.908   15.805   35.216  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.857e+00  1.628e-03    4212   <2e-16 ***
## x.var       3.266e-03  2.904e-06    1125   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1973076  on 763  degrees of freedom
## Residual deviance:  515562  on 762  degrees of freedom
## AIC: 522494
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PAPUA NEW GUINEA  --  636 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -145.27  -85.50  -33.75   82.89  216.74 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -149.59653    7.76794  -19.26   <2e-16 ***
## x.var          0.75088    0.01759   42.68   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 107.2 on 762 degrees of freedom
## Multiple R-squared:  0.7051, Adjusted R-squared:  0.7047 
## F-statistic:  1822 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.08640 -0.34953  0.02214  0.34306  0.87852 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8.793e-01  3.416e-02  -25.74   <2e-16 ***
## x.var        1.040e-02  7.736e-05  134.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4716 on 762 degrees of freedom
## Multiple R-squared:  0.9595, Adjusted R-squared:  0.9595 
## F-statistic: 1.808e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -8.989  -3.035  -1.902   1.199   7.004  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.038e-01  1.781e-02   17.06   <2e-16 ***
## x.var       8.492e-03  2.707e-05  313.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 194681  on 763  degrees of freedom
## Residual deviance:   9357  on 762  degrees of freedom
## AIC: 12759
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PARAGUAY  --  18281 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4120.1 -2209.3   282.7  2236.2  4641.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4670.874    181.925  -25.68   <2e-16 ***
## x.var          29.347      0.412   71.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2512 on 762 degrees of freedom
## Multiple R-squared:  0.8694, Adjusted R-squared:  0.8692 
## F-statistic:  5073 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3995 -0.9721  0.1248  0.9469  1.8094 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.6147212  0.0883785   18.27   <2e-16 ***
## x.var       0.0135313  0.0002002   67.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.22 on 762 degrees of freedom
## Multiple R-squared:  0.8571, Adjusted R-squared:  0.8569 
## F-statistic:  4570 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -68.122  -32.942   -9.917    9.907   61.427  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.881e+00  1.831e-03    3211   <2e-16 ***
## x.var       5.756e-03  2.958e-06    1946   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6329803  on 763  degrees of freedom
## Residual deviance:  839061  on 762  degrees of freedom
## AIC: 845537
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PERU  --  209927 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33272 -11312   -676   9632  30256 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -10308.085   1128.469  -9.135   <2e-16 ***
## x.var          331.816      2.556 129.827   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15580 on 762 degrees of freedom
## Multiple R-squared:  0.9567, Adjusted R-squared:  0.9567 
## F-statistic: 1.686e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.9764 -1.0214  0.6335  1.7042  2.4374 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.5233910  0.1684117   38.73   <2e-16 ***
## x.var       0.0102964  0.0003814   26.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.325 on 762 degrees of freedom
## Multiple R-squared:  0.4888, Adjusted R-squared:  0.4882 
## F-statistic: 728.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -262.64  -108.17    33.67    73.36   134.77  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.026e+01  3.040e-04   33741   <2e-16 ***
## x.var       3.102e-03  5.469e-07    5671   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 48341429  on 763  degrees of freedom
## Residual deviance: 11745002  on 762  degrees of freedom
## AIC: 11754319
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PHILIPPINES  --  55977 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -8982  -4728  -1572   5320  11223 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.130e+04  4.170e+02  -27.10   <2e-16 ***
## x.var        7.663e+01  9.444e-01   81.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5757 on 762 degrees of freedom
## Multiple R-squared:  0.8963, Adjusted R-squared:  0.8961 
## F-statistic:  6584 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6443 -0.5579  0.4068  0.9648  1.4873 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.5304085  0.1036756    43.7   <2e-16 ***
## x.var       0.0103564  0.0002348    44.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.431 on 762 degrees of freedom
## Multiple R-squared:  0.7185, Adjusted R-squared:  0.7182 
## F-statistic:  1945 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -69.481  -36.630    9.411   17.847   25.954  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.161e+00  1.040e-03    6888   <2e-16 ***
## x.var       5.303e-03  1.703e-06    3113   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 14101202  on 763  degrees of freedom
## Residual deviance:   662186  on 762  degrees of freedom
## AIC: 670040
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## POLAND  --  110517 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19960.9  -6905.9   -272.3   7236.0  20601.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -20763.381    720.241  -28.83   <2e-16 ***
## x.var          161.926      1.631   99.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9944 on 762 degrees of freedom
## Multiple R-squared:  0.9282, Adjusted R-squared:  0.9281 
## F-statistic:  9854 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8178 -0.9589  0.6246  1.1542  1.8062 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.1971949  0.1207879   34.75   <2e-16 ***
## x.var       0.0124127  0.0002736   45.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.668 on 762 degrees of freedom
## Multiple R-squared:  0.7299, Adjusted R-squared:  0.7295 
## F-statistic:  2059 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -117.69   -98.17   -58.47    70.09   142.87  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.331e+00  6.351e-04   13117   <2e-16 ***
## x.var       4.714e-03  1.062e-06    4438   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 32014727  on 763  degrees of freedom
## Residual deviance:  6218405  on 762  degrees of freedom
## AIC: 6226552
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PORTUGAL  --  20922 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4330.1 -2171.6  -176.8  1793.4  5449.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2767.3926   192.5626  -14.37   <2e-16 ***
## x.var          33.7782     0.4361   77.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2659 on 762 degrees of freedom
## Multiple R-squared:  0.8873, Adjusted R-squared:  0.8871 
## F-statistic:  5999 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0497 -0.6944  0.6631  1.1123  1.4940 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.5367438  0.1199588   37.82   <2e-16 ***
## x.var       0.0093272  0.0002717   34.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.656 on 762 degrees of freedom
## Multiple R-squared:  0.6073, Adjusted R-squared:  0.6068 
## F-statistic:  1179 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -65.54  -40.93  -28.46   26.68   81.90  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.465e+00  1.124e-03    6639   <2e-16 ***
## x.var       3.760e-03  1.957e-06    1921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5957684  on 763  degrees of freedom
## Residual deviance: 1527030  on 762  degrees of freedom
## AIC: 1534528
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## QATAR  --  664 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -104.447  -44.079    0.054   34.002  114.530 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -57.959244   4.114076  -14.09   <2e-16 ***
## x.var         1.024371   0.009318  109.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56.8 on 762 degrees of freedom
## Multiple R-squared:  0.9407, Adjusted R-squared:  0.9406 
## F-statistic: 1.209e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7984 -0.6360  0.2870  0.8154  1.4782 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.3300165  0.0801767   29.06   <2e-16 ***
## x.var       0.0070968  0.0001816   39.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.107 on 762 degrees of freedom
## Multiple R-squared:  0.6672, Adjusted R-squared:  0.6667 
## F-statistic:  1527 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -13.2116   -6.0497    0.5164    3.6284    8.8244  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.245e+00  5.907e-03   718.6   <2e-16 ***
## x.var       3.397e-03  1.047e-05   324.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 154908  on 763  degrees of freedom
## Residual deviance:  32176  on 762  degrees of freedom
## AIC: 37308
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ROMANIA  --  62958 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -6423  -3573  -1628   4670  11445 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.153e+04  3.670e+02  -31.42   <2e-16 ***
## x.var        8.755e+01  8.313e-01  105.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5068 on 762 degrees of freedom
## Multiple R-squared:  0.9357, Adjusted R-squared:  0.9356 
## F-statistic: 1.109e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0776 -0.7656  0.6775  1.2767  1.4941 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.4187048  0.1234302   35.80   <2e-16 ***
## x.var       0.0109823  0.0002796   39.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.704 on 762 degrees of freedom
## Multiple R-squared:  0.6695, Adjusted R-squared:  0.669 
## F-statistic:  1543 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -74.76  -48.96  -12.66   40.37   57.02  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.647e+00  8.811e-04    8679   <2e-16 ***
## x.var       4.810e-03  1.469e-06    3275   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 15616779  on 763  degrees of freedom
## Residual deviance:  1435005  on 762  degrees of freedom
## AIC: 1442887
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## RUSSIA  --  340872 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34452 -29143 -11439  25722  69897 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -65886.321   2398.878  -27.46   <2e-16 ***
## x.var          440.918      5.433   81.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33120 on 762 degrees of freedom
## Multiple R-squared:  0.8963, Adjusted R-squared:  0.8962 
## F-statistic:  6586 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6828 -0.9773  0.5313  1.6045  2.1653 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9509915  0.1482201   33.40   <2e-16 ***
## x.var       0.0128384  0.0003357   38.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 762 degrees of freedom
## Multiple R-squared:  0.6575, Adjusted R-squared:  0.657 
## F-statistic:  1463 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -143.797   -65.628    -6.041    40.152    87.854  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.861e+00  4.394e-04   20167   <2e-16 ***
## x.var       5.373e-03  7.183e-07    7481   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 82286549  on 763  degrees of freedom
## Residual deviance:  4139152  on 762  degrees of freedom
## AIC: 4147985
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## RWANDA  --  1457 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -358.38 -238.50    7.15  211.44  391.06 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -393.25290   16.79626  -23.41   <2e-16 ***
## x.var          2.19540    0.03804   57.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 231.9 on 762 degrees of freedom
## Multiple R-squared:  0.8138, Adjusted R-squared:  0.8136 
## F-statistic:  3331 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4376 -0.4657  0.1392  0.4512  1.0950 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.1151793  0.0470768  -2.447   0.0146 *  
## x.var        0.0115676  0.0001066 108.491   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.65 on 762 degrees of freedom
## Multiple R-squared:  0.9392, Adjusted R-squared:  0.9391 
## F-statistic: 1.177e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -19.326   -6.724   -5.423    3.233   13.775  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.557e+00  8.040e-03     318   <2e-16 ***
## x.var       6.798e-03  1.264e-05     538   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 506718  on 763  degrees of freedom
## Residual deviance:  41394  on 762  degrees of freedom
## AIC: 45834
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT KITTS AND NEVIS  --  42 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.7585  -6.9289  -0.6022   5.6997  22.0357 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8.324069   0.550306  -15.13   <2e-16 ***
## x.var        0.037271   0.001246   29.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.598 on 762 degrees of freedom
## Multiple R-squared:  0.5399, Adjusted R-squared:  0.5393 
## F-statistic: 894.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.49471 -0.57307 -0.03637  0.77783  1.11202 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.0320927  0.0556816  -18.54   <2e-16 ***
## x.var        0.0049352  0.0001261   39.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7688 on 762 degrees of freedom
## Multiple R-squared:  0.6677, Adjusted R-squared:  0.6673 
## F-statistic:  1531 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3250  -0.9182  -0.3401  -0.1069   2.9055  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -5.9213495  0.1348833  -43.90   <2e-16 ***
## x.var        0.0130858  0.0001948   67.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12699.80  on 763  degrees of freedom
## Residual deviance:   890.92  on 762  degrees of freedom
## AIC: 1984.9
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT LUCIA  --  357 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -68.51 -50.30 -14.08  53.43 114.47 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -83.280623   3.992128  -20.86   <2e-16 ***
## x.var         0.427949   0.009042   47.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.12 on 762 degrees of freedom
## Multiple R-squared:  0.7462, Adjusted R-squared:  0.7459 
## F-statistic:  2240 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.70562 -0.45422  0.07126  0.50064  1.25567 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.2658127  0.0496759  -25.48   <2e-16 ***
## x.var        0.0101414  0.0001125   90.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6859 on 762 degrees of freedom
## Multiple R-squared:  0.9143, Adjusted R-squared:  0.9141 
## F-statistic:  8125 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -6.374  -3.232  -1.741   1.476   4.603  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.566e-02  2.202e-02   3.436 0.000591 ***
## x.var       8.012e-03  3.375e-05 237.396  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 108336.8  on 763  degrees of freedom
## Residual deviance:   6669.3  on 762  degrees of freedom
## AIC: 9514.9
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT VINCENT AND THE GRENADINES  --  106 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.632 -14.759  -4.312  14.888  48.532 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -21.824628   1.365155  -15.99   <2e-16 ***
## x.var         0.103922   0.003092   33.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.85 on 762 degrees of freedom
## Multiple R-squared:  0.5972, Adjusted R-squared:  0.5967 
## F-statistic:  1130 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4464 -0.4261  0.1630  0.4222  1.1256 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.133e+00  4.346e-02  -26.06   <2e-16 ***
## x.var        7.184e-03  9.844e-05   72.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6001 on 762 degrees of freedom
## Multiple R-squared:  0.8748, Adjusted R-squared:  0.8747 
## F-statistic:  5326 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.2078  -1.3076  -0.6231   0.5451   3.4793  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.700e+00  5.803e-02  -46.52   <2e-16 ***
## x.var        9.962e-03  8.638e-05  115.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 29980.7  on 763  degrees of freedom
## Residual deviance:  1663.8  on 762  degrees of freedom
## AIC: 3618.5
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAMOA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## SAN MARINO  --  112 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.7687  -6.7378  -0.3888   7.5407  15.4764 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 14.521141   0.643073   22.58   <2e-16 ***
## x.var        0.130157   0.001456   89.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.879 on 762 degrees of freedom
## Multiple R-squared:  0.9129, Adjusted R-squared:  0.9128 
## F-statistic:  7986 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6849 -0.2488  0.2335  0.3629  0.8450 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.5510795  0.0546859   46.65   <2e-16 ***
## x.var       0.0035216  0.0001239   28.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.755 on 762 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.5141 
## F-statistic: 808.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.4776  -0.9146   0.2940   1.2927   2.5080  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.251e+00  1.140e-02  285.03   <2e-16 ***
## x.var       2.110e-03  2.178e-05   96.89   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13929.6  on 763  degrees of freedom
## Residual deviance:  3935.9  on 762  degrees of freedom
## AIC: 8235.5
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## SAO TOME AND PRINCIPE  --  72 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4928  -3.5316   0.6575   3.6412  11.0251 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6.2318452  0.3621621  -17.21   <2e-16 ***
## x.var        0.0887490  0.0008202  108.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5 on 762 degrees of freedom
## Multiple R-squared:  0.9389, Adjusted R-squared:  0.9388 
## F-statistic: 1.171e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47088 -0.31001  0.09021  0.42717  0.96403 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9707178  0.0443524   21.89   <2e-16 ***
## x.var       0.0050016  0.0001005   49.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6124 on 762 degrees of freedom
## Multiple R-squared:  0.7649, Adjusted R-squared:  0.7646 
## F-statistic:  2479 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8738  -0.6783   0.1137   0.9488   1.9677  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.657e+00  2.101e-02   78.86   <2e-16 ***
## x.var       3.582e-03  3.689e-05   97.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12963.9  on 763  degrees of freedom
## Residual deviance:  1812.5  on 762  degrees of freedom
## AIC: 5214.2
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAUDI ARABIA  --  8990 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1720.6  -803.4   260.6   653.3  1391.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 178.8590    65.6926   2.723  0.00662 ** 
## x.var        13.7850     0.1488  92.651  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 907 on 762 degrees of freedom
## Multiple R-squared:  0.9185, Adjusted R-squared:  0.9184 
## F-statistic:  8584 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8430 -0.8891  0.3191  1.4740  2.0395 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3069326  0.1307682   32.94   <2e-16 ***
## x.var       0.0086457  0.0002962   29.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 762 degrees of freedom
## Multiple R-squared:  0.5279, Adjusted R-squared:  0.5273 
## F-statistic: 852.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -62.137  -26.057    6.603   19.457   31.179  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.398e+00  1.335e-03    5542   <2e-16 ***
## x.var       2.703e-03  2.456e-06    1100   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2049545  on 763  degrees of freedom
## Residual deviance:  711054  on 762  degrees of freedom
## AIC: 718163
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SENEGAL  --  1960 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -326.34  -96.57  -25.54  109.12  365.39 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -368.55426   11.99563  -30.72   <2e-16 ***
## x.var          3.16836    0.02717  116.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 165.6 on 762 degrees of freedom
## Multiple R-squared:  0.9469, Adjusted R-squared:  0.9469 
## F-statistic: 1.36e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7974 -0.7500  0.2846  0.8892  1.4868 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1603924  0.0838479   25.77   <2e-16 ***
## x.var       0.0090998  0.0001899   47.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.158 on 762 degrees of freedom
## Multiple R-squared:  0.7508, Adjusted R-squared:  0.7505 
## F-statistic:  2296 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -19.2757  -10.7544   -0.8061    6.2164   12.9171  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.611e+00  4.264e-03  1081.3   <2e-16 ***
## x.var       4.419e-03  7.214e-06   612.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 546516  on 763  degrees of freedom
## Residual deviance:  68465  on 762  degrees of freedom
## AIC: 74044
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SERBIA  --  15010 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2074.9  -850.3  -296.7   972.1  3002.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2655.1269    89.5227  -29.66   <2e-16 ***
## x.var          19.1914     0.2028   94.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1236 on 762 degrees of freedom
## Multiple R-squared:  0.9216, Adjusted R-squared:  0.9215 
## F-statistic:  8959 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8512 -0.6464  0.5876  0.9375  1.3080 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2601710  0.0957561   34.05   <2e-16 ***
## x.var       0.0101896  0.0002169   46.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.322 on 762 degrees of freedom
## Multiple R-squared:  0.7434, Adjusted R-squared:  0.7431 
## F-statistic:  2207 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -33.118  -21.755   -6.339   16.813   32.811  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.990e+00  1.958e-03    3060   <2e-16 ***
## x.var       5.004e-03  3.241e-06    1544   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3547087  on 763  degrees of freedom
## Residual deviance:  335609  on 762  degrees of freedom
## AIC: 342375
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SEYCHELLES  --  160 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.856 -25.686   3.374  20.839  40.999 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -41.214948   1.763096  -23.38   <2e-16 ***
## x.var         0.215615   0.003993   54.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.34 on 762 degrees of freedom
## Multiple R-squared:  0.7928, Adjusted R-squared:  0.7925 
## F-statistic:  2916 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8220 -0.4313  0.1721  0.5515  1.3224 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.3314897  0.0536694  -24.81   <2e-16 ***
## x.var        0.0090359  0.0001216   74.34   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.741 on 762 degrees of freedom
## Multiple R-squared:  0.8788, Adjusted R-squared:  0.8787 
## F-statistic:  5526 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.6407  -2.8451  -1.5386   0.6637   5.1728  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.893e-01  2.959e-02  -13.16   <2e-16 ***
## x.var        7.696e-03  4.562e-05  168.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 55659.3  on 763  degrees of freedom
## Residual deviance:  5807.3  on 762  degrees of freedom
## AIC: 8178.1
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SIERRA LEONE  --  125 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.800 -11.333   1.277  11.651  22.486 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 13.589637   1.000011   13.59   <2e-16 ***
## x.var        0.165327   0.002265   73.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.81 on 762 degrees of freedom
## Multiple R-squared:  0.8749, Adjusted R-squared:  0.8747 
## F-statistic:  5328 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.42356 -0.50394  0.07657  0.82641  1.36166 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9662311  0.0750264   26.21   <2e-16 ***
## x.var       0.0049710  0.0001699   29.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.036 on 762 degrees of freedom
## Multiple R-squared:  0.529,  Adjusted R-squared:  0.5284 
## F-statistic: 855.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.4080  -1.3127   0.5919   1.7892   3.2658  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.3576297  0.0106248   316.0   <2e-16 ***
## x.var       0.0022563  0.0000201   112.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 22195.7  on 763  degrees of freedom
## Residual deviance:  8661.7  on 762  degrees of freedom
## AIC: 12846
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SINGAPORE  --  963 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -277.36 -165.98  -20.41  113.30  493.32 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -174.21499   14.46004  -12.05   <2e-16 ***
## x.var          0.84279    0.03275   25.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 199.6 on 762 degrees of freedom
## Multiple R-squared:  0.465,  Adjusted R-squared:  0.4643 
## F-statistic: 662.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5782 -0.8333  0.1138  0.7917  1.1992 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1969146  0.0608709   19.66   <2e-16 ***
## x.var       0.0064620  0.0001379   46.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8404 on 762 degrees of freedom
## Multiple R-squared:  0.7425, Adjusted R-squared:  0.7421 
## F-statistic:  2197 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -14.006   -3.450    1.062    5.779   10.749  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.466e-01  1.911e-02   -12.9   <2e-16 ***
## x.var        9.447e-03  2.863e-05   329.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 254365  on 763  degrees of freedom
## Residual deviance:  31946  on 762  degrees of freedom
## AIC: 36028
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SLOVAKIA  --  18314 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3923.4 -1340.8   268.1  1557.4  4004.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4032.5793   152.2125  -26.49   <2e-16 ***
## x.var          27.9234     0.3447   81.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2102 on 762 degrees of freedom
## Multiple R-squared:  0.8959, Adjusted R-squared:  0.8958 
## F-statistic:  6561 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.18667 -0.97376 -0.05713  1.10621  1.98824 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1995580  0.0900352   13.32   <2e-16 ***
## x.var       0.0141016  0.0002039   69.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.243 on 762 degrees of freedom
## Multiple R-squared:  0.8626, Adjusted R-squared:  0.8624 
## F-statistic:  4782 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -59.02  -42.48  -28.33   23.89   70.05  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.226e+00  1.687e-03    3690   <2e-16 ***
## x.var       5.198e-03  2.774e-06    1874   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6189242  on 763  degrees of freedom
## Residual deviance: 1369659  on 762  degrees of freedom
## AIC: 1375970
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## SLOVENIA  --  6246 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1417.73  -386.13   -67.62   595.07  1002.63 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -993.0089    48.1544  -20.62   <2e-16 ***
## x.var          9.5099     0.1091   87.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 664.9 on 762 degrees of freedom
## Multiple R-squared:  0.9089, Adjusted R-squared:  0.9088 
## F-statistic:  7603 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2064 -0.7141  0.1613  0.8430  1.7469 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.681349   0.089650   29.91   <2e-16 ***
## x.var       0.010098   0.000203   49.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.238 on 762 degrees of freedom
## Multiple R-squared:  0.7645, Adjusted R-squared:  0.7642 
## F-statistic:  2473 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -35.03  -27.85  -12.45   20.01   38.23  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.897e+00  2.326e-03    2536   <2e-16 ***
## x.var       4.163e-03  3.977e-06    1047   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1868695  on 763  degrees of freedom
## Residual deviance:  505251  on 762  degrees of freedom
## AIC: 511556
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOLOMON ISLANDS  --  87 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.984 -3.640 -1.271  1.048 80.643 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.130897   0.688215  -4.549 6.26e-06 ***
## x.var        0.012418   0.001559   7.967 5.92e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.502 on 762 degrees of freedom
## Multiple R-squared:  0.07689,    Adjusted R-squared:  0.07568 
## F-statistic: 63.47 on 1 and 762 DF,  p-value: 5.918e-15
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4806 -0.2908 -0.1011  0.0887  3.9669 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.2495927  0.0467442  -5.340 1.23e-07 ***
## x.var        0.0009948  0.0001059   9.396  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6454 on 762 degrees of freedom
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.1027 
## F-statistic: 88.29 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.1121  -0.0001   0.0000   0.0000   3.2205  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -80.774328   2.409813  -33.52   <2e-16 ***
## x.var         0.112109   0.003189   35.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8729.63  on 763  degrees of freedom
## Residual deviance:  194.93  on 762  degrees of freedom
## AIC: 352.26
## 
## Number of Fisher Scoring iterations: 10
## 
## --------------------------------------------------------------------------------

## SOMALIA  --  1348 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -371.35 -104.07   34.73  126.06  304.79 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -306.90242   12.21884  -25.12   <2e-16 ***
## x.var          2.10974    0.02767   76.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 168.7 on 762 degrees of freedom
## Multiple R-squared:  0.8841, Adjusted R-squared:  0.8839 
## F-statistic:  5812 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4863 -0.3631  0.1464  0.6819  1.4264 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8242553  0.0695030   26.25   <2e-16 ***
## x.var       0.0085978  0.0001574   54.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9596 on 762 degrees of freedom
## Multiple R-squared:  0.7965, Adjusted R-squared:  0.7963 
## F-statistic:  2983 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -16.166   -6.942   -1.892    4.787   13.301  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.618e+00  6.182e-03   585.2   <2e-16 ***
## x.var       5.234e-03  1.015e-05   515.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 404644  on 763  degrees of freedom
## Residual deviance:  38494  on 762  degrees of freedom
## AIC: 43660
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOUTH AFRICA  --  98978 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -10801  -4255  -1200   4175  16222 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.638e+04  4.375e+02  -37.44   <2e-16 ***
## x.var        1.540e+02  9.908e-01  155.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6040 on 762 degrees of freedom
## Multiple R-squared:  0.9694, Adjusted R-squared:  0.9694 
## F-statistic: 2.415e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0784 -1.2005  0.3449  1.6919  2.5271 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2636132  0.1470728   28.99   <2e-16 ***
## x.var       0.0125350  0.0003331   37.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.031 on 762 degrees of freedom
## Multiple R-squared:  0.6502, Adjusted R-squared:  0.6497 
## F-statistic:  1416 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -127.898   -94.395     1.293    47.481    99.083  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.653e+00  5.831e-04   14838   <2e-16 ***
## x.var       4.202e-03  9.955e-07    4221   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 26506477  on 763  degrees of freedom
## Residual deviance:  4251190  on 762  degrees of freedom
## AIC: 4259396
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOUTH SUDAN  --  137 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.2675  -8.1835   0.0801   6.4786  24.0704 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.380912   0.771850  -5.676 1.96e-08 ***
## x.var        0.212891   0.001748 121.782  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.66 on 762 degrees of freedom
## Multiple R-squared:  0.9511, Adjusted R-squared:  0.9511 
## F-statistic: 1.483e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0795 -0.6896  0.2173  0.7449  1.3392 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.3818544  0.0704083   19.63   <2e-16 ***
## x.var       0.0061194  0.0001595   38.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9721 on 762 degrees of freedom
## Multiple R-squared:  0.659,  Adjusted R-squared:  0.6586 
## F-statistic:  1473 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.4775  -2.5835   0.7832   1.5780   4.2019  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.989e+00  1.166e-02   256.3   <2e-16 ***
## x.var       2.996e-03  2.111e-05   141.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 31520.8  on 763  degrees of freedom
## Residual deviance:  8779.6  on 762  degrees of freedom
## AIC: 12813
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SPAIN  --  98936 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11612.5  -6312.3    -21.4   5330.6  13198.1 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4642.122    503.635   9.217   <2e-16 ***
## x.var        132.953      1.141 116.558   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6954 on 762 degrees of freedom
## Multiple R-squared:  0.9469, Adjusted R-squared:  0.9468 
## F-statistic: 1.359e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.5520 -0.6232  0.6442  1.1901  2.1217 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.2438671  0.1547874   46.80   <2e-16 ***
## x.var       0.0075163  0.0003506   21.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.137 on 762 degrees of freedom
## Multiple R-squared:  0.3763, Adjusted R-squared:  0.3755 
## F-statistic: 459.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -200.828   -27.041     5.451    35.071    86.428  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.799e+00  4.098e-04   23909   <2e-16 ***
## x.var       2.542e-03  7.615e-07    3339   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 16102850  on 763  degrees of freedom
## Residual deviance:  3914951  on 762  degrees of freedom
## AIC: 3924013
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SRI LANKA  --  16086 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5072.4 -3228.7    88.4  3621.8  4443.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4465.7064   236.1083  -18.91   <2e-16 ***
## x.var          21.7780     0.5348   40.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3260 on 762 degrees of freedom
## Multiple R-squared:  0.6852, Adjusted R-squared:  0.6848 
## F-statistic:  1659 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2808 -0.4252  0.1421  0.4253  0.9685 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6.870e-03  4.069e-02  -0.169    0.866    
## x.var        1.433e-02  9.216e-05 155.497   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5618 on 762 degrees of freedom
## Multiple R-squared:  0.9694, Adjusted R-squared:  0.9694 
## F-statistic: 2.418e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -72.427  -18.923  -12.881   -5.489   63.993  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.169e+00  3.647e-03     869   <2e-16 ***
## x.var       9.212e-03  5.482e-06    1680   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6169213  on 763  degrees of freedom
## Residual deviance:  509929  on 762  degrees of freedom
## AIC: 515379
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SUDAN  --  3895 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -391.86 -115.03  -10.35   93.29  347.69 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -324.65961   11.11241  -29.22   <2e-16 ***
## x.var          5.33854    0.02517  212.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 153.4 on 762 degrees of freedom
## Multiple R-squared:  0.9833, Adjusted R-squared:  0.9833 
## F-statistic: 4.499e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.641 -0.897  0.410  1.188  1.812 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2047214  0.1070999   29.92   <2e-16 ***
## x.var       0.0085534  0.0002426   35.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.479 on 762 degrees of freedom
## Multiple R-squared:   0.62,  Adjusted R-squared:  0.6195 
## F-statistic:  1243 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -29.9824  -11.1450    0.9656    9.8227   15.5728  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.853e+00  2.624e-03  2231.0   <2e-16 ***
## x.var       3.452e-03  4.636e-06   744.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 799635  on 763  degrees of freedom
## Residual deviance: 150668  on 762  degrees of freedom
## AIC: 156915
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SUMMER OLYMPICS 2020  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## SURINAME  --  1314 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -327.74 -156.48  -18.55  194.18  304.57 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -306.35555   13.63401  -22.47   <2e-16 ***
## x.var          1.78770    0.03088   57.89   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 188.2 on 762 degrees of freedom
## Multiple R-squared:  0.8148, Adjusted R-squared:  0.8145 
## F-statistic:  3352 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.45014 -0.76446  0.02823  0.57989  1.49964 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.7337803  0.0568134   12.92   <2e-16 ***
## x.var       0.0099494  0.0001287   77.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7844 on 762 degrees of freedom
## Multiple R-squared:  0.887,  Adjusted R-squared:  0.8868 
## F-statistic:  5979 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -12.8758   -5.5178   -0.6982    3.3245    7.3306  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.6706476  0.0082440     324   <2e-16 ***
## x.var       0.0063423  0.0000131     484   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 378961  on 763  degrees of freedom
## Residual deviance:  18878  on 762  degrees of freedom
## AIC: 23579
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SWEDEN  --  17064 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2318.8 -1320.2  -177.9  1012.6  2695.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 659.7339   109.7474   6.011 2.85e-09 ***
## x.var        23.6082     0.2486  94.979  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1515 on 762 degrees of freedom
## Multiple R-squared:  0.9221, Adjusted R-squared:  0.922 
## F-statistic:  9021 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9162 -0.6668  0.5736  1.0684  1.8765 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.561802   0.133771   41.58   <2e-16 ***
## x.var       0.007384   0.000303   24.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.847 on 762 degrees of freedom
## Multiple R-squared:  0.438,  Adjusted R-squared:  0.4373 
## F-statistic: 593.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -83.529  -20.088    2.037   18.116   41.496  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.029e+00  9.870e-04    8135   <2e-16 ***
## x.var       2.591e-03  1.828e-06    1417   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3071064  on 763  degrees of freedom
## Residual deviance:  867807  on 762  degrees of freedom
## AIC: 875532
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SWITZERLAND  --  13062 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2704.2 -1219.9  -149.6  1034.8  2828.2 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -727.2872   107.7751  -6.748 2.96e-11 ***
## x.var         19.9069     0.2441  81.554  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1488 on 762 degrees of freedom
## Multiple R-squared:  0.8972, Adjusted R-squared:  0.8971 
## F-statistic:  6651 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3984 -0.5992  0.5363  1.0704  1.5916 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.0614475  0.1168046   43.33   <2e-16 ***
## x.var       0.0076589  0.0002645   28.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.613 on 762 degrees of freedom
## Multiple R-squared:  0.5238, Adjusted R-squared:  0.5232 
## F-statistic: 838.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -61.34  -27.45  -13.36   24.45   51.70  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.396e+00  1.260e-03    5868   <2e-16 ***
## x.var       3.160e-03  2.261e-06    1398   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3041838  on 763  degrees of freedom
## Residual deviance:  808363  on 762  degrees of freedom
## AIC: 815793
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SYRIA  --  3059 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -403.61 -202.51   -2.96  182.49  643.13 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -647.79541   18.04830  -35.89   <2e-16 ***
## x.var          4.66256    0.04088  114.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 249.2 on 762 degrees of freedom
## Multiple R-squared:  0.9447, Adjusted R-squared:  0.9446 
## F-statistic: 1.301e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.98508 -0.97244  0.03244  1.08386  1.47257 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2351341  0.0778094   15.87   <2e-16 ***
## x.var       0.0111933  0.0001762   63.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.074 on 762 degrees of freedom
## Multiple R-squared:  0.8411, Adjusted R-squared:  0.8409 
## F-statistic:  4034 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.995  -15.391   -3.763    9.224   17.761  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.562e+00  3.987e-03  1144.4   <2e-16 ***
## x.var       5.022e-03  6.594e-06   761.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 901825  on 763  degrees of freedom
## Residual deviance: 119299  on 762  degrees of freedom
## AIC: 124776
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TAIWAN*  --  852 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -416.47 -170.78   51.97  177.45  272.62 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -274.06796   14.95243  -18.33   <2e-16 ***
## x.var          1.45172    0.03387   42.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 206.4 on 762 degrees of freedom
## Multiple R-squared:  0.7069, Adjusted R-squared:  0.7065 
## F-statistic:  1838 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9786 -0.7482  0.1526  0.8233  1.5506 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0069469  0.0717229  -0.097    0.923    
## x.var        0.0094408  0.0001624  58.118   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9903 on 762 degrees of freedom
## Multiple R-squared:  0.8159, Adjusted R-squared:  0.8157 
## F-statistic:  3378 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -20.771   -9.705   -3.622   -1.380   21.997  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.656e+00  1.106e-02   149.8   <2e-16 ***
## x.var       7.497e-03  1.712e-05   437.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 417138  on 763  degrees of freedom
## Residual deviance:  87461  on 762  degrees of freedom
## AIC: 91513
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## TAJIKISTAN  --  125 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.573 -11.527   3.417  11.510  18.883 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.043580   1.034791   14.54   <2e-16 ***
## x.var        0.171860   0.002344   73.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.29 on 762 degrees of freedom
## Multiple R-squared:  0.8759, Adjusted R-squared:  0.8757 
## F-statistic:  5377 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.43639 -0.57584  0.08088  0.98040  1.30549 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.9087445  0.0782850   24.38   <2e-16 ***
## x.var       0.0051730  0.0001773   29.18   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.081 on 762 degrees of freedom
## Multiple R-squared:  0.5277, Adjusted R-squared:  0.527 
## F-statistic: 851.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.7666  -1.3955   0.8182   1.9638   3.1308  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.421e+00  1.033e-02   331.4   <2e-16 ***
## x.var       2.228e-03  1.957e-05   113.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 23532.7  on 763  degrees of freedom
## Residual deviance:  9631.3  on 762  degrees of freedom
## AIC: 13815
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TANZANIA  --  798 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -321.83 -177.95   -5.07  146.81  346.64 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -188.27911   14.68992  -12.82   <2e-16 ***
## x.var          0.90632    0.03327   27.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 202.8 on 762 degrees of freedom
## Multiple R-squared:  0.4934, Adjusted R-squared:  0.4927 
## F-statistic: 742.1 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.65571 -0.94484  0.03968  0.89413  1.50718 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.8380260  0.0696108   12.04   <2e-16 ***
## x.var       0.0069059  0.0001577   43.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9611 on 762 degrees of freedom
## Multiple R-squared:  0.7157, Adjusted R-squared:  0.7154 
## F-statistic:  1919 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -17.195   -5.347   -0.457    4.911   21.328  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.809e-01  1.879e-02  -14.95   <2e-16 ***
## x.var        9.600e-03  2.809e-05  341.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 289001  on 763  degrees of freedom
## Residual deviance:  47463  on 762  degrees of freedom
## AIC: 51375
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## THAILAND  --  22768 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7483.0 -4693.0   240.4  4854.5  6862.4 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6320.281    360.302  -17.54   <2e-16 ***
## x.var          29.868      0.816   36.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4975 on 762 degrees of freedom
## Multiple R-squared:  0.6374, Adjusted R-squared:  0.637 
## F-statistic:  1340 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.96224 -0.91395  0.06557  0.84001  1.85900 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9252960  0.0737945   12.54   <2e-16 ***
## x.var       0.0124931  0.0001671   74.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.019 on 762 degrees of freedom
## Multiple R-squared:   0.88,  Adjusted R-squared:  0.8798 
## F-statistic:  5587 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -92.615  -29.606   -8.861   -0.079   74.051  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.768e+00  3.538e-03   782.4   <2e-16 ***
## x.var       1.024e-02  5.249e-06  1950.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9147560  on 763  degrees of freedom
## Residual deviance:  876819  on 762  degrees of freedom
## AIC: 882495
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TIMOR-LESTE  --  125 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.161 -30.832   0.445  27.608  45.322 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -37.195368   2.091485  -17.78   <2e-16 ***
## x.var         0.177967   0.004737   37.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.88 on 762 degrees of freedom
## Multiple R-squared:  0.6494, Adjusted R-squared:  0.649 
## F-statistic:  1412 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0939 -0.7542  0.2555  0.7464  1.5158 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.5239872  0.0688002  -22.15   <2e-16 ***
## x.var        0.0082225  0.0001558   52.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9499 on 762 degrees of freedom
## Multiple R-squared:  0.7851, Adjusted R-squared:  0.7849 
## F-statistic:  2785 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.5934  -2.5502  -1.1118  -0.4834   7.2474  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.046e+00  4.345e-02  -47.09   <2e-16 ***
## x.var        9.797e-03  6.482e-05  151.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 54495.1  on 763  degrees of freedom
## Residual deviance:  6482.6  on 762  degrees of freedom
## AIC: 8304.2
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## TOGO  --  272 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.843 -16.689  -8.186  19.387  44.984 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -45.375543   1.536044  -29.54   <2e-16 ***
## x.var         0.391547   0.003479  112.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.21 on 762 degrees of freedom
## Multiple R-squared:  0.9433, Adjusted R-squared:  0.9432 
## F-statistic: 1.267e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7989 -0.2791  0.2154  0.4390  0.8392 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.3631255  0.0471477   28.91   <2e-16 ***
## x.var       0.0067044  0.0001068   62.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.651 on 762 degrees of freedom
## Multiple R-squared:  0.838,  Adjusted R-squared:  0.8378 
## F-statistic:  3942 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.7740  -2.4243   0.5441   1.5806   2.7731  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.527e+00  1.210e-02   208.8   <2e-16 ***
## x.var       4.409e-03  2.049e-05   215.2   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 63976.4  on 763  degrees of freedom
## Residual deviance:  5012.1  on 762  degrees of freedom
## AIC: 9282.1
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TONGA  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## TRINIDAD AND TOBAGO  --  3596 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -841.82 -391.76  -94.14  353.98 1457.20 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -749.94208   39.99140  -18.75   <2e-16 ***
## x.var          3.78107    0.09057   41.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 552.1 on 762 degrees of freedom
## Multiple R-squared:  0.6958, Adjusted R-squared:  0.6954 
## F-statistic:  1743 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3630 -0.3593 -0.0099  0.3826  1.0131 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.6888948  0.0388112   17.75   <2e-16 ***
## x.var       0.0106996  0.0000879  121.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5359 on 762 degrees of freedom
## Multiple R-squared:  0.9511, Adjusted R-squared:  0.951 
## F-statistic: 1.482e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -13.311   -4.583   -1.889    1.887   10.441  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.988e+00  7.828e-03   254.0   <2e-16 ***
## x.var       8.394e-03  1.192e-05   704.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 948963  on 763  degrees of freedom
## Residual deviance:  22726  on 762  degrees of freedom
## AIC: 27653
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## TUNISIA  --  27592 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4557.5 -3079.5   -57.2  2883.7  6837.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6881.94     234.01  -29.41   <2e-16 ***
## x.var          44.10       0.53   83.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3231 on 762 degrees of freedom
## Multiple R-squared:  0.9008, Adjusted R-squared:  0.9007 
## F-statistic:  6922 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7712 -0.7279  0.1404  0.9522  1.8462 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.998494   0.089188   22.41   <2e-16 ***
## x.var       0.013555   0.000202   67.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 762 degrees of freedom
## Multiple R-squared:  0.8553, Adjusted R-squared:  0.8551 
## F-statistic:  4503 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -81.36  -45.23  -13.35   28.14   55.74  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.379e+00  1.458e-03    4374   <2e-16 ***
## x.var       5.628e-03  2.365e-06    2379   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9347331  on 763  degrees of freedom
## Residual deviance: 1241173  on 762  degrees of freedom
## AIC: 1247958
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TURKEY  --  93258 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10590.6  -6192.4   -713.6   6281.7  15860.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -15987.502    497.529  -32.13   <2e-16 ***
## x.var          127.132      1.127  112.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6869 on 762 degrees of freedom
## Multiple R-squared:  0.9435, Adjusted R-squared:  0.9434 
## F-statistic: 1.273e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7639 -0.6754  0.6150  1.1803  1.9255 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.1966866  0.1347039   38.58   <2e-16 ***
## x.var       0.0103123  0.0003051   33.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 762 degrees of freedom
## Multiple R-squared:  0.5999, Adjusted R-squared:  0.5994 
## F-statistic:  1143 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -94.44  -32.96  -16.57   33.65   68.79  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.136e+00  7.070e-04   11507   <2e-16 ***
## x.var       4.649e-03  1.186e-06    3921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 21573379  on 763  degrees of freedom
## Residual deviance:  1553232  on 762  degrees of freedom
## AIC: 1561521
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## US  --  941889 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -64109 -24754  -7691  23566  72685 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -69959.371   2593.198  -26.98   <2e-16 ***
## x.var         1274.722      5.873  217.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35800 on 762 degrees of freedom
## Multiple R-squared:  0.9841, Adjusted R-squared:  0.9841 
## F-statistic: 4.711e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3681 -0.8521  0.7126  1.5829  2.3029 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.9893215  0.1762365   45.33   <2e-16 ***
## x.var       0.0099673  0.0003992   24.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.433 on 762 degrees of freedom
## Multiple R-squared:   0.45,  Adjusted R-squared:  0.4493 
## F-statistic: 623.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -459.5  -112.4    -9.6   106.2   271.6  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.139e+01  1.665e-04   68380   <2e-16 ***
## x.var       3.376e-03  2.954e-07   11428   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 180344842  on 763  degrees of freedom
## Residual deviance:  28493599  on 762  degrees of freedom
## AIC: 28503952
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UGANDA  --  3585 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1345.9  -591.6   173.4   604.4  1002.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1008.101     50.314  -20.04   <2e-16 ***
## x.var           5.438      0.114   47.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 694.7 on 762 degrees of freedom
## Multiple R-squared:  0.7493, Adjusted R-squared:  0.7489 
## F-statistic:  2277 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.13788 -0.73034  0.04715  0.83650  1.47326 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.3017555  0.0703922  -4.287 2.05e-05 ***
## x.var        0.0132589  0.0001594  83.165  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9719 on 762 degrees of freedom
## Multiple R-squared:  0.9008, Adjusted R-squared:  0.9006 
## F-statistic:  6916 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -32.814  -11.453   -7.422   -1.654   32.831  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.162e+00  5.481e-03   576.9   <2e-16 ***
## x.var       7.231e-03  8.532e-06   847.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1367367  on 763  degrees of freedom
## Residual deviance:  163032  on 762  degrees of freedom
## AIC: 167739
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UKRAINE  --  112173 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -12597  -9700  -2992   8417  22368 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -22516.836    776.740  -28.99   <2e-16 ***
## x.var          149.294      1.759   84.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10720 on 762 degrees of freedom
## Multiple R-squared:  0.9043, Adjusted R-squared:  0.9042 
## F-statistic:  7202 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5377 -0.9395  0.7202  1.1579  1.6338 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.8913630  0.1169453   33.27   <2e-16 ***
## x.var       0.0126730  0.0002649   47.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.615 on 762 degrees of freedom
## Multiple R-squared:  0.7503, Adjusted R-squared:  0.7499 
## F-statistic:  2289 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -90.45  -70.60  -21.09   39.91   97.67  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.742e+00  7.626e-04   10153   <2e-16 ***
## x.var       5.424e-03  1.245e-06    4358   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 29282080  on 763  degrees of freedom
## Residual deviance:  2621748  on 762  degrees of freedom
## AIC: 2629733
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UNITED ARAB EMIRATES  --  2298 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -300.7 -165.7   33.5  162.2  288.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -291.75151   12.36628  -23.59   <2e-16 ***
## x.var          3.64965    0.02801  130.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 170.7 on 762 degrees of freedom
## Multiple R-squared:  0.9571, Adjusted R-squared:  0.957 
## F-statistic: 1.698e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6049 -0.6012  0.5042  0.8253  1.4077 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.1519276  0.0923262   34.14   <2e-16 ***
## x.var       0.0078107  0.0002091   37.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.275 on 762 degrees of freedom
## Multiple R-squared:  0.6468, Adjusted R-squared:  0.6463 
## F-statistic:  1395 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.895   -6.372   -2.111    7.679   15.402  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.263e+00  3.395e-03  1550.4   <2e-16 ***
## x.var       3.728e-03  5.919e-06   629.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 563982  on 763  degrees of freedom
## Residual deviance:  88814  on 762  degrees of freedom
## AIC: 94801
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UNITED KINGDOM  --  161549 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -18483 -11037  -3216   7373  31045 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -554.604    976.150  -0.568     0.57    
## x.var        229.840      2.211 103.960   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13480 on 762 degrees of freedom
## Multiple R-squared:  0.9341, Adjusted R-squared:  0.9341 
## F-statistic: 1.081e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4954 -0.7770  0.7106  1.2456  2.3126 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.1157324  0.1636787   43.47   <2e-16 ***
## x.var       0.0086281  0.0003707   23.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.26 on 762 degrees of freedom
## Multiple R-squared:  0.4155, Adjusted R-squared:  0.4147 
## F-statistic: 541.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -238.059   -47.082    -5.982    43.491   153.225  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.011e+01  3.389e-04   29823   <2e-16 ***
## x.var       2.829e-03  6.191e-07    4570   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 30735665  on 763  degrees of freedom
## Residual deviance:  7448572  on 762  degrees of freedom
## AIC: 7457855
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## URUGUAY  --  6919 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2097.8 -1031.5   164.1  1021.7  1919.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1931.1440    86.7931  -22.25   <2e-16 ***
## x.var          11.3189     0.1966   57.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1198 on 762 degrees of freedom
## Multiple R-squared:  0.8131, Adjusted R-squared:  0.8129 
## F-statistic:  3316 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7385 -0.4779  0.1706  0.5150  1.2413 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9114722  0.0575030   15.85   <2e-16 ***
## x.var       0.0125301  0.0001302   96.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7939 on 762 degrees of freedom
## Multiple R-squared:  0.9239, Adjusted R-squared:  0.9238 
## F-statistic:  9257 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -47.07  -22.73  -16.53    7.81   51.07  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.545e+00  3.253e-03    1397   <2e-16 ***
## x.var       6.302e-03  5.176e-06    1217   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2794746  on 763  degrees of freedom
## Residual deviance:  525752  on 762  degrees of freedom
## AIC: 531386
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## UZBEKISTAN  --  1624 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -223.58 -102.62   24.62  107.55  191.78 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -193.96670    8.52982  -22.74   <2e-16 ***
## x.var          2.19152    0.01932  113.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 117.8 on 762 degrees of freedom
## Multiple R-squared:  0.9441, Adjusted R-squared:  0.944 
## F-statistic: 1.287e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6729 -0.7163 -0.1628  1.0310  1.8550 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.107567   0.086985   24.23   <2e-16 ***
## x.var       0.008698   0.000197   44.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.201 on 762 degrees of freedom
## Multiple R-squared:  0.719,  Adjusted R-squared:  0.7186 
## F-statistic:  1949 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -16.3825   -8.1049   -0.2261    4.4272   13.1373  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.648e+00  4.529e-03  1026.3   <2e-16 ***
## x.var       3.868e-03  7.845e-06   493.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 360298  on 763  degrees of freedom
## Residual deviance:  65533  on 762  degrees of freedom
## AIC: 70962
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VANUATU  --  1 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5416 -0.1808  0.0265  0.2070  0.4565 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.191e-01  1.875e-02  -17.02   <2e-16 ***
## x.var        1.892e-03  4.246e-05   44.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2588 on 762 degrees of freedom
## Multiple R-squared:  0.7226, Adjusted R-squared:  0.7222 
## F-statistic:  1985 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37541 -0.12529  0.01837  0.14347  0.31643 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.212e-01  1.299e-02  -17.02   <2e-16 ***
## x.var        1.311e-03  2.943e-05   44.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1794 on 762 degrees of freedom
## Multiple R-squared:  0.7226, Adjusted R-squared:  0.7222 
## F-statistic:  1985 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75312  -0.46663  -0.28051   0.07022   1.03862  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.0819512  0.2472494  -16.51   <2e-16 ***
## x.var        0.0062016  0.0003945   15.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 559.43  on 763  degrees of freedom
## Residual deviance: 184.76  on 762  degrees of freedom
## AIC: 806.76
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VENEZUELA  --  5624 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -888.7 -430.2  -46.5  467.9 1214.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.222e+03  3.967e+01  -30.82   <2e-16 ***
## x.var        8.472e+00  8.985e-02   94.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 547.7 on 762 degrees of freedom
## Multiple R-squared:  0.9211, Adjusted R-squared:  0.921 
## F-statistic:  8891 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7507 -0.7812  0.1909  0.9388  1.6597 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0469133  0.0844805   24.23   <2e-16 ***
## x.var       0.0108279  0.0001913   56.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.166 on 762 degrees of freedom
## Multiple R-squared:  0.8078, Adjusted R-squared:  0.8075 
## F-statistic:  3203 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.561  -18.201    2.697    6.832   15.133  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.036e+00  3.061e-03    1645   <2e-16 ***
## x.var       5.194e-03  5.034e-06    1032   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1598523  on 763  degrees of freedom
## Residual deviance:  137215  on 762  degrees of freedom
## AIC: 143223
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VIETNAM  --  39773 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12876.0  -6614.8    177.8   5324.1  17947.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8949.370    568.610  -15.74   <2e-16 ***
## x.var          40.282      1.288   31.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7851 on 762 degrees of freedom
## Multiple R-squared:  0.5622, Adjusted R-squared:  0.5616 
## F-statistic: 978.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4326 -1.0090  0.3033  1.0576  1.7514 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.5373721  0.0916261  -16.78   <2e-16 ***
## x.var        0.0157182  0.0002075   75.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.265 on 762 degrees of freedom
## Multiple R-squared:  0.8828, Adjusted R-squared:  0.8826 
## F-statistic:  5737 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -96.841  -30.873   -7.753   -3.605   85.283  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.328e+00  3.962e-03   335.3   <2e-16 ***
## x.var       1.271e-02  5.739e-06  2215.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13712238  on 763  degrees of freedom
## Residual deviance:  1139772  on 762  degrees of freedom
## AIC: 1144239
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## WEST BANK AND GAZA  --  5439 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -834.33 -261.30   28.94  320.53 1098.35 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.107e+03  3.256e+01  -33.99   <2e-16 ***
## x.var        8.376e+00  7.374e-02  113.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 449.5 on 762 degrees of freedom
## Multiple R-squared:  0.9442, Adjusted R-squared:  0.9442 
## F-statistic: 1.29e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0447 -1.3765  0.1484  1.3074  1.7717 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2607081  0.0930028   13.56   <2e-16 ***
## x.var       0.0122493  0.0002106   58.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.284 on 762 degrees of freedom
## Multiple R-squared:  0.8161, Adjusted R-squared:  0.8159 
## F-statistic:  3382 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.395  -22.496   -8.733   14.526   29.001  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.292e+00  2.855e-03    1853   <2e-16 ***
## x.var       4.821e-03  4.757e-06    1013   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1641845  on 763  degrees of freedom
## Residual deviance:  282424  on 762  degrees of freedom
## AIC: 288225
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## WINTER OLYMPICS 2022  --  0 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0     NaN      NaN
## x.var              0          0     NaN      NaN
## 
## Residual standard error: 0 on 762 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 762 DF,  p-value: NA
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: algorithm did not converge

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  -1.667e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.730e+01  3.728e+04  -0.001    0.999
## x.var        3.508e-16  8.443e+01   0.000    1.000
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 763  degrees of freedom
## Residual deviance: 2.1221e-09  on 762  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
## 
## --------------------------------------------------------------------------------

## YEMEN  --  2130 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -353.56  -75.82   34.08  102.26  234.48 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -237.49093    9.92382  -23.93   <2e-16 ***
## x.var          3.01464    0.02248  134.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137 on 762 degrees of freedom
## Multiple R-squared:  0.9594, Adjusted R-squared:  0.9593 
## F-statistic: 1.799e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2600 -0.8833  0.3198  1.1178  2.1330 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.3814988  0.1072465   22.21   <2e-16 ***
## x.var       0.0088733  0.0002429   36.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.481 on 762 degrees of freedom
## Multiple R-squared:  0.6365, Adjusted R-squared:  0.6361 
## F-statistic:  1335 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.607   -7.163    1.516    6.189   10.476  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.086e+00  3.719e-03  1367.5   <2e-16 ***
## x.var       3.710e-03  6.490e-06   571.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 464879  on 763  degrees of freedom
## Residual deviance:  74077  on 762  degrees of freedom
## AIC: 79711
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ZAMBIA  --  3947 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -913.96 -544.48   48.04  481.84  977.30 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -983.68044   40.64285   -24.2   <2e-16 ***
## x.var          6.37948    0.09205    69.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 561.1 on 762 degrees of freedom
## Multiple R-squared:  0.8631, Adjusted R-squared:  0.8629 
## F-statistic:  4803 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2722 -0.9091  0.2215  0.8762  1.7748 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.4799361  0.0792280   18.68   <2e-16 ***
## x.var       0.0111583  0.0001794   62.18   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.094 on 762 degrees of freedom
## Multiple R-squared:  0.8354, Adjusted R-squared:  0.8352 
## F-statistic:  3867 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -31.288  -14.825   -3.764    4.905   28.748  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.498e+00  3.781e-03  1189.8   <2e-16 ***
## x.var       5.553e-03  6.145e-06   903.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1303439  on 763  degrees of freedom
## Residual deviance:  142921  on 762  degrees of freedom
## AIC: 148572
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ZIMBABWE  --  5388 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1190.89  -684.43   -22.09   665.57  1343.82 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1351.9781    54.0820  -25.00   <2e-16 ***
## x.var           8.1597     0.1225   66.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 746.7 on 762 degrees of freedom
## Multiple R-squared:  0.8535, Adjusted R-squared:  0.8533 
## F-statistic:  4438 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8229 -0.9616  0.1311  0.8756  1.5314 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.0848824  0.0721901   15.03   <2e-16 ***
## x.var       0.0120994  0.0001635   74.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9967 on 762 degrees of freedom
## Multiple R-squared:  0.8779, Adjusted R-squared:  0.8777 
## F-statistic:  5476 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -33.522  -14.624   -8.700    8.338   28.213  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.392e+00  3.666e-03    1198   <2e-16 ***
## x.var       6.053e-03  5.872e-06    1031   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1754770  on 763  degrees of freedom
## Residual deviance:  168087  on 762  degrees of freedom
## AIC: 173721
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# or just
tots.per.location(covid19.data("ts-confirmed"))

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:29:27 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## -------------------------------------------------------------------------------- 
## AFGHANISTAN  --  172901 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35929 -11019   2252  12537  30136 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -22010.619   1262.462  -17.43   <2e-16 ***
## x.var          252.088      2.859   88.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17430 on 762 degrees of freedom
## Multiple R-squared:  0.9107, Adjusted R-squared:  0.9106 
## F-statistic:  7773 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5585 -0.7744  0.2035  1.4144  2.5343 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.2298417  0.1470755   42.36   <2e-16 ***
## x.var       0.0099587  0.0003331   29.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.031 on 762 degrees of freedom
## Multiple R-squared:  0.5398, Adjusted R-squared:  0.5392 
## F-statistic: 893.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -174.59   -54.30    12.52    48.31   127.23  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.408e+00  4.202e-04   22387   <2e-16 ***
## x.var       3.848e-03  7.286e-07    5281   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 39285703  on 763  degrees of freedom
## Residual deviance:  5535177  on 762  degrees of freedom
## AIC: 5544191
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ALBANIA  --  270734 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33333 -16802   -845  13174  46722 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -47078.030   1488.666  -31.62   <2e-16 ***
## x.var          356.411      3.372  105.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20550 on 762 degrees of freedom
## Multiple R-squared:  0.9362, Adjusted R-squared:  0.9361 
## F-statistic: 1.117e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5765 -0.8233  0.4144  1.3843  1.7345 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9912363  0.1275738   39.12   <2e-16 ***
## x.var       0.0124535  0.0002889   43.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.761 on 762 degrees of freedom
## Multiple R-squared:  0.7091, Adjusted R-squared:  0.7087 
## F-statistic:  1858 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -155.09  -130.81   -49.36    60.42   193.75  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.043e+00  4.376e-04   20667   <2e-16 ***
## x.var       4.820e-03  7.291e-07    6611   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 67661090  on 763  degrees of freedom
## Residual deviance:  9826126  on 762  degrees of freedom
## AIC: 9834899
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ALGERIA  --  264488 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -17925  -8758  -2300   6918  30913 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -31271.316    829.266  -37.71   <2e-16 ***
## x.var          358.561      1.878  190.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11450 on 762 degrees of freedom
## Multiple R-squared:  0.9795, Adjusted R-squared:  0.9795 
## F-statistic: 3.645e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8005 -0.7878  0.4623  1.4575  1.9804 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.4524349  0.1454576   44.36   <2e-16 ***
## x.var       0.0102370  0.0003294   31.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.008 on 762 degrees of freedom
## Multiple R-squared:  0.5589, Adjusted R-squared:  0.5583 
## F-statistic: 965.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -205.89  -102.63    22.98    62.02   124.00  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.762e+00  3.522e-04   27714   <2e-16 ***
## x.var       3.847e-03  6.108e-07    6298   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 56004111  on 763  degrees of freedom
## Residual deviance:  8016972  on 762  degrees of freedom
## AIC: 8026235
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANDORRA  --  37901 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4676.6 -2084.8  -472.0   357.7 13585.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4583.1776   254.6489  -18.00   <2e-16 ***
## x.var          37.8262     0.5767   65.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3516 on 762 degrees of freedom
## Multiple R-squared:  0.8495, Adjusted R-squared:  0.8493 
## F-statistic:  4301 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8398 -0.7519  0.5258  1.0234  1.4134 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.4643945  0.1086797   41.08   <2e-16 ***
## x.var       0.0093850  0.0002461   38.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.501 on 762 degrees of freedom
## Multiple R-squared:  0.6561, Adjusted R-squared:  0.6556 
## F-statistic:  1454 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -52.96  -35.63  -13.27   28.28   46.02  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.007e+00  1.265e-03    5540   <2e-16 ***
## x.var       4.534e-03  2.130e-06    2128   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6698923  on 763  degrees of freedom
## Residual deviance:  866535  on 762  degrees of freedom
## AIC: 874015
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANGOLA  --  98698 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -12148  -6617  -4204   4273  26953 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -18688.169    713.564  -26.19   <2e-16 ***
## x.var          121.668      1.616   75.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9852 on 762 degrees of freedom
## Multiple R-squared:  0.8815, Adjusted R-squared:  0.8813 
## F-statistic:  5668 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9094 -1.1338  0.1151  1.4817  2.3350 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.1167430  0.1184242   26.32   <2e-16 ***
## x.var       0.0136659  0.0002682   50.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.635 on 762 degrees of freedom
## Multiple R-squared:  0.7731, Adjusted R-squared:  0.7728 
## F-statistic:  2596 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -81.60  -60.72   -0.45   30.38   50.44  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.463e+00  8.619e-04    8658   <2e-16 ***
## x.var       5.530e-03  1.402e-06    3944   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 23791121  on 763  degrees of freedom
## Residual deviance:  1730852  on 762  degrees of freedom
## AIC: 1738524
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ANTARCTICA  --  11 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3189 -1.9123 -0.5057  0.9009  7.6737 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.7839062  0.2010301  -8.874   <2e-16 ***
## x.var        0.0073740  0.0004553  16.196   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.776 on 762 degrees of freedom
## Multiple R-squared:  0.2561, Adjusted R-squared:  0.2551 
## F-statistic: 262.3 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7497 -0.4320 -0.1142  0.2035  1.7335 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.4029855  0.0454128  -8.874   <2e-16 ***
## x.var        0.0016658  0.0001029  16.196   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.627 on 762 degrees of freedom
## Multiple R-squared:  0.2561, Adjusted R-squared:  0.2551 
## F-statistic: 262.3 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5400  -0.2325  -0.0171  -0.0012   3.5332  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.815e+01  7.200e-01  -25.20   <2e-16 ***
## x.var        2.778e-02  9.871e-04   28.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3741.3  on 763  degrees of freedom
## Residual deviance:  486.0  on 762  degrees of freedom
## AIC: 796.07
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## ANTIGUA AND BARBUDA  --  7429 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1334.2  -825.2  -183.6   686.5  3361.2 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1354.4085    73.1428  -18.52   <2e-16 ***
## x.var           7.1178     0.1657   42.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1010 on 762 degrees of freedom
## Multiple R-squared:  0.7078, Adjusted R-squared:  0.7075 
## F-statistic:  1846 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.16148 -0.29109  0.06123  0.51805  0.96147 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.6299796  0.0509645   31.98   <2e-16 ***
## x.var       0.0104217  0.0001154   90.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7037 on 762 degrees of freedom
## Multiple R-squared:  0.9145, Adjusted R-squared:  0.9144 
## F-statistic:  8152 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -15.725   -7.439   -4.292    2.125   17.892  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.159e+00  5.091e-03   620.6   <2e-16 ***
## x.var       7.621e-03  7.862e-06   969.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1688408  on 763  degrees of freedom
## Residual deviance:   53600  on 762  degrees of freedom
## AIC: 59193
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## ARGENTINA  --  8868188 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -847161 -509710 -235619  377460 2176098 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1446153.9    50363.8  -28.71   <2e-16 ***
## x.var          10652.6      114.1   93.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 695400 on 762 degrees of freedom
## Multiple R-squared:  0.9196, Adjusted R-squared:  0.9195 
## F-statistic:  8721 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.6308 -1.2029  0.5732  1.9120  2.7200 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0136028  0.1794884   39.08   <2e-16 ***
## x.var       0.0150547  0.0004065   37.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.478 on 762 degrees of freedom
## Multiple R-squared:  0.6428, Adjusted R-squared:  0.6424 
## F-statistic:  1371 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -870.25  -704.64   -52.13   311.06   715.04  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.237e+01  8.179e-05  151172   <2e-16 ***
## x.var       4.926e-03  1.358e-07   36281   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1984924540  on 763  degrees of freedom
## Residual deviance:  225146934  on 762  degrees of freedom
## AIC: 225158023
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ARMENIA  --  417456 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38074 -15567   2431  13968  52128 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -49458.147   1485.292   -33.3   <2e-16 ***
## x.var          542.914      3.364   161.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20510 on 762 degrees of freedom
## Multiple R-squared:  0.9716, Adjusted R-squared:  0.9715 
## F-statistic: 2.605e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7334 -0.9308  0.5001  1.7019  2.1371 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.2938911  0.1555810   40.45   <2e-16 ***
## x.var       0.0112701  0.0003524   31.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 762 degrees of freedom
## Multiple R-squared:  0.5731, Adjusted R-squared:  0.5725 
## F-statistic:  1023 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -247.70  -112.43   -32.63   120.59   186.12  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.013e+01  2.907e-04   34830   <2e-16 ***
## x.var       3.913e-03  5.026e-07    7786   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 88282514  on 763  degrees of freedom
## Residual deviance: 14519333  on 762  degrees of freedom
## AIC: 14528765
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## AUSTRALIA  --  3124101 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -409084 -333041 -102057  120709 2431611 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -298595.98   37467.16   -7.97 5.81e-15 ***
## x.var          1297.23      84.86   15.29  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 517300 on 762 degrees of freedom
## Multiple R-squared:  0.2347, Adjusted R-squared:  0.2337 
## F-statistic: 233.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5884 -0.6530  0.1357  1.1694  1.6120 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.5517823  0.1033927   63.37   <2e-16 ***
## x.var       0.0091557  0.0002342   39.10   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.428 on 762 degrees of freedom
## Multiple R-squared:  0.6674, Adjusted R-squared:  0.6669 
## F-statistic:  1529 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -753.5  -113.6   177.9   290.9   703.9  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.496e+00  9.155e-04    2726   <2e-16 ***
## x.var       1.596e-02  1.299e-06   12282   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 530196794  on 763  degrees of freedom
## Residual deviance:  77076471  on 762  degrees of freedom
## AIC: 77085551
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## AUSTRIA  --  2550371 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -236800 -140556  -52978   90448 1270624 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -311079.89   16730.67  -18.59   <2e-16 ***
## x.var          2082.23      37.89   54.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 231000 on 762 degrees of freedom
## Multiple R-squared:  0.7985, Adjusted R-squared:  0.7982 
## F-statistic:  3020 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3394 -0.8748  0.6740  1.2305  1.8707 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9332985  0.1425123   48.65   <2e-16 ***
## x.var       0.0119444  0.0003228   37.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.968 on 762 degrees of freedom
## Multiple R-squared:  0.6425, Adjusted R-squared:  0.642 
## F-statistic:  1369 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -300.2  -251.5  -145.2   231.5   358.4  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.041e+01  2.021e-04   51520   <2e-16 ***
## x.var       5.372e-03  3.305e-07   16256   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 411469286  on 763  degrees of freedom
## Residual deviance:  42483288  on 762  degrees of freedom
## AIC: 42493420
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## AZERBAIJAN  --  779783 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -93103 -38499  -7034  42442 162674 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.293e+05  4.021e+03  -32.16   <2e-16 ***
## x.var        9.770e+02  9.107e+00  107.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55520 on 762 degrees of freedom
## Multiple R-squared:  0.9379, Adjusted R-squared:  0.9378 
## F-statistic: 1.151e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4355 -0.9161  0.5390  1.5009  2.0555 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.9429752  0.1458518   40.75   <2e-16 ***
## x.var       0.0126286  0.0003303   38.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.014 on 762 degrees of freedom
## Multiple R-squared:  0.6573, Adjusted R-squared:  0.6568 
## F-statistic:  1462 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -248.17  -158.82   -42.72   115.25   248.74  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.005e+01  2.647e-04   37955   <2e-16 ***
## x.var       4.827e-03  4.409e-07   10948   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 178849316  on 763  degrees of freedom
## Residual deviance:  20115850  on 762  degrees of freedom
## AIC: 20125416
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BAHAMAS  --  33081 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3536.6 -1902.5  -321.2  1172.4  7727.2 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5382.827    201.799  -26.67   <2e-16 ***
## x.var          40.787      0.457   89.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2786 on 762 degrees of freedom
## Multiple R-squared:  0.9127, Adjusted R-squared:  0.9126 
## F-statistic:  7964 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8941 -0.7687 -0.0557  1.2049  2.2704 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2668113  0.1102138   29.64   <2e-16 ***
## x.var       0.0116166  0.0002496   46.54   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.522 on 762 degrees of freedom
## Multiple R-squared:  0.7397, Adjusted R-squared:  0.7394 
## F-statistic:  2166 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -59.939  -44.301    2.865   14.370   44.477  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.878e+00  1.293e-03    5321   <2e-16 ***
## x.var       4.817e-03  2.154e-06    2236   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7412200  on 763  degrees of freedom
## Residual deviance:  797351  on 762  degrees of freedom
## AIC: 804546
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BAHRAIN  --  501643 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -41123 -23271  -5319  18003 152115 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -54869.947   2321.821  -23.63   <2e-16 ***
## x.var          529.316      5.259  100.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32060 on 762 degrees of freedom
## Multiple R-squared:  0.9301, Adjusted R-squared:   0.93 
## F-statistic: 1.013e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0586 -0.8843  0.5332  1.3266  1.9292 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.7176967  0.1391071   48.29   <2e-16 ***
## x.var       0.0103319  0.0003151   32.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.921 on 762 degrees of freedom
## Multiple R-squared:  0.5853, Adjusted R-squared:  0.5847 
## F-statistic:  1075 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -222.39  -128.34    17.08    55.18   201.78  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.928e+00  3.107e-04   31953   <2e-16 ***
## x.var       4.148e-03  5.316e-07    7802   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86945031  on 763  degrees of freedom
## Residual deviance: 11294833  on 762  degrees of freedom
## AIC: 11304331
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BANGLADESH  --  1938135 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -262020 -109531    3950  115208  281514 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -284142.11   10656.67  -26.66   <2e-16 ***
## x.var          2628.33      24.14  108.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 147100 on 762 degrees of freedom
## Multiple R-squared:  0.9396, Adjusted R-squared:  0.9395 
## F-statistic: 1.186e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.5238 -1.1456  0.4509  2.0200  2.9758 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9291048  0.1887258   36.72   <2e-16 ***
## x.var       0.0129276  0.0004274   30.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.606 on 762 degrees of freedom
## Multiple R-squared:  0.5455, Adjusted R-squared:  0.5449 
## F-statistic: 914.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -511.75  -271.10    40.38   151.90   322.81  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.146e+01  1.423e-04   80564   <2e-16 ***
## x.var       4.239e-03  2.425e-07   17476   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 429355931  on 763  degrees of freedom
## Residual deviance:  46643225  on 762  degrees of freedom
## AIC: 46653618
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BARBADOS  --  54344 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -10163  -5433  -1772   4786  32183 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8467.855    594.010  -14.26   <2e-16 ***
## x.var          40.090      1.345   29.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8201 on 762 degrees of freedom
## Multiple R-squared:  0.5382, Adjusted R-squared:  0.5376 
## F-statistic:   888 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.71706 -0.33902  0.02289  0.57799  1.23788 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0503208  0.0618643   33.14   <2e-16 ***
## x.var       0.0121225  0.0001401   86.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8541 on 762 degrees of freedom
## Multiple R-squared:  0.9076, Adjusted R-squared:  0.9075 
## F-statistic:  7486 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -45.191   -8.862   -2.730    9.860   40.827  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.110e+00  3.029e-03    1027   <2e-16 ***
## x.var       1.017e-02  4.498e-06    2261   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 11331887  on 763  degrees of freedom
## Residual deviance:   274054  on 762  degrees of freedom
## AIC: 280467
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## BELARUS  --  896319 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83909 -32093 -18873  35055 193817 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.270e+05  3.884e+03  -32.71   <2e-16 ***
## x.var        1.086e+03  8.796e+00  123.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 53620 on 762 degrees of freedom
## Multiple R-squared:  0.9524, Adjusted R-squared:  0.9523 
## F-statistic: 1.524e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.2136 -0.9234  0.6421  1.4482  2.4482 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.7896764  0.1628862   41.68   <2e-16 ***
## x.var       0.0114586  0.0003689   31.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.249 on 762 degrees of freedom
## Multiple R-squared:  0.5587, Adjusted R-squared:  0.5581 
## F-statistic: 964.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -301.94   -87.33   -30.43    93.99   173.93  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.044e+01  2.311e-04   45156   <2e-16 ***
## x.var       4.435e-03  3.908e-07   11348   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 179378654  on 763  degrees of freedom
## Residual deviance:  15057119  on 762  degrees of freedom
## AIC: 15066991
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BELGIUM  --  3529041 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -371724 -204334  -97931  112160 1450649 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -449312.65   24342.00  -18.46   <2e-16 ***
## x.var          3308.51      55.13   60.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 336100 on 762 degrees of freedom
## Multiple R-squared:  0.8254, Adjusted R-squared:  0.8251 
## F-statistic:  3601 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.9660 -0.9465  0.8014  1.3650  1.9820 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.8180091  0.1558071   50.18   <2e-16 ***
## x.var       0.0113834  0.0003529   32.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.151 on 762 degrees of freedom
## Multiple R-squared:  0.5773, Adjusted R-squared:  0.5767 
## F-statistic:  1041 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -427.6  -299.6  -166.9   279.8   370.0  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.119e+01  1.468e-04   76252   <2e-16 ***
## x.var       4.927e-03  2.437e-07   20221   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 607334617  on 763  degrees of freedom
## Residual deviance:  60645527  on 762  degrees of freedom
## AIC: 60656196
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BELIZE  --  56450 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -7715  -3444  -1051   2047  23547 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8368.043    442.032  -18.93   <2e-16 ***
## x.var          54.020      1.001   53.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6103 on 762 degrees of freedom
## Multiple R-squared:  0.7926, Adjusted R-squared:  0.7923 
## F-statistic:  2912 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0089 -1.0827 -0.4367  1.5851  2.4688 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1695559  0.1107571   19.59   <2e-16 ***
## x.var       0.0137599  0.0002508   54.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.529 on 762 degrees of freedom
## Multiple R-squared:  0.7979, Adjusted R-squared:  0.7977 
## F-statistic:  3009 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -62.63  -41.56  -15.47   18.34   69.88  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.615e+00  1.306e-03    5064   <2e-16 ***
## x.var       5.581e-03  2.121e-06    2631   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 11032609  on 763  degrees of freedom
## Residual deviance:  1166540  on 762  degrees of freedom
## AIC: 1173505
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BENIN  --  26567 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6822.8 -3619.4   124.1  4119.5  6715.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5929.7709   291.2150  -20.36   <2e-16 ***
## x.var          37.7395     0.6596   57.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4021 on 762 degrees of freedom
## Multiple R-squared:  0.8112, Adjusted R-squared:  0.811 
## F-statistic:  3274 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0833 -0.5587  0.1679  1.0093  1.9606 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5004496  0.1055660   33.16   <2e-16 ***
## x.var       0.0107928  0.0002391   45.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.458 on 762 degrees of freedom
## Multiple R-squared:  0.7278, Adjusted R-squared:  0.7275 
## F-statistic:  2038 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -58.297  -26.463   -0.946    9.687   56.579  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.193e+00  1.589e-03    3896   <2e-16 ***
## x.var       5.671e-03  2.574e-06    2203   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7427099  on 763  degrees of freedom
## Residual deviance:  447881  on 762  degrees of freedom
## AIC: 455013
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## BHUTAN  --  10514 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -780.5 -454.5 -179.7  163.1 6936.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -955.2812    63.7125  -14.99   <2e-16 ***
## x.var          5.9329     0.1443   41.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 879.7 on 762 degrees of freedom
## Multiple R-squared:  0.6893, Adjusted R-squared:  0.6889 
## F-statistic:  1690 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4450 -0.7951  0.3435  0.7664  1.1879 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0042304  0.0697208   28.75   <2e-16 ***
## x.var       0.0100180  0.0001579   63.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9626 on 762 degrees of freedom
## Multiple R-squared:  0.8408, Adjusted R-squared:  0.8406 
## F-statistic:  4025 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -29.061  -11.937   -1.159    5.925   53.888  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.225e+00  4.134e-03  1022.1   <2e-16 ***
## x.var       5.837e-03  6.662e-06   876.1   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1236102  on 763  degrees of freedom
## Residual deviance:  114070  on 762  degrees of freedom
## AIC: 119838
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## BOLIVIA  --  891851 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -78094 -44131 -10570  14300 224591 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -119736.94    4595.74  -26.05   <2e-16 ***
## x.var          1033.41      10.41   99.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 63450 on 762 degrees of freedom
## Multiple R-squared:  0.9282, Adjusted R-squared:  0.9281 
## F-statistic:  9857 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7015 -1.2557  0.4715  1.6641  2.8065 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.0801810  0.1680889   36.17   <2e-16 ***
## x.var       0.0126802  0.0003807   33.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.321 on 762 degrees of freedom
## Multiple R-squared:  0.5928, Adjusted R-squared:  0.5923 
## F-statistic:  1109 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -304.25  -159.29    24.61    97.19   171.06  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.041e+01  2.356e-04   44176   <2e-16 ***
## x.var       4.408e-03  3.987e-07   11057   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 173797756  on 763  degrees of freedom
## Residual deviance:  18188903  on 762  degrees of freedom
## AIC: 18198570
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BOSNIA AND HERZEGOVINA  --  369870 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42223 -14372  -2968  14974  56842 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -57333.046   1667.535  -34.38   <2e-16 ***
## x.var          491.427      3.777  130.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23020 on 762 degrees of freedom
## Multiple R-squared:  0.9569, Adjusted R-squared:  0.9569 
## F-statistic: 1.693e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1146 -0.9214  0.6418  1.4713  1.9947 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.5933740  0.1398618   39.99   <2e-16 ***
## x.var       0.0121226  0.0003168   38.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.931 on 762 degrees of freedom
## Multiple R-squared:  0.6578, Adjusted R-squared:  0.6573 
## F-statistic:  1465 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -199.27  -150.43   -49.77   119.97   205.58  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.649e+00  3.429e-04   28135   <2e-16 ***
## x.var       4.427e-03  5.801e-07    7632   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 88546971  on 763  degrees of freedom
## Residual deviance: 14286405  on 762  degrees of freedom
## AIC: 14295548
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BOTSWANA  --  262652 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -51186 -40042    989  30951  68078 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -62529.511   2705.125  -23.11   <2e-16 ***
## x.var          338.357      6.127   55.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37350 on 762 degrees of freedom
## Multiple R-squared:  0.8001, Adjusted R-squared:  0.7998 
## F-statistic:  3050 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4383 -0.9809  0.2020  1.3842  1.9229 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.3290224  0.1066904   21.83   <2e-16 ***
## x.var       0.0163135  0.0002416   67.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.473 on 762 degrees of freedom
## Multiple R-squared:  0.8568, Adjusted R-squared:  0.8566 
## F-statistic:  4558 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -182.41   -76.83   -35.71    17.94   157.29  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.323e+00  6.901e-04   10612   <2e-16 ***
## x.var       7.188e-03  1.075e-06    6686   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 78820503  on 763  degrees of freedom
## Residual deviance:  4198719  on 762  degrees of freedom
## AIC: 4206545
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BRAZIL  --  28493336 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2031572 -1160836  -392192  1259888  3921773 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3961055.2   107180.6  -36.96   <2e-16 ***
## x.var          39282.1      242.7  161.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1480000 on 762 degrees of freedom
## Multiple R-squared:  0.9717, Adjusted R-squared:  0.9717 
## F-statistic: 2.619e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6218 -1.2256  0.7272  2.1084  2.9970 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.1335332  0.2135139   42.78   <2e-16 ***
## x.var       0.0139505  0.0004836   28.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.948 on 762 degrees of freedom
## Multiple R-squared:  0.522,  Adjusted R-squared:  0.5214 
## F-statistic: 832.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2047.1  -1276.8    153.9    725.5   1287.8  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.428e+01  3.562e-05  400812   <2e-16 ***
## x.var       4.092e-03  6.109e-08   66991   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6516070501  on 763  degrees of freedom
## Residual deviance:  966850129  on 762  degrees of freedom
## AIC: 966862524
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BRUNEI  --  44334 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -6717  -3662   -265   3256  33190 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4520.6994   335.2899  -13.48   <2e-16 ***
## x.var          20.5034     0.7594   27.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4629 on 762 degrees of freedom
## Multiple R-squared:  0.4889, Adjusted R-squared:  0.4883 
## F-statistic:   729 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7317 -0.9562  0.2597  0.9690  1.9341 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.2954460  0.0872092   26.32   <2e-16 ***
## x.var       0.0092818  0.0001975   46.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.204 on 762 degrees of freedom
## Multiple R-squared:  0.7435, Adjusted R-squared:  0.7431 
## F-statistic:  2208 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -57.433  -16.421    1.109   17.362   72.156  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.0007081  0.0052942     189   <2e-16 ***
## x.var       0.0122191  0.0000077    1587   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6785337  on 763  degrees of freedom
## Residual deviance:  518166  on 762  degrees of freedom
## AIC: 523950
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BULGARIA  --  1080571 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -121914  -65707  -26730   47558  326916 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -179937.56    6562.19  -27.42   <2e-16 ***
## x.var          1221.98      14.86   82.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 90600 on 762 degrees of freedom
## Multiple R-squared:  0.8987, Adjusted R-squared:  0.8986 
## F-statistic:  6760 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0199 -0.9503  0.6507  1.2999  2.2120 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.3842228  0.1380144   39.01   <2e-16 ***
## x.var       0.0138197  0.0003126   44.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.906 on 762 degrees of freedom
## Multiple R-squared:  0.7195, Adjusted R-squared:  0.7191 
## F-statistic:  1955 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -255.90  -232.70   -70.22   157.10   305.97  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.936e+00  2.600e-04   38218   <2e-16 ***
## x.var       5.295e-03  4.261e-07   12429   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 241682079  on 763  degrees of freedom
## Residual deviance:  27605419  on 762  degrees of freedom
## AIC: 27614894
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BURKINA FASO  --  20751 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3550  -1392    -22   1544   2934 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2906.3506   134.5545  -21.60   <2e-16 ***
## x.var          29.7180     0.3047   97.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1858 on 762 degrees of freedom
## Multiple R-squared:  0.9258, Adjusted R-squared:  0.9257 
## F-statistic:  9510 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8762 -0.6589  0.6973  0.9628  1.3953 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.4358476  0.1095535   40.49   <2e-16 ***
## x.var       0.0091740  0.0002481   36.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.513 on 762 degrees of freedom
## Multiple R-squared:  0.6421, Adjusted R-squared:  0.6416 
## F-statistic:  1367 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -55.38  -33.01  -24.01   23.77   63.74  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.131e+00  1.278e-03    5580   <2e-16 ***
## x.var       4.034e-03  2.197e-06    1836   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5175656  on 763  degrees of freedom
## Residual deviance: 1029049  on 762  degrees of freedom
## AIC: 1036436
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BURMA  --  575508 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -145074  -54886  -14401   73023  132003 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -132827.04    5492.39  -24.18   <2e-16 ***
## x.var           824.21      12.44   66.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 75830 on 762 degrees of freedom
## Multiple R-squared:  0.8521, Adjusted R-squared:  0.8519 
## F-statistic:  4390 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2932 -0.9851 -0.0092  1.4037  2.9685 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2078551  0.1356823   23.64   <2e-16 ***
## x.var       0.0166982  0.0003073   54.34   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.873 on 762 degrees of freedom
## Multiple R-squared:  0.7949, Adjusted R-squared:  0.7946 
## F-statistic:  2953 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -286.96  -176.48   -53.31   113.06   197.70  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.155e+00  3.511e-04   26072   <2e-16 ***
## x.var       5.843e-03  5.658e-07   10327   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 174712897  on 763  degrees of freedom
## Residual deviance:  18708068  on 762  degrees of freedom
## AIC: 18716685
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## BURUNDI  --  38027 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7635.2 -5244.8  -605.5  2959.7 16327.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7834.9656   441.3198  -17.75   <2e-16 ***
## x.var          39.1032     0.9995   39.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6093 on 762 degrees of freedom
## Multiple R-squared:  0.6676, Adjusted R-squared:  0.6672 
## F-statistic:  1531 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7293 -0.5946  0.1807  0.6494  1.6665 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.8353044  0.0739050   24.83   <2e-16 ***
## x.var       0.0129558  0.0001674   77.40   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.02 on 762 degrees of freedom
## Multiple R-squared:  0.8872, Adjusted R-squared:  0.887 
## F-statistic:  5991 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -41.814  -12.422   -4.705    2.147   38.857  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.166e+00  2.513e-03    1658   <2e-16 ***
## x.var       8.621e-03  3.812e-06    2262   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9885760  on 763  degrees of freedom
## Residual deviance:  140221  on 762  degrees of freedom
## AIC: 146684
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## CABO VERDE  --  55870 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5854.4 -3298.0  -834.9  2418.5 10065.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -9484.8122   304.9134  -31.11   <2e-16 ***
## x.var          74.6938     0.6906  108.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4210 on 762 degrees of freedom
## Multiple R-squared:  0.9388, Adjusted R-squared:  0.9388 
## F-statistic: 1.17e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5285 -1.0379  0.5914  1.4030  1.9176 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.8450310  0.1254929   30.64   <2e-16 ***
## x.var       0.0117841  0.0002842   41.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.733 on 762 degrees of freedom
## Multiple R-squared:  0.6929, Adjusted R-squared:  0.6925 
## F-statistic:  1719 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -75.259  -51.042   -3.188   22.804   69.035  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.581e+00  9.287e-04    8163   <2e-16 ***
## x.var       4.681e-03  1.555e-06    3010   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13340330  on 763  degrees of freedom
## Residual deviance:  1510340  on 762  degrees of freedom
## AIC: 1518018
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CAMBODIA  --  128133 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44589 -22677   5116  21738  37615 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -37807.723   1805.307  -20.94   <2e-16 ***
## x.var          192.832      4.089   47.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24930 on 762 degrees of freedom
## Multiple R-squared:  0.7448, Adjusted R-squared:  0.7445 
## F-statistic:  2224 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1262 -0.8358  0.1504  0.8760  1.6342 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.0538166  0.0712519   28.82   <2e-16 ***
## x.var       0.0144776  0.0001614   89.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9838 on 762 degrees of freedom
## Multiple R-squared:  0.9135, Adjusted R-squared:  0.9134 
## F-statistic:  8049 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -219.57   -92.12   -44.54    17.02   161.09  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.086e+00  1.059e-03    5745   <2e-16 ***
## x.var       8.156e-03  1.620e-06    5036   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 53518853  on 763  degrees of freedom
## Residual deviance:  7163115  on 762  degrees of freedom
## AIC: 7170288
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## CAMEROON  --  119107 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -18506  -4145   2175   5810  17913 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -18087.62     640.34  -28.25   <2e-16 ***
## x.var          174.79       1.45  120.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8841 on 762 degrees of freedom
## Multiple R-squared:  0.9502, Adjusted R-squared:  0.9501 
## F-statistic: 1.453e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1079 -0.7301  0.6259  1.2620  2.2322 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.6571502  0.1422994   39.76   <2e-16 ***
## x.var       0.0102435  0.0003223   31.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.965 on 762 degrees of freedom
## Multiple R-squared:   0.57,  Adjusted R-squared:  0.5695 
## F-statistic:  1010 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -130.605   -51.572    -2.928    17.448   114.702  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.822e+00  5.402e-04   16330   <2e-16 ***
## x.var       4.144e-03  9.245e-07    4483   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 28189677  on 763  degrees of freedom
## Residual deviance:  3227392  on 762  degrees of freedom
## AIC: 3236031
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CANADA  --  3261722 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -316416 -161758 -106863   80450  964708 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -483337.21   19874.53  -24.32   <2e-16 ***
## x.var          3639.20      45.01   80.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 274400 on 762 degrees of freedom
## Multiple R-squared:  0.8956, Adjusted R-squared:  0.8955 
## F-statistic:  6536 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2040 -0.7894  0.7848  1.2502  1.8350 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.1601716  0.1421992   57.39   <2e-16 ***
## x.var       0.0109631  0.0003221   34.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.963 on 762 degrees of freedom
## Multiple R-squared:  0.6033, Adjusted R-squared:  0.6028 
## F-statistic:  1159 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -465.9  -257.7  -119.0   223.6   452.8  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.135e+01  1.375e-04   82549   <2e-16 ***
## x.var       4.840e-03  2.290e-07   21141   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 652396976  on 763  degrees of freedom
## Residual deviance:  59789918  on 762  degrees of freedom
## AIC: 59800752
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CENTRAL AFRICAN REPUBLIC  --  14187 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -2274  -1029    148   1004   2038 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -741.34      83.89  -8.837   <2e-16 ***
## x.var          17.96       0.19  94.537   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1158 on 762 degrees of freedom
## Multiple R-squared:  0.9214, Adjusted R-squared:  0.9213 
## F-statistic:  8937 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6205 -0.9424  0.1777  1.4341  2.6541 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.1342458  0.1340584   30.84   <2e-16 ***
## x.var       0.0091738  0.0003036   30.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.851 on 762 degrees of freedom
## Multiple R-squared:  0.5451, Adjusted R-squared:  0.5445 
## F-statistic: 912.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -60.372  -12.849    0.068   16.116   36.300  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.252e+00  1.345e-03    5390   <2e-16 ***
## x.var       3.213e-03  2.407e-06    1335   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2623303  on 763  degrees of freedom
## Residual deviance:  577440  on 762  degrees of freedom
## AIC: 584583
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHAD  --  7248 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -934.56 -488.16  -28.05  431.57  952.52 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -698.61041   37.91405  -18.43   <2e-16 ***
## x.var          9.89746    0.08587  115.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 523.5 on 762 degrees of freedom
## Multiple R-squared:  0.9458, Adjusted R-squared:  0.9457 
## F-statistic: 1.329e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1534 -0.9374  0.5588  1.1453  1.8806 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.6513661  0.1121758   32.55   <2e-16 ***
## x.var       0.0088080  0.0002541   34.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.549 on 762 degrees of freedom
## Multiple R-squared:  0.612,  Adjusted R-squared:  0.6115 
## F-statistic:  1202 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -38.050  -14.540   -5.134   12.004   30.883  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.367e+00  1.992e-03    3196   <2e-16 ***
## x.var       3.588e-03  3.497e-06    1026   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1548279  on 763  degrees of freedom
## Residual deviance:  303021  on 762  degrees of freedom
## AIC: 309682
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHILE  --  2921131 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -201554 -104324  -14761   68847  799068 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -273579.17   10629.31  -25.74   <2e-16 ***
## x.var          3135.66      24.07  130.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 146800 on 762 degrees of freedom
## Multiple R-squared:  0.957,  Adjusted R-squared:  0.957 
## F-statistic: 1.697e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3821 -0.9875  0.5703  1.6528  2.6888 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.0175840  0.1790322   44.78   <2e-16 ***
## x.var       0.0113910  0.0004055   28.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.472 on 762 degrees of freedom
## Multiple R-squared:  0.5088, Adjusted R-squared:  0.5081 
## F-statistic: 789.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -612.61  -250.45    59.15   121.63   371.41  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.193e+01  1.191e-04  100145   <2e-16 ***
## x.var       3.847e-03  2.066e-07   18625   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 486023020  on 763  degrees of freedom
## Residual deviance:  66314246  on 762  degrees of freedom
## AIC: 66325047
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CHINA  --  137160 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -69834  -2274   2070   4191  15639 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 70314.983    730.637   96.24   <2e-16 ***
## x.var          67.024      1.655   40.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10090 on 762 degrees of freedom
## Multiple R-squared:  0.6828, Adjusted R-squared:  0.6824 
## F-statistic:  1641 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7207 -0.0510  0.0780  0.1457  0.2244 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.103e+01  3.042e-02   362.5   <2e-16 ***
## x.var       1.027e-03  6.890e-05    14.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.42 on 762 degrees of freedom
## Multiple R-squared:  0.2256, Adjusted R-squared:  0.2246 
## F-statistic:   222 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -372.33    -6.35     9.63    15.35    36.96  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.119e+01  2.510e-04   44591   <2e-16 ***
## x.var       7.019e-04  5.334e-07    1316   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3485227  on 763  degrees of freedom
## Residual deviance: 1741244  on 762  degrees of freedom
## AIC: 1751377
## 
## Number of Fisher Scoring iterations: 4
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.78582  -0.02500   0.03309   0.05629   0.09503  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.118e+01  1.021e-02 1095.09   <2e-16 ***
## x.var       7.342e-04  2.312e-05   31.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01986225)
## 
##     Null deviance: 78.139  on 763  degrees of freedom
## Residual deviance: 59.061  on 762  degrees of freedom
## AIC: 17679
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COLOMBIA  --  6054307 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -497934 -370456 -147661  319084 1077183 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.086e+06  3.133e+04  -34.68   <2e-16 ***
## x.var        9.174e+03  7.095e+01  129.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 432500 on 762 degrees of freedom
## Multiple R-squared:  0.9564, Adjusted R-squared:  0.9563 
## F-statistic: 1.672e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.905 -1.200  0.585  1.976  2.832 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.2674866  0.1878024   38.70   <2e-16 ***
## x.var       0.0145010  0.0004253   34.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.593 on 762 degrees of freedom
## Multiple R-squared:  0.604,  Adjusted R-squared:  0.6035 
## F-statistic:  1162 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -902.36  -707.88    -7.71   376.70   757.73  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.255e+01  8.008e-05  156670   <2e-16 ***
## x.var       4.468e-03  1.352e-07   33039   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1636003237  on 763  degrees of freedom
## Residual deviance:  238865354  on 762  degrees of freedom
## AIC: 238876469
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COMOROS  --  8024 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1388.98  -708.17   -20.63   602.83  1897.31 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1247.7243    59.9010  -20.83   <2e-16 ***
## x.var           9.8003     0.1357   72.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 827 on 762 degrees of freedom
## Multiple R-squared:  0.8726, Adjusted R-squared:  0.8724 
## F-statistic:  5218 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2419 -1.1614  0.5605  1.2514  1.7909 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.1493963  0.1054963   20.37   <2e-16 ***
## x.var       0.0110357  0.0002389   46.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.457 on 762 degrees of freedom
## Multiple R-squared:  0.7368, Adjusted R-squared:  0.7365 
## F-statistic:  2133 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -28.515  -17.843  -11.663    9.593   39.821  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.5434224  0.0025685    2158   <2e-16 ***
## x.var       0.0046898  0.0000043    1091   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1878878  on 763  degrees of freedom
## Residual deviance:  324289  on 762  degrees of freedom
## AIC: 330378
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CONGO (BRAZZAVILLE)  --  23925 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2149.8  -805.8  -529.8   584.8  3820.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2914.7184    95.9712  -30.37   <2e-16 ***
## x.var          30.8643     0.2174  142.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1325 on 762 degrees of freedom
## Multiple R-squared:  0.9636, Adjusted R-squared:  0.9635 
## F-statistic: 2.016e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7302 -0.8178  0.3513  1.2521  2.0589 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.213279   0.122323   34.44   <2e-16 ***
## x.var       0.009752   0.000277   35.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.689 on 762 degrees of freedom
## Multiple R-squared:  0.6192, Adjusted R-squared:  0.6187 
## F-statistic:  1239 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -59.099  -17.949    3.227   19.187   26.384  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.215e+00  1.236e-03    5836   <2e-16 ***
## x.var       3.973e-03  2.132e-06    1864   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4837812  on 763  degrees of freedom
## Residual deviance:  588837  on 762  degrees of freedom
## AIC: 596212
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CONGO (KINSHASA)  --  85876 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9864.0 -5717.5  -370.5  3195.1 16971.8 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13363.52     498.92  -26.79   <2e-16 ***
## x.var          110.42       1.13   97.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6888 on 762 degrees of freedom
## Multiple R-squared:  0.9261, Adjusted R-squared:  0.926 
## F-statistic:  9549 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5403 -0.7978  0.2808  1.1668  2.1681 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.03027    0.13246   37.97   <2e-16 ***
## x.var        0.01041    0.00030   34.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.829 on 762 degrees of freedom
## Multiple R-squared:  0.6123, Adjusted R-squared:  0.6118 
## F-statistic:  1204 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -91.124  -31.092    5.747   23.453   47.669  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.081e+00  7.398e-04   10923   <2e-16 ***
## x.var       4.530e-03  1.246e-06    3635   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 18335904  on 763  degrees of freedom
## Residual deviance:  1321152  on 762  degrees of freedom
## AIC: 1329352
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COSTA RICA  --  797030 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -86380 -40164 -16374  39317 173200 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.351e+05  4.122e+03  -32.78   <2e-16 ***
## x.var        9.934e+02  9.336e+00  106.40   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56920 on 762 degrees of freedom
## Multiple R-squared:  0.9369, Adjusted R-squared:  0.9369 
## F-statistic: 1.132e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0453 -0.8448  0.1866  1.6284  2.3758 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.4510216  0.1446253   37.69   <2e-16 ***
## x.var       0.0135054  0.0003276   41.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.997 on 762 degrees of freedom
## Multiple R-squared:  0.6905, Adjusted R-squared:  0.6901 
## F-statistic:  1700 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -273.21  -198.51    35.46   107.50   146.03  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.987e+00  2.683e-04   37226   <2e-16 ***
## x.var       4.933e-03  4.452e-07   11082   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 183172962  on 763  degrees of freedom
## Residual deviance:  18851713  on 762  degrees of freedom
## AIC: 18861150
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## COTE D'IVOIRE  --  81410 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7198.6 -2622.9  -171.7  2353.5  8455.2 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8068.1294   270.3811  -29.84   <2e-16 ***
## x.var         108.9165     0.6124  177.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3733 on 762 degrees of freedom
## Multiple R-squared:  0.9765, Adjusted R-squared:  0.9764 
## F-statistic: 3.163e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9869 -0.8427  0.5462  1.2583  2.3106 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.5038391  0.1439086   38.24   <2e-16 ***
## x.var       0.0098593  0.0003259   30.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.987 on 762 degrees of freedom
## Multiple R-squared:  0.5456, Adjusted R-squared:  0.545 
## F-statistic:   915 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -123.25   -44.51    12.54    32.39    76.49  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.727e+00  6.081e-04   14350   <2e-16 ***
## x.var       3.639e-03  1.065e-06    3417   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 16360180  on 763  degrees of freedom
## Residual deviance:  2482748  on 762  degrees of freedom
## AIC: 2491149
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CROATIA  --  1047108 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -111267  -69955  -24581   44402  372913 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -164657.17    7261.89  -22.67   <2e-16 ***
## x.var          1097.97      16.45   66.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 100300 on 762 degrees of freedom
## Multiple R-squared:  0.854,  Adjusted R-squared:  0.8538 
## F-statistic:  4457 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9002 -0.9760  0.5057  1.0496  2.2560 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.4437161  0.1282545   42.45   <2e-16 ***
## x.var       0.0134254  0.0002905   46.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.771 on 762 degrees of freedom
## Multiple R-squared:  0.7371, Adjusted R-squared:  0.7367 
## F-statistic:  2136 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -258.5  -209.9   -84.7   157.3   266.9  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.760e+00  2.795e-04   34920   <2e-16 ***
## x.var       5.393e-03  4.566e-07   11810   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 221487807  on 763  degrees of freedom
## Residual deviance:  26323877  on 762  degrees of freedom
## AIC: 26333303
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CUBA  --  1066927 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -301403 -215945    9152  214408  295136 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -296619.7    15055.5  -19.70   <2e-16 ***
## x.var          1484.2       34.1   43.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 207900 on 762 degrees of freedom
## Multiple R-squared:  0.7132, Adjusted R-squared:  0.7128 
## F-statistic:  1894 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7173 -0.0580  0.3465  0.6590  1.8743 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.9466677  0.1036862   38.06   <2e-16 ***
## x.var       0.0154136  0.0002348   65.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.432 on 762 degrees of freedom
## Multiple R-squared:  0.8497, Adjusted R-squared:  0.8495 
## F-statistic:  4308 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -569.80  -134.84   -90.59   -71.22   472.51  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.844e+00  4.046e-04   19386   <2e-16 ***
## x.var       8.563e-03  6.143e-07   13939   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 398979771  on 763  degrees of freedom
## Residual deviance:  30705108  on 762  degrees of freedom
## AIC: 30713943
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CYPRUS  --  313406 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -30807 -24053 -10010  12195 143971 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -50624.77    2402.01  -21.08   <2e-16 ***
## x.var          288.04       5.44   52.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33160 on 762 degrees of freedom
## Multiple R-squared:  0.7863, Adjusted R-squared:  0.786 
## F-statistic:  2803 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7010 -0.6282  0.3933  0.9892  1.5134 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0744849  0.1042750   39.07   <2e-16 ***
## x.var       0.0130525  0.0002362   55.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.44 on 762 degrees of freedom
## Multiple R-squared:  0.8003, Adjusted R-squared:  0.8001 
## F-statistic:  3055 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -123.46   -73.34   -44.59    50.73   112.56  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.579e+00  6.778e-04   11181   <2e-16 ***
## x.var       6.591e-03  1.071e-06    6156   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 63596411  on 763  degrees of freedom
## Residual deviance:  3907934  on 762  degrees of freedom
## AIC: 3916179
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## CZECHIA  --  3538222 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -439018 -229201  -20364  196334  948722 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -528279.25   20472.80  -25.80   <2e-16 ***
## x.var          4080.86      46.37   88.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 282700 on 762 degrees of freedom
## Multiple R-squared:  0.9104, Adjusted R-squared:  0.9103 
## F-statistic:  7746 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.0500 -1.0444  0.5713  1.3975  2.3353 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.4997028  0.1527256   42.56   <2e-16 ***
## x.var       0.0141110  0.0003459   40.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.109 on 762 degrees of freedom
## Multiple R-squared:  0.6859, Adjusted R-squared:  0.6855 
## F-statistic:  1664 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -646.0  -505.3  -237.6   282.8   722.0  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.153e+01  1.274e-04   90547   <2e-16 ***
## x.var       4.747e-03  2.128e-07   22307   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 808764626  on 763  degrees of freedom
## Residual deviance: 154864173  on 762  degrees of freedom
## AIC: 154874598
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## DENMARK  --  2710998 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -273150 -164198  -83376   75773 1886010 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -259092.08   23271.82  -11.13   <2e-16 ***
## x.var          1418.95      52.71   26.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 321300 on 762 degrees of freedom
## Multiple R-squared:  0.4875, Adjusted R-squared:  0.4868 
## F-statistic: 724.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8167 -0.6585  0.7187  1.1561  1.5914 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.395091   0.133344   47.96   <2e-16 ***
## x.var       0.011711   0.000302   38.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.841 on 762 degrees of freedom
## Multiple R-squared:  0.6637, Adjusted R-squared:  0.6632 
## F-statistic:  1504 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -411.90  -120.86   -55.98   120.09   864.30  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.867e+00  3.285e-04   26992   <2e-16 ***
## x.var       7.030e-03  5.136e-07   13689   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 344745066  on 763  degrees of freedom
## Residual deviance:  36688082  on 762  degrees of freedom
## AIC: 36697730
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## DIAMOND PRINCESS  --  712 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -623.96  -15.35   17.64   51.34   80.80 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 621.12824    8.18219  75.912   <2e-16 ***
## x.var         0.17668    0.01853   9.534   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 113 on 762 degrees of freedom
## Multiple R-squared:  0.1066, Adjusted R-squared:  0.1054 
## F-statistic: 90.89 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9582 -0.1046  0.1287  0.3608  0.5768 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.9385055  0.0666233  89.136  < 2e-16 ***
## x.var       0.0012295  0.0001509   8.148 1.51e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9198 on 762 degrees of freedom
## Multiple R-squared:  0.08015,    Adjusted R-squared:  0.07894 
## F-statistic: 66.39 on 1 and 762 DF,  p-value: 1.512e-15
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -35.380   -0.570    0.708    1.993    3.099  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.435e+00  2.830e-03 2274.02   <2e-16 ***
## x.var       2.567e-04  6.257e-06   41.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 28981  on 763  degrees of freedom
## Residual deviance: 27296  on 762  degrees of freedom
## AIC: 33570
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## DJIBOUTI  --  15543 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2183.40  -741.74   -17.41   765.67  1751.91 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -253.2447    74.3826  -3.405 0.000697 ***
## x.var         20.8245     0.1685 123.612  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1027 on 762 degrees of freedom
## Multiple R-squared:  0.9525, Adjusted R-squared:  0.9524 
## F-statistic: 1.528e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4017 -0.7134  0.4440  1.2533  2.2921 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9473965  0.1359074   36.40   <2e-16 ***
## x.var       0.0081127  0.0003078   26.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.876 on 762 degrees of freedom
## Multiple R-squared:  0.4769, Adjusted R-squared:  0.4762 
## F-statistic: 694.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -70.238  -17.184    2.225   18.379   32.634  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.636e+00  1.154e-03    6619   <2e-16 ***
## x.var       2.916e-03  2.097e-06    1391   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2781929  on 763  degrees of freedom
## Residual deviance:  612101  on 762  degrees of freedom
## AIC: 619553
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## DOMINICA  --  10991 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2889.5 -1500.8  -114.4  1230.1  6072.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1999.6058   134.9342  -14.82   <2e-16 ***
## x.var           9.0551     0.3056   29.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1863 on 762 degrees of freedom
## Multiple R-squared:  0.5353, Adjusted R-squared:  0.5347 
## F-statistic: 877.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4877 -0.4108  0.1004  0.4699  1.2952 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.6555495  0.0484490   13.53   <2e-16 ***
## x.var       0.0110306  0.0001097  100.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6689 on 762 degrees of freedom
## Multiple R-squared:  0.9299, Adjusted R-squared:  0.9298 
## F-statistic: 1.011e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -34.134   -5.252    0.637    3.639   26.629  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.157e-01  8.042e-03   14.39   <2e-16 ***
## x.var       1.232e-02  1.169e-05 1053.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2890419  on 763  degrees of freedom
## Residual deviance:  111034  on 762  degrees of freedom
## AIC: 116044
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## DOMINICAN REPUBLIC  --  572596 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31198 -17935  -5396   7008  89447 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -60946.260   1844.701  -33.04   <2e-16 ***
## x.var          717.826      4.178  171.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25470 on 762 degrees of freedom
## Multiple R-squared:  0.9748, Adjusted R-squared:  0.9748 
## F-statistic: 2.952e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.2088 -0.9486  0.5866  1.5797  2.3086 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.7817426  0.1637346   41.42   <2e-16 ***
## x.var       0.0109490  0.0003708   29.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.261 on 762 degrees of freedom
## Multiple R-squared:  0.5336, Adjusted R-squared:  0.533 
## F-statistic: 871.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -297.74  -130.83    23.68   102.98   170.33  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.048e+01  2.467e-04   42500   <2e-16 ***
## x.var       3.808e-03  4.285e-07    8887   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 111522603  on 763  degrees of freedom
## Residual deviance:  16299723  on 762  degrees of freedom
## AIC: 16309436
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ECUADOR  --  820541 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -58856 -26882 -11866  24493 163711 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -83495.506   2849.643   -29.3   <2e-16 ***
## x.var          970.696      6.454   150.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39340 on 762 degrees of freedom
## Multiple R-squared:  0.9674, Adjusted R-squared:  0.9674 
## F-statistic: 2.262e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.7460 -0.8621  0.6635  1.7003  2.0142 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.3374765  0.1645344   44.59   <2e-16 ***
## x.var       0.0104743  0.0003726   28.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.272 on 762 degrees of freedom
## Multiple R-squared:  0.509,  Adjusted R-squared:  0.5084 
## F-statistic:   790 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -342.45  -144.27     7.47   106.50   190.59  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.077e+01  2.131e-04   50559   <2e-16 ***
## x.var       3.827e-03  3.698e-07   10348   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 148718449  on 763  degrees of freedom
## Residual deviance:  19403238  on 762  degrees of freedom
## AIC: 19413240
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EGYPT  --  475341 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -29380 -13550  -3333  11883  67994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -41302.596   1292.543  -31.95   <2e-16 ***
## x.var          587.238      2.927  200.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17850 on 762 degrees of freedom
## Multiple R-squared:  0.9814, Adjusted R-squared:  0.9814 
## F-statistic: 4.024e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3213 -0.9065  0.4730  1.3987  2.4846 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0892644  0.1566383   45.26   <2e-16 ***
## x.var       0.0100880  0.0003548   28.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.163 on 762 degrees of freedom
## Multiple R-squared:  0.5148, Adjusted R-squared:  0.5142 
## F-statistic: 808.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -287.37   -85.11    29.19    85.02   122.62  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.045e+01  2.584e-04   40449   <2e-16 ***
## x.var       3.584e-03  4.537e-07    7900   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86357718  on 763  degrees of freedom
## Residual deviance: 12540366  on 762  degrees of freedom
## AIC: 12550086
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EL SALVADOR  --  147786 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -7065  -3748  -1496   2351  18664 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.801e+04  3.954e+02  -45.57   <2e-16 ***
## x.var        1.949e+02  8.954e-01  217.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5459 on 762 degrees of freedom
## Multiple R-squared:  0.9842, Adjusted R-squared:  0.9841 
## F-statistic: 4.737e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7628 -1.0380  0.4101  1.6454  2.5318 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.103895   0.152329   33.51   <2e-16 ***
## x.var       0.011560   0.000345   33.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.103 on 762 degrees of freedom
## Multiple R-squared:  0.5957, Adjusted R-squared:  0.5952 
## F-statistic:  1123 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -151.65   -76.71    18.61    45.20    78.55  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.085e+00  4.880e-04   18618   <2e-16 ***
## x.var       3.937e-03  8.426e-07    4672   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 31190141  on 763  degrees of freedom
## Residual deviance:  4572432  on 762  degrees of freedom
## AIC: 4581010
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## EQUATORIAL GUINEA  --  15874 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1740.1  -822.8  -187.4   811.9  2027.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1162.7563    69.1090  -16.82   <2e-16 ***
## x.var          20.6066     0.1565  131.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 954.2 on 762 degrees of freedom
## Multiple R-squared:  0.9579, Adjusted R-squared:  0.9578 
## F-statistic: 1.733e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8897 -0.7928  0.3104  1.3523  2.3618 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.4209742  0.1270048   34.81   <2e-16 ***
## x.var       0.0088444  0.0002876   30.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.754 on 762 degrees of freedom
## Multiple R-squared:  0.5537, Adjusted R-squared:  0.5531 
## F-statistic: 945.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -59.136  -13.415    5.393   10.370   36.783  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.248e+00  1.316e-03    5506   <2e-16 ***
## x.var       3.395e-03  2.333e-06    1456   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2969737  on 763  degrees of freedom
## Residual deviance:  502106  on 762  degrees of freedom
## AIC: 509375
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ERITREA  --  9694 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1777.03  -693.23    48.99   762.50  2044.41 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2058.1490    68.6954  -29.96   <2e-16 ***
## x.var          13.7400     0.1556   88.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 948.5 on 762 degrees of freedom
## Multiple R-squared:  0.911,  Adjusted R-squared:  0.9109 
## F-statistic:  7799 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1086 -0.5995  0.4873  0.8423  1.1719 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.4697824  0.0805174   30.67   <2e-16 ***
## x.var       0.0108267  0.0001824   59.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.112 on 762 degrees of freedom
## Multiple R-squared:  0.8222, Adjusted R-squared:  0.822 
## F-statistic:  3525 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -33.20  -19.93  -15.47   16.40   35.19  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.383e+00  2.495e-03    2157   <2e-16 ***
## x.var       5.386e-03  4.078e-06    1321   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2740337  on 763  degrees of freedom
## Residual deviance:  300327  on 762  degrees of freedom
## AIC: 306673
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ESTONIA  --  474550 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -48204 -30903  -7813  15638 228043 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -69652.956   3188.430  -21.85   <2e-16 ***
## x.var          413.821      7.221   57.30   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 44020 on 762 degrees of freedom
## Multiple R-squared:  0.8117, Adjusted R-squared:  0.8114 
## F-statistic:  3284 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2051 -0.6221  0.3348  1.0957  1.4632 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7526763  0.1084971   43.80   <2e-16 ***
## x.var       0.0125684  0.0002457   51.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.498 on 762 degrees of freedom
## Multiple R-squared:  0.7744, Adjusted R-squared:  0.7741 
## F-statistic:  2616 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -139.44   -98.64   -63.92    38.21   201.45  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.227e+00  5.268e-04   15616   <2e-16 ***
## x.var       6.182e-03  8.409e-07    7352   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 89931801  on 763  degrees of freedom
## Residual deviance:  8158183  on 762  degrees of freedom
## AIC: 8166825
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ESWATINI  --  69050 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -14297  -6636   -547   5166  17229 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13898.010    602.009  -23.09   <2e-16 ***
## x.var           89.037      1.363   65.30   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8312 on 762 degrees of freedom
## Multiple R-squared:  0.8484, Adjusted R-squared:  0.8482 
## F-statistic:  4264 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2326 -1.0119  0.2131  1.3286  2.1316 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5964890  0.1171195   30.71   <2e-16 ***
## x.var       0.0122328  0.0002653   46.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.617 on 762 degrees of freedom
## Multiple R-squared:  0.7362, Adjusted R-squared:  0.7359 
## F-statistic:  2127 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -65.209  -36.919   -4.876   15.608   57.391  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.081e+00  1.027e-03    6897   <2e-16 ***
## x.var       5.628e-03  1.665e-06    3381   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 17345187  on 763  degrees of freedom
## Residual deviance:   975169  on 762  degrees of freedom
## AIC: 982797
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ETHIOPIA  --  468495 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37235 -20482  -6463  16391  77303 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -77973.919   1992.842  -39.13   <2e-16 ***
## x.var          670.726      4.514  148.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27510 on 762 degrees of freedom
## Multiple R-squared:  0.9666, Adjusted R-squared:  0.9666 
## F-statistic: 2.208e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6482 -1.4573  0.4148  1.7081  2.8617 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9380826  0.1589267   31.07   <2e-16 ***
## x.var       0.0139245  0.0003599   38.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.194 on 762 degrees of freedom
## Multiple R-squared:  0.6626, Adjusted R-squared:  0.6622 
## F-statistic:  1497 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -261.44  -188.60    26.05    79.87   206.95  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.967e+00  2.929e-04   34023   <2e-16 ***
## x.var       4.417e-03  4.957e-07    8912   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 118155921  on 763  degrees of freedom
## Residual deviance:  16986282  on 762  degrees of freedom
## AIC: 16995435
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## FIJI  --  63687 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -24639 -12195   2625  12938  17746 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17831.322    995.182  -17.92   <2e-16 ***
## x.var           85.599      2.254   37.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13740 on 762 degrees of freedom
## Multiple R-squared:  0.6543, Adjusted R-squared:  0.6539 
## F-statistic:  1442 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.51282 -0.90600  0.07585  0.99296  2.01095 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.219779   0.090938  -2.417   0.0159 *  
## x.var        0.015447   0.000206  75.000   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.256 on 762 degrees of freedom
## Multiple R-squared:  0.8807, Adjusted R-squared:  0.8805 
## F-statistic:  5625 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -151.42   -66.36   -29.33   -13.65   160.94  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.206e+00  1.955e-03    2152   <2e-16 ***
## x.var       9.688e-03  2.920e-06    3318   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 27116844  on 763  degrees of freedom
## Residual deviance:  4178236  on 762  degrees of freedom
## AIC: 4183871
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## FINLAND  --  629727 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -60886 -42528 -33221  25329 372030 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -77908.63    5466.73  -14.25   <2e-16 ***
## x.var          439.27      12.38   35.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 75480 on 762 degrees of freedom
## Multiple R-squared:  0.6229, Adjusted R-squared:  0.6224 
## F-statistic:  1259 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0864 -0.5779  0.5933  0.8280  1.5763 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.0137940  0.1091900   55.08   <2e-16 ***
## x.var       0.0103775  0.0002473   41.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.508 on 762 degrees of freedom
## Multiple R-squared:  0.698,  Adjusted R-squared:  0.6976 
## F-statistic:  1761 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -154.37   -45.76   -14.36    48.23   234.49  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.933e+00  5.579e-04   14219   <2e-16 ***
## x.var       6.688e-03  8.792e-07    7606   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 96489478  on 763  degrees of freedom
## Residual deviance:  4488011  on 762  degrees of freedom
## AIC: 4497021
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## FRANCE  --  22638153 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2151810 -1264474  -825252   351712 11235751 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2789796.7   171787.1  -16.24   <2e-16 ***
## x.var          18576.2      389.1   47.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2372000 on 762 degrees of freedom
## Multiple R-squared:  0.7495, Adjusted R-squared:  0.7491 
## F-statistic:  2280 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.2054 -0.8610  0.8136  1.2580  1.8025 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.1816360  0.1463939   62.72   <2e-16 ***
## x.var       0.0118604  0.0003316   35.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.021 on 762 degrees of freedom
## Multiple R-squared:  0.6268, Adjusted R-squared:  0.6263 
## F-statistic:  1280 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1178.1   -747.6   -299.4    547.4   1105.0  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.258e+01  6.805e-05  184893   <2e-16 ***
## x.var       5.401e-03  1.112e-07   48588   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3680731441  on 763  degrees of freedom
## Residual deviance:  374962529  on 762  degrees of freedom
## AIC: 374974410
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GABON  --  47520 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -6207  -2468    325   1712   7713 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6157.7211   239.2263  -25.74   <2e-16 ***
## x.var          61.3635     0.5418  113.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3303 on 762 degrees of freedom
## Multiple R-squared:  0.9439, Adjusted R-squared:  0.9439 
## F-statistic: 1.283e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1905 -0.8651  0.4664  1.3369  2.3107 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6623944  0.1361481   34.24   <2e-16 ***
## x.var       0.0101565  0.0003084   32.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.88 on 762 degrees of freedom
## Multiple R-squared:  0.5874, Adjusted R-squared:  0.5869 
## F-statistic:  1085 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -80.705  -17.979   -0.391   20.306   47.142  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.821e+00  8.992e-04    8697   <2e-16 ***
## x.var       4.083e-03  1.543e-06    2646   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9624099  on 763  degrees of freedom
## Residual deviance:  972807  on 762  degrees of freedom
## AIC: 980645
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GAMBIA  --  11924 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1389.4  -609.1   -76.9   661.4  1596.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1613.9043    55.1397  -29.27   <2e-16 ***
## x.var          17.1094     0.1249  137.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 761.3 on 762 degrees of freedom
## Multiple R-squared:  0.961,  Adjusted R-squared:  0.9609 
## F-statistic: 1.877e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3606 -1.1269 -0.0597  1.2909  2.7532 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.7410439  0.1168124   23.46   <2e-16 ***
## x.var       0.0112647  0.0002646   42.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.613 on 762 degrees of freedom
## Multiple R-squared:  0.7041, Adjusted R-squared:  0.7037 
## F-statistic:  1813 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -50.039  -33.817    5.562   16.614   31.229  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.627e+00  1.660e-03    3993   <2e-16 ***
## x.var       3.971e-03  2.862e-06    1387   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2924430  on 763  degrees of freedom
## Residual deviance:  570053  on 762  degrees of freedom
## AIC: 576735
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GEORGIA  --  1575999 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -169631 -114045  -47156   89846  695588 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -245201.39   10958.26  -22.38   <2e-16 ***
## x.var          1473.31      24.82   59.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 151300 on 762 degrees of freedom
## Multiple R-squared:  0.8222, Adjusted R-squared:  0.822 
## F-statistic:  3524 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7875 -0.8871  0.0395  1.1233  2.7527 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2344114  0.1227871   34.49   <2e-16 ***
## x.var       0.0158030  0.0002781   56.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.695 on 762 degrees of freedom
## Multiple R-squared:  0.8091, Adjusted R-squared:  0.8088 
## F-statistic:  3229 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -318.30  -210.76   -27.21    83.52   294.07  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.563e+00  2.746e-04   34829   <2e-16 ***
## x.var       6.088e-03  4.394e-07   13856   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 315123031  on 763  degrees of freedom
## Residual deviance:  27343004  on 762  degrees of freedom
## AIC: 27352197
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GERMANY  --  14252200 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1186979  -792951  -388747   491176  7210021 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1711698.5    92047.2  -18.60   <2e-16 ***
## x.var          11458.0      208.5   54.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1271000 on 762 degrees of freedom
## Multiple R-squared:  0.7986, Adjusted R-squared:  0.7983 
## F-statistic:  3021 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.1728 -0.8095  0.7012  1.2045  1.7909 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.1172956  0.1410667   64.63   <2e-16 ***
## x.var       0.0110957  0.0003195   34.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.948 on 762 degrees of freedom
## Multiple R-squared:  0.6128, Adjusted R-squared:  0.6123 
## F-statistic:  1206 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -678.3  -484.4  -309.3   445.0   901.0  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.212e+01  8.617e-05  140653   <2e-16 ***
## x.var       5.372e-03  1.409e-07   38132   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2211325557  on 763  degrees of freedom
## Residual deviance:  180981873  on 762  degrees of freedom
## AIC: 180993477
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GHANA  --  159124 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9593.6 -5547.2   -13.9  4795.1 12512.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -12732.608    420.783  -30.26   <2e-16 ***
## x.var          219.688      0.953  230.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5810 on 762 degrees of freedom
## Multiple R-squared:  0.9859, Adjusted R-squared:  0.9858 
## F-statistic: 5.314e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5442 -0.9633  0.4580  1.5661  2.5365 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.0064771  0.1586866   37.85   <2e-16 ***
## x.var       0.0103406  0.0003594   28.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.191 on 762 degrees of freedom
## Multiple R-squared:  0.5207, Adjusted R-squared:  0.5201 
## F-statistic: 827.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -189.90   -66.97    20.55    50.63   101.08  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.599e+00  4.052e-04   23692   <2e-16 ***
## x.var       3.415e-03  7.172e-07    4761   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 32363988  on 763  degrees of freedom
## Residual deviance:  5918913  on 762  degrees of freedom
## AIC: 5927835
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GREECE  --  2353577 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -232357 -188834 -134065  103464 1248589 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -356344.58   20636.91  -17.27   <2e-16 ***
## x.var          1912.74      46.74   40.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 284900 on 762 degrees of freedom
## Multiple R-squared:  0.6873, Adjusted R-squared:  0.6869 
## F-statistic:  1675 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9219 -0.7239  0.5162  0.9891  1.7791 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.4351169  0.1170872   46.42   <2e-16 ***
## x.var       0.0139075  0.0002652   52.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.617 on 762 degrees of freedom
## Multiple R-squared:  0.7831, Adjusted R-squared:  0.7828 
## F-statistic:  2750 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -254.53  -158.32   -82.53   103.79   209.86  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.977e+00  2.955e-04   30383   <2e-16 ***
## x.var       7.300e-03  4.592e-07   15897   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 442129628  on 763  degrees of freedom
## Residual deviance:  15686493  on 762  degrees of freedom
## AIC: 15696052
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GRENADA  --  13565 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3517.3 -1779.5     0.1  1282.2  7911.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2337.9148   170.6097  -13.70   <2e-16 ***
## x.var          10.4737     0.3864   27.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2356 on 762 degrees of freedom
## Multiple R-squared:  0.4909, Adjusted R-squared:  0.4902 
## F-statistic: 734.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7108 -0.6314  0.1411  0.6643  1.3127 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.6659527  0.0597234   11.15   <2e-16 ***
## x.var       0.0109056  0.0001353   80.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8246 on 762 degrees of freedom
## Multiple R-squared:  0.8951, Adjusted R-squared:  0.8949 
## F-statistic:  6500 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -41.323   -7.296    0.011    4.962   48.608  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.543e-01  8.016e-03  -31.72   <2e-16 ***
## x.var        1.305e-02  1.158e-05 1126.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3530221  on 763  degrees of freedom
## Residual deviance:  217028  on 762  degrees of freedom
## AIC: 222008
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUATEMALA  --  766475 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -104133  -70799   -4044   68950  143281 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -139345.37    5273.85  -26.42   <2e-16 ***
## x.var           998.09      11.94   83.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 72810 on 762 degrees of freedom
## Multiple R-squared:  0.9016, Adjusted R-squared:  0.9015 
## F-statistic:  6982 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2188 -1.0827  0.2742  1.7598  2.7422 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.5213949  0.1618951   34.10   <2e-16 ***
## x.var       0.0134121  0.0003667   36.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.235 on 762 degrees of freedom
## Multiple R-squared:  0.6371, Adjusted R-squared:  0.6367 
## F-statistic:  1338 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -247.85  -159.10    28.84    66.43   150.59  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.913e+00  2.736e-04   36230   <2e-16 ***
## x.var       5.043e-03  4.523e-07   11149   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 180349470  on 763  degrees of freedom
## Residual deviance:  12266791  on 762  degrees of freedom
## AIC: 12276239
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUINEA  --  36393 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2334.16  -679.74    59.31   728.41  2686.29 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2737.5407    79.2346  -34.55   <2e-16 ***
## x.var          51.2499     0.1795  285.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1094 on 762 degrees of freedom
## Multiple R-squared:  0.9907, Adjusted R-squared:  0.9907 
## F-statistic: 8.156e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6173 -0.8255  0.5113  1.5101  1.9859 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.1459090  0.1419452   36.25   <2e-16 ***
## x.var       0.0092435  0.0003215   28.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.96 on 762 degrees of freedom
## Multiple R-squared:  0.5204, Adjusted R-squared:  0.5197 
## F-statistic: 826.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -94.32  -30.07   14.33   22.91   37.79  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.188e+00  8.267e-04    9905   <2e-16 ***
## x.var       3.357e-03  1.468e-06    2287   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7260111  on 763  degrees of freedom
## Residual deviance: 1185040  on 762  degrees of freedom
## AIC: 1192981
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUINEA-BISSAU  --  7972 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -899.31 -349.96   44.98  389.59  914.50 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -417.51385   32.78727  -12.73   <2e-16 ***
## x.var          9.78406    0.07426  131.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 452.7 on 762 degrees of freedom
## Multiple R-squared:  0.958,  Adjusted R-squared:  0.9579 
## F-statistic: 1.736e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6062 -0.7646  0.2288  1.2771  2.0493 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0941080  0.1227009   33.37   <2e-16 ***
## x.var       0.0081285  0.0002779   29.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.694 on 762 degrees of freedom
## Multiple R-squared:  0.5289, Adjusted R-squared:  0.5283 
## F-statistic: 855.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -43.158   -6.280    2.838    9.162   16.044  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.632e+00  1.831e-03    3623   <2e-16 ***
## x.var       3.229e-03  3.272e-06     987   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1340999  on 763  degrees of freedom
## Residual deviance:  221524  on 762  degrees of freedom
## AIC: 228317
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## GUYANA  --  62779 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -8149  -4924  -2270   2741  21961 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11572.524    500.848  -23.11   <2e-16 ***
## x.var           68.840      1.134   60.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6915 on 762 degrees of freedom
## Multiple R-squared:  0.8286, Adjusted R-squared:  0.8283 
## F-statistic:  3683 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7532 -0.6881  0.2455  0.9282  1.7493 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.1336448  0.0958738   32.69   <2e-16 ***
## x.var       0.0123920  0.0002171   57.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.324 on 762 degrees of freedom
## Multiple R-squared:  0.8104, Adjusted R-squared:  0.8101 
## F-statistic:  3257 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -50.330  -39.137    5.787   15.168   31.136  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.441e+00  1.289e-03    4996   <2e-16 ***
## x.var       6.172e-03  2.059e-06    2998   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 14170752  on 763  degrees of freedom
## Residual deviance:   585109  on 762  degrees of freedom
## AIC: 592436
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HAITI  --  30299 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3254.0 -1114.5   218.2   940.7  2739.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2314.5112   105.7812  -21.88   <2e-16 ***
## x.var          39.3246     0.2396  164.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1460 on 762 degrees of freedom
## Multiple R-squared:  0.9725, Adjusted R-squared:  0.9725 
## F-statistic: 2.694e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0829 -1.0660  0.1706  1.4690  2.5887 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.5103959  0.1404760   32.11   <2e-16 ***
## x.var       0.0098705  0.0003182   31.02   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.94 on 762 degrees of freedom
## Multiple R-squared:  0.5581, Adjusted R-squared:  0.5575 
## F-statistic: 962.5 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -81.641  -21.634    7.533   19.436   32.767  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.870e+00  9.605e-04    8194   <2e-16 ***
## x.var       3.427e-03  1.699e-06    2017   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5748529  on 763  degrees of freedom
## Residual deviance: 1000221  on 762  degrees of freedom
## AIC: 1007850
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HOLY SEE  --  29 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.6045 -3.6829 -0.8263  3.5735 10.3189 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.032018   0.355429   19.79   <2e-16 ***
## x.var       0.035737   0.000805   44.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.907 on 762 degrees of freedom
## Multiple R-squared:  0.7212, Adjusted R-squared:  0.7208 
## F-statistic:  1971 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8651 -0.2732  0.1538  0.4004  0.8064 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.736e+00  4.368e-02   39.76   <2e-16 ***
## x.var       2.924e-03  9.893e-05   29.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6031 on 762 degrees of freedom
## Multiple R-squared:  0.5341, Adjusted R-squared:  0.5334 
## F-statistic: 873.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.5847  -0.8970  -0.2034   1.0339   2.5813  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.274e+00  1.930e-02  117.80   <2e-16 ***
## x.var       1.779e-03  3.771e-05   47.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4483.8  on 763  degrees of freedom
## Residual deviance: 2156.8  on 762  degrees of freedom
## AIC: 5625.5
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HONDURAS  --  409708 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31117 -16879  -9279  16256  65701 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -66332.843   1705.291   -38.9   <2e-16 ***
## x.var          631.747      3.862   163.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23540 on 762 degrees of freedom
## Multiple R-squared:  0.9723, Adjusted R-squared:  0.9723 
## F-statistic: 2.676e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4276 -1.0118  0.4615  1.6581  2.4691 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.8237442  0.1601737   36.36   <2e-16 ***
## x.var       0.0123226  0.0003628   33.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.211 on 762 degrees of freedom
## Multiple R-squared:  0.6023, Adjusted R-squared:  0.6017 
## F-statistic:  1154 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -255.63  -169.43    38.22    99.58   112.64  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.008e+01  2.861e-04   35247   <2e-16 ***
## x.var       4.175e-03  4.890e-07    8537   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 105372975  on 763  degrees of freedom
## Residual deviance:  14579362  on 762  degrees of freedom
## AIC: 14588728
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## HUNGARY  --  1769164 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -216253 -115127  -29079   98654  501617 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -293705.53   10965.01  -26.79   <2e-16 ***
## x.var          2043.52      24.83   82.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 151400 on 762 degrees of freedom
## Multiple R-squared:  0.8988, Adjusted R-squared:  0.8987 
## F-statistic:  6771 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9827 -1.0841  0.4902  1.3887  2.3648 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.3642986  0.1402326   38.25   <2e-16 ***
## x.var       0.0147244  0.0003176   46.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.936 on 762 degrees of freedom
## Multiple R-squared:  0.7383, Adjusted R-squared:  0.7379 
## F-statistic:  2149 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -436.4  -319.9  -152.1   191.0   507.2  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.054e+01  1.963e-04   53659   <2e-16 ***
## x.var       5.175e-03  3.231e-07   16019   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 417484818  on 763  degrees of freedom
## Residual deviance:  66034571  on 762  degrees of freedom
## AIC: 66044301
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ICELAND  --  115241 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -11432  -8141  -1816   3442  86399 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8458.407    964.213  -8.772   <2e-16 ***
## x.var          48.823      2.184  22.357   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13310 on 762 degrees of freedom
## Multiple R-squared:  0.3961, Adjusted R-squared:  0.3953 
## F-statistic: 499.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4692 -0.5020  0.2504  0.8904  1.6222 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.1823115  0.1049400   49.38   <2e-16 ***
## x.var       0.0077532  0.0002377   32.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.449 on 762 degrees of freedom
## Multiple R-squared:  0.5827, Adjusted R-squared:  0.5822 
## F-statistic:  1064 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -75.82  -41.11   11.45   30.02  246.24  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.905e+00  1.606e-03    3677   <2e-16 ***
## x.var       6.446e-03  2.546e-06    2532   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 11838331  on 763  degrees of freedom
## Residual deviance:  1885389  on 762  degrees of freedom
## AIC: 1892952
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## INDIA  --  42881179 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -7388089 -2500167  -449729  3171563  7767190 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7830249.0   272197.1  -28.77   <2e-16 ***
## x.var          63059.2      616.5  102.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3758000 on 762 degrees of freedom
## Multiple R-squared:  0.9321, Adjusted R-squared:  0.932 
## F-statistic: 1.046e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.9027 -1.3241  0.8209  2.0331  3.0397 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.7809665  0.1972657   44.51   <2e-16 ***
## x.var       0.0152153  0.0004468   34.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.724 on 762 degrees of freedom
## Multiple R-squared:  0.6035, Adjusted R-squared:  0.603 
## F-statistic:  1160 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2272.7  -1839.2   -112.1    688.9   2458.1  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.437e+01  3.148e-05  456582   <2e-16 ***
## x.var       4.609e-03  5.286e-08   87182   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1.1533e+10  on 763  degrees of freedom
## Residual deviance: 1.6748e+09  on 762  degrees of freedom
## AIC: 1674793698
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## INDONESIA  --  5350902 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -711761 -554016  -13383  512568 1106051 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.113e+06  4.104e+04  -27.13   <2e-16 ***
## x.var        7.330e+03  9.295e+01   78.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 566600 on 762 degrees of freedom
## Multiple R-squared:  0.8909, Adjusted R-squared:  0.8907 
## F-statistic:  6219 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.5314 -0.8279  0.6378  1.6327  2.0006 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9611878  0.1639983   42.45   <2e-16 ***
## x.var       0.0142546  0.0003714   38.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.264 on 762 degrees of freedom
## Multiple R-squared:  0.659,  Adjusted R-squared:  0.6586 
## F-statistic:  1473 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -834.7  -495.8  -133.0   244.1   795.5  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.161e+01  1.097e-04  105847   <2e-16 ***
## x.var       5.464e-03  1.788e-07   30568   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1460316533  on 763  degrees of freedom
## Residual deviance:  143680793  on 762  degrees of freedom
## AIC: 143691610
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## IRAN  --  6998975 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1002951  -697803   -73862   703916  1449488 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1459482      52110  -28.01   <2e-16 ***
## x.var           9994        118   84.68   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 719500 on 762 degrees of freedom
## Multiple R-squared:  0.9039, Adjusted R-squared:  0.9038 
## F-statistic:  7171 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.2835 -0.4126  0.5723  1.2550  1.5606 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.9693926  0.1552273   57.78   <2e-16 ***
## x.var       0.0112173  0.0003516   31.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.143 on 762 degrees of freedom
## Multiple R-squared:  0.5719, Adjusted R-squared:  0.5714 
## F-statistic:  1018 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -892.90  -362.17   -64.56   257.55   586.91  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.207e+01  9.016e-05  133844   <2e-16 ***
## x.var       5.253e-03  1.480e-07   35503   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1853968182  on 763  degrees of freedom
## Residual deviance:  114326374  on 762  degrees of freedom
## AIC: 114337861
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## IRAQ  --  2296665 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -293865 -116164  -59657  147161  432590 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -436110.3    13023.5  -33.49   <2e-16 ***
## x.var          3520.3       29.5  119.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 179800 on 762 degrees of freedom
## Multiple R-squared:  0.9492, Adjusted R-squared:  0.9492 
## F-statistic: 1.424e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3136 -1.0075  0.2949  1.7377  2.5521 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.871943   0.161155   42.64   <2e-16 ***
## x.var       0.013385   0.000365   36.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.225 on 762 degrees of freedom
## Multiple R-squared:  0.6383, Adjusted R-squared:  0.6378 
## F-statistic:  1345 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -579.86  -425.44    77.71   202.48   343.67  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.149e+01  1.330e-04   86392   <2e-16 ***
## x.var       4.601e-03  2.234e-07   20592   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 624999739  on 763  degrees of freedom
## Residual deviance:  75418503  on 762  degrees of freedom
## AIC: 75429010
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## IRELAND  --  1284179 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -150866 -108235  -55938   64589  596955 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -188376.89   11706.59  -16.09   <2e-16 ***
## x.var          1146.07      26.51   43.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 161600 on 762 degrees of freedom
## Multiple R-squared:  0.7103, Adjusted R-squared:  0.7099 
## F-statistic:  1868 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1005 -0.8147  0.6665  1.1637  2.1499 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.6755337  0.1443409   46.25   <2e-16 ***
## x.var       0.0111830  0.0003269   34.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.993 on 762 degrees of freedom
## Multiple R-squared:  0.6056, Adjusted R-squared:  0.6051 
## F-statistic:  1170 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -182.75   -94.39   -31.27    65.10   226.88  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.381e+00  3.058e-04   30676   <2e-16 ***
## x.var       5.990e-03  4.907e-07   12205   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 232302077  on 763  degrees of freedom
## Residual deviance:  11212940  on 762  degrees of freedom
## AIC: 11222644
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## ISRAEL  --  3589326 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -321746 -164544  -88818   20643 1804376 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -393572.7    24901.0  -15.80   <2e-16 ***
## x.var          2851.5       56.4   50.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 343800 on 762 degrees of freedom
## Multiple R-squared:  0.7704, Adjusted R-squared:  0.7701 
## F-statistic:  2556 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.6272 -0.8681  0.8391  1.5152  2.1387 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.2637973  0.1551170   46.83   <2e-16 ***
## x.var       0.0121133  0.0003513   34.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.142 on 762 degrees of freedom
## Multiple R-squared:  0.6094, Adjusted R-squared:  0.6089 
## F-statistic:  1189 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -513.87  -333.12   -61.78   142.78   515.23  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.100e+01  1.603e-04   68612   <2e-16 ***
## x.var       4.994e-03  2.654e-07   18815   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 538490407  on 763  degrees of freedom
## Residual deviance:  62102890  on 762  degrees of freedom
## AIC: 62113328
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ITALY  --  12603758 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1238425  -725559  -239860   210633  5368090 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1526320.1    86485.8  -17.65   <2e-16 ***
## x.var          11468.6      195.9   58.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1194000 on 762 degrees of freedom
## Multiple R-squared:  0.8181, Adjusted R-squared:  0.8179 
## F-statistic:  3428 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.5424 -0.7156  0.6415  1.2200  1.7252 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.4465465  0.1509268   62.59   <2e-16 ***
## x.var       0.0106529  0.0003418   31.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.084 on 762 degrees of freedom
## Multiple R-squared:  0.5604, Adjusted R-squared:  0.5598 
## F-statistic: 971.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -944.6  -635.5  -352.5   517.7   872.7  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.249e+01  7.759e-05  161030   <2e-16 ***
## x.var       4.848e-03  1.291e-07   37540   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2141088036  on 763  degrees of freedom
## Residual deviance:  271073880  on 762  degrees of freedom
## AIC: 271085597
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## JAMAICA  --  127741 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -19606  -9822  -4003   9001  30384 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -26109.381    944.465  -27.64   <2e-16 ***
## x.var          163.031      2.139   76.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13040 on 762 degrees of freedom
## Multiple R-squared:  0.884,  Adjusted R-squared:  0.8839 
## F-statistic:  5809 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5073 -0.7408  0.5011  1.0455  1.7143 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.8851362  0.1078199   36.03   <2e-16 ***
## x.var       0.0126970  0.0002442   51.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.489 on 762 degrees of freedom
## Multiple R-squared:  0.7801, Adjusted R-squared:  0.7798 
## F-statistic:  2703 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -88.57  -67.69  -12.10   30.23   96.49  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.565e+00  7.831e-04    9661   <2e-16 ***
## x.var       5.799e-03  1.264e-06    4589   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 33111455  on 763  degrees of freedom
## Residual deviance:  2433764  on 762  degrees of freedom
## AIC: 2441758
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## JAPAN  --  4692406 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -409310 -330217  -63603  185044 2703285 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -591494.3    32100.8  -18.43   <2e-16 ***
## x.var          3377.8       72.7   46.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 443200 on 762 degrees of freedom
## Multiple R-squared:  0.7391, Adjusted R-squared:  0.7387 
## F-statistic:  2158 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4199 -0.4802  0.4994  0.8623  1.1143 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.4727567  0.0926803   80.63   <2e-16 ***
## x.var       0.0114508  0.0002099   54.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.28 on 762 degrees of freedom
## Multiple R-squared:  0.7961, Adjusted R-squared:  0.7959 
## F-statistic:  2976 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -574.6  -201.2  -103.4   130.3   594.8  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.007e+01  1.967e-04   51190   <2e-16 ***
## x.var       6.553e-03  3.109e-07   21075   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 732563523  on 763  degrees of freedom
## Residual deviance:  35564063  on 762  degrees of freedom
## AIC: 35574524
## 
## Number of Fisher Scoring iterations: 4
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.7163  -0.4313  -0.0013   0.3371   0.8172  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.560e+00  3.716e-02   230.3   <2e-16 ***
## x.var       9.575e-03  8.417e-05   113.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2632921)
## 
##     Null deviance: 2461.78  on 763  degrees of freedom
## Residual deviance:  571.51  on 762  degrees of freedom
## AIC: 20150
## 
## Number of Fisher Scoring iterations: 11
## 
## --------------------------------------------------------------------------------

## JORDAN  --  1599422 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -202304  -83838  -11614   78147  456969 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -257053.30    8750.92  -29.37   <2e-16 ***
## x.var          1831.81      19.82   92.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 120800 on 762 degrees of freedom
## Multiple R-squared:  0.9181, Adjusted R-squared:  0.918 
## F-statistic:  8542 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0196 -0.9883  0.1215  1.6400  2.8394 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3522516  0.1390049   31.31   <2e-16 ***
## x.var       0.0162762  0.0003148   51.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.919 on 762 degrees of freedom
## Multiple R-squared:  0.7782, Adjusted R-squared:  0.7779 
## F-statistic:  2673 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -459.8  -324.9  -153.9   170.7   480.9  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.050e+01  2.029e-04   51777   <2e-16 ***
## x.var       5.065e-03  3.351e-07   15116   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 377996750  on 763  degrees of freedom
## Residual deviance:  68393439  on 762  degrees of freedom
## AIC: 68402846
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KAZAKHSTAN  --  1388713 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -208596 -152222   13035  122154  312270 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -259164.04   10505.42  -24.67   <2e-16 ***
## x.var          1758.68      23.79   73.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 145000 on 762 degrees of freedom
## Multiple R-squared:  0.8776, Adjusted R-squared:  0.8774 
## F-statistic:  5463 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8983 -0.8580  0.4344  1.5094  2.7660 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  6.23175    0.16338   38.14   <2e-16 ***
## x.var        0.01307    0.00037   35.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.256 on 762 degrees of freedom
## Multiple R-squared:  0.6208, Adjusted R-squared:  0.6203 
## F-statistic:  1247 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -287.21  -187.34    27.09    56.01   232.71  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.030e+01  2.169e-04   47479   <2e-16 ***
## x.var       5.299e-03  3.554e-07   14912   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 326297173  on 763  degrees of freedom
## Residual deviance:  18014080  on 762  degrees of freedom
## AIC: 18023974
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KENYA  --  322781 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -28188 -13681  -5922  13693  54731 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -55198.112   1403.628  -39.33   <2e-16 ***
## x.var          467.274      3.179  146.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19380 on 762 degrees of freedom
## Multiple R-squared:  0.9659, Adjusted R-squared:  0.9659 
## F-statistic: 2.161e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8443 -1.0593  0.3851  1.6979  2.4629 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.1915296  0.1510266   34.38   <2e-16 ***
## x.var       0.0127996  0.0003421   37.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.085 on 762 degrees of freedom
## Multiple R-squared:  0.6476, Adjusted R-squared:  0.6471 
## F-statistic:  1400 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -202.81  -134.45   -18.55    82.05   126.64  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.574e+00  3.543e-04   27022   <2e-16 ***
## x.var       4.461e-03  5.985e-07    7454   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 81305793  on 763  degrees of freedom
## Residual deviance: 10234718  on 762  degrees of freedom
## AIC: 10243737
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KIRIBATI  --  2867 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -244.20 -149.31  -53.84   43.64 2602.21 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -129.74672   26.95231  -4.814 1.78e-06 ***
## x.var          0.51641    0.06104   8.460  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 372.1 on 762 degrees of freedom
## Multiple R-squared:  0.08586,    Adjusted R-squared:  0.08466 
## F-statistic: 71.57 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0491 -0.6096 -0.2407  0.2317  5.8069 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.8431665  0.0830968  -10.15   <2e-16 ***
## x.var        0.0039260  0.0001882   20.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.147 on 762 degrees of freedom
## Multiple R-squared:  0.3635, Adjusted R-squared:  0.3627 
## F-statistic: 435.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -23.998    0.000    0.000    1.826   22.876  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -5.867e+01  2.906e-01  -201.9   <2e-16 ***
## x.var        8.776e-02  3.858e-04   227.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 349000  on 763  degrees of freedom
## Residual deviance:  17064  on 762  degrees of freedom
## AIC: 18009
## 
## Number of Fisher Scoring iterations: 8
## 
## --------------------------------------------------------------------------------

## KOREA, SOUTH  --  2499188 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -169057 -121155  -61324   75896 1941885 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -186055.43   15361.58  -12.11   <2e-16 ***
## x.var           972.98      34.79   27.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 212100 on 762 degrees of freedom
## Multiple R-squared:  0.5065, Adjusted R-squared:  0.5059 
## F-statistic: 782.1 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7789 -0.2212  0.2200  0.4805  1.1171 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.4543606  0.0805702   92.52   <2e-16 ***
## x.var       0.0088445  0.0001825   48.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.112 on 762 degrees of freedom
## Multiple R-squared:  0.7551, Adjusted R-squared:  0.7548 
## F-statistic:  2349 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -161.48   -52.61    12.32    45.68  1148.46  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.021e+00  4.409e-04   18194   <2e-16 ***
## x.var       7.702e-03  6.797e-07   11332   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 235216711  on 763  degrees of freedom
## Residual deviance:  10131516  on 762  degrees of freedom
## AIC: 10141200
## 
## Number of Fisher Scoring iterations: 4
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8081  -0.1308  -0.0355   0.1513   1.0163  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.236e+00  2.234e-02   368.6   <2e-16 ***
## x.var       7.364e-03  5.061e-05   145.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.09516853)
## 
##     Null deviance: 1990.07  on 763  degrees of freedom
## Residual deviance:  337.29  on 762  degrees of freedom
## AIC: 18156
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## KOSOVO  --  225664 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26073 -11761  -3775   9574  41267 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -37635.136   1144.122  -32.89   <2e-16 ***
## x.var          291.357      2.591  112.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15800 on 762 degrees of freedom
## Multiple R-squared:  0.9432, Adjusted R-squared:  0.9431 
## F-statistic: 1.264e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5482 -0.8898  0.5110  1.4712  1.8776 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9078976  0.1341302   36.59   <2e-16 ***
## x.var       0.0123127  0.0003038   40.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.852 on 762 degrees of freedom
## Multiple R-squared:  0.6831, Adjusted R-squared:  0.6827 
## F-statistic:  1643 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -141.32  -116.86   -38.64    68.55   140.85  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.900e+00  4.760e-04   18698   <2e-16 ***
## x.var       4.739e-03  7.954e-07    5959   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 53361718  on 763  degrees of freedom
## Residual deviance:  6738121  on 762  degrees of freedom
## AIC: 6746781
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KUWAIT  --  616409 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -49647 -21283  -4765  20124 109051 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -71212.655   2414.900  -29.49   <2e-16 ***
## x.var          757.292      5.469  138.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33340 on 762 degrees of freedom
## Multiple R-squared:  0.9618, Adjusted R-squared:  0.9617 
## F-statistic: 1.917e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3139 -0.9678  0.4746  1.5825  2.1174 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9629834  0.1502228   46.35   <2e-16 ***
## x.var       0.0106330  0.0003402   31.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.074 on 762 degrees of freedom
## Multiple R-squared:  0.5617, Adjusted R-squared:  0.5612 
## F-statistic: 976.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -291.98  -126.27    32.86    77.91   169.52  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.042e+01  2.492e-04   41821   <2e-16 ***
## x.var       3.965e-03  4.298e-07    9227   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 119712401  on 763  degrees of freedom
## Residual deviance:  15624543  on 762  degrees of freedom
## AIC: 15634317
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## KYRGYZSTAN  --  200388 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19886.8  -8087.9   -596.6   6374.6  26413.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -26718.608    892.910  -29.92   <2e-16 ***
## x.var          304.940      2.022  150.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12330 on 762 degrees of freedom
## Multiple R-squared:  0.9676, Adjusted R-squared:  0.9675 
## F-statistic: 2.274e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0895 -0.9731  0.2469  1.6507  2.7980 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.4284277  0.1561668   34.76   <2e-16 ***
## x.var       0.0118042  0.0003537   33.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.156 on 762 degrees of freedom
## Multiple R-squared:  0.5938, Adjusted R-squared:  0.5932 
## F-statistic:  1114 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -193.60  -123.08    25.44    63.48   105.82  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.595e+00  3.826e-04   25080   <2e-16 ***
## x.var       3.853e-03  6.631e-07    5811   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 49657480  on 763  degrees of freedom
## Residual deviance:  8782713  on 762  degrees of freedom
## AIC: 8791614
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LAOS  --  141694 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33524 -22810  -4932  15088  78938 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -26908.432   2002.453  -13.44   <2e-16 ***
## x.var          117.362      4.535   25.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27650 on 762 degrees of freedom
## Multiple R-squared:  0.4677, Adjusted R-squared:  0.467 
## F-statistic: 669.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6859 -0.6776  0.2131  0.7920  2.0926 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.3830105  0.0810118  -4.728  2.7e-06 ***
## x.var        0.0156842  0.0001835  85.481  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.119 on 762 degrees of freedom
## Multiple R-squared:  0.9056, Adjusted R-squared:  0.9054 
## F-statistic:  7307 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -160.942   -25.260    -4.303     2.826    79.498  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.246e-01  2.939e-03   144.5   <2e-16 ***
## x.var       1.549e-02  4.182e-06  3704.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 41285196  on 763  degrees of freedom
## Residual deviance:   860052  on 762  degrees of freedom
## AIC: 865624
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LATVIA  --  612920 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53427 -36207 -19651  21503 335981 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -80181.464   4097.622  -19.57   <2e-16 ***
## x.var          467.436      9.281   50.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56570 on 762 degrees of freedom
## Multiple R-squared:  0.769,  Adjusted R-squared:  0.7687 
## F-statistic:  2537 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7843 -0.7651  0.3061  1.0855  1.7529 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2385021  0.1086616   39.01   <2e-16 ***
## x.var       0.0136440  0.0002461   55.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.5 on 762 degrees of freedom
## Multiple R-squared:  0.8013, Adjusted R-squared:  0.8011 
## F-statistic:  3074 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -159.80  -104.51   -69.07    72.35   182.76  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.231e+00  5.106e-04   16119   <2e-16 ***
## x.var       6.351e-03  8.114e-07    7827   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 103332211  on 763  degrees of freedom
## Residual deviance:   9080802  on 762  degrees of freedom
## AIC: 9089357
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LEBANON  --  1057000 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -121326  -51609    -769   58704  223003 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -174940.86    5876.61  -29.77   <2e-16 ***
## x.var          1320.60      13.31   99.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 81140 on 762 degrees of freedom
## Multiple R-squared:  0.9282, Adjusted R-squared:  0.9281 
## F-statistic:  9845 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7347 -0.9220  0.0296  1.5695  2.0236 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.309581   0.131589   40.35   <2e-16 ***
## x.var       0.014171   0.000298   47.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.817 on 762 degrees of freedom
## Multiple R-squared:  0.7479, Adjusted R-squared:  0.7476 
## F-statistic:  2261 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -353.2  -277.6  -135.2   156.1   395.3  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.035e+01  2.278e-04   45409   <2e-16 ***
## x.var       4.831e-03  3.794e-07   12731   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 262607509  on 763  degrees of freedom
## Residual deviance:  47918218  on 762  degrees of freedom
## AIC: 47927765
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LESOTHO  --  32599 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4872.0 -2958.3  -623.4  1950.3  8431.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6351.0591   256.7595  -24.73   <2e-16 ***
## x.var          40.9035     0.5815   70.34   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3545 on 762 degrees of freedom
## Multiple R-squared:  0.8665, Adjusted R-squared:  0.8664 
## F-statistic:  4948 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.14159 -1.59474  0.00211  1.70853  2.32094 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.5213492  0.1238044   12.29   <2e-16 ***
## x.var       0.0144664  0.0002804   51.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.709 on 762 degrees of freedom
## Multiple R-squared:  0.7774, Adjusted R-squared:  0.7771 
## F-statistic:  2662 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -50.626  -33.560  -15.026    8.867   66.075  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.326e+00  1.505e-03    4203   <2e-16 ***
## x.var       5.595e-03  2.443e-06    2290   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8257578  on 763  degrees of freedom
## Residual deviance:  771898  on 762  degrees of freedom
## AIC: 778477
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIBERIA  --  7360 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1692.5  -480.5   184.0   625.6  1087.6 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1097.5937    55.1110  -19.92   <2e-16 ***
## x.var          10.0182     0.1248   80.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 760.9 on 762 degrees of freedom
## Multiple R-squared:  0.8942, Adjusted R-squared:  0.8941 
## F-statistic:  6442 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0537 -0.5876  0.1976  1.0315  1.8548 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5762780  0.1036431   34.51   <2e-16 ***
## x.var       0.0086812  0.0002347   36.98   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.431 on 762 degrees of freedom
## Multiple R-squared:  0.6422, Adjusted R-squared:  0.6417 
## F-statistic:  1368 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -30.3304  -13.2181   -0.0231    8.2670   27.2589  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.871e+00  2.320e-03    2530   <2e-16 ***
## x.var       4.269e-03  3.950e-06    1081   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1638985  on 763  degrees of freedom
## Residual deviance:  171182  on 762  degrees of freedom
## AIC: 177754
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIBYA  --  489940 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53389 -31573 -18261  30737  91779 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -90957.63    2763.79  -32.91   <2e-16 ***
## x.var          640.21       6.26  102.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 38160 on 762 degrees of freedom
## Multiple R-squared:  0.9321, Adjusted R-squared:  0.932 
## F-statistic: 1.046e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7507 -1.3923  0.1198  1.8188  2.8132 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.7818585  0.1495524   25.29   <2e-16 ***
## x.var       0.0156259  0.0003387   46.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.065 on 762 degrees of freedom
## Multiple R-squared:  0.7364, Adjusted R-squared:  0.736 
## F-statistic:  2128 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -220.50  -167.35    11.74    97.51   130.65  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.413e+00  3.470e-04   27124   <2e-16 ***
## x.var       5.121e-03  5.721e-07    8951   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 122639059  on 763  degrees of freedom
## Residual deviance:  13466385  on 762  degrees of freedom
## AIC: 13475131
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LIECHTENSTEIN  --  11467 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1279.4  -737.9  -148.5   315.9  5418.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1368.7743    78.7457  -17.38   <2e-16 ***
## x.var           9.7080     0.1783   54.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1087 on 762 degrees of freedom
## Multiple R-squared:  0.7954, Adjusted R-squared:  0.7952 
## F-statistic:  2963 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1953 -0.6476  0.0396  0.7903  1.5988 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.7911431  0.0784651   35.57   <2e-16 ***
## x.var       0.0096233  0.0001777   54.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.083 on 762 degrees of freedom
## Multiple R-squared:  0.7937, Adjusted R-squared:  0.7935 
## F-statistic:  2932 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -26.91  -20.13  -10.85   14.08   32.07  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.249e+00  2.799e-03    1876   <2e-16 ***
## x.var       5.086e-03  4.619e-06    1101   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1925361  on 763  degrees of freedom
## Residual deviance:  279000  on 762  degrees of freedom
## AIC: 285268
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LITHUANIA  --  879371 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -95696 -52879 -16852  32530 358340 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -132633.82    5539.84  -23.94   <2e-16 ***
## x.var           855.58      12.55   68.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 76490 on 762 degrees of freedom
## Multiple R-squared:  0.8592, Adjusted R-squared:  0.859 
## F-statistic:  4650 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1962 -0.8691  0.3108  1.1237  2.3041 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6548866  0.1257457   37.02   <2e-16 ***
## x.var       0.0142441  0.0002848   50.02   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.736 on 762 degrees of freedom
## Multiple R-squared:  0.7665, Adjusted R-squared:  0.7662 
## F-statistic:  2502 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -256.49  -178.43   -66.95   118.48   247.40  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.374e+00  3.285e-04   28536   <2e-16 ***
## x.var       5.586e-03  5.334e-07   10471   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 178881181  on 763  degrees of freedom
## Residual deviance:  22519317  on 762  degrees of freedom
## AIC: 22528367
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## LUXEMBOURG  --  180376 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -17920  -9897   -909   3695  62850 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -20327.351   1030.142  -19.73   <2e-16 ***
## x.var          180.437      2.333   77.34   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14220 on 762 degrees of freedom
## Multiple R-squared:  0.887,  Adjusted R-squared:  0.8868 
## F-statistic:  5981 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1766 -0.7635  0.7432  1.1198  1.5960 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.8000221  0.1307524   44.36   <2e-16 ***
## x.var       0.0099088  0.0002961   33.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 762 degrees of freedom
## Multiple R-squared:  0.595,  Adjusted R-squared:  0.5945 
## F-statistic:  1120 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -122.46   -77.95   -41.34    65.86   106.86  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.713e+00  5.549e-04   15702   <2e-16 ***
## x.var       4.336e-03  9.420e-07    4603   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 31322481  on 763  degrees of freedom
## Residual deviance:  4531381  on 762  degrees of freedom
## AIC: 4540041
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MS ZAANDAM  --  9 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3454 -0.7021  0.4071  1.5163  2.6255 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.9616113  0.1612947   36.96   <2e-16 ***
## x.var       0.0058150  0.0003653   15.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.227 on 762 degrees of freedom
## Multiple R-squared:  0.2495, Adjusted R-squared:  0.2486 
## F-statistic: 253.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6328 -0.1775  0.1025  0.3825  0.6625 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.536e+00  4.094e-02   37.52   <2e-16 ***
## x.var       1.468e-03  9.273e-05   15.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5653 on 762 degrees of freedom
## Multiple R-squared:  0.2475, Adjusted R-squared:  0.2465 
## F-statistic: 250.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.5917  -0.2401   0.1700   0.5620   0.9372  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.817e+00  2.721e-02   66.78   <2e-16 ***
## x.var       7.139e-04  5.776e-05   12.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1161.9  on 763  degrees of freedom
## Residual deviance: 1008.0  on 762  degrees of freedom
## AIC: 3835.7
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MADAGASCAR  --  63433 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7599.4 -3618.3   -25.9  3119.7  7290.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6575.3982   291.7519  -22.54   <2e-16 ***
## x.var          82.5236     0.6608  124.89   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4028 on 762 degrees of freedom
## Multiple R-squared:  0.9534, Adjusted R-squared:  0.9534 
## F-statistic: 1.56e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2682 -1.0144  0.4594  1.3160  2.6561 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6375152  0.1403160   33.05   <2e-16 ***
## x.var       0.0108746  0.0003178   34.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.937 on 762 degrees of freedom
## Multiple R-squared:  0.6058, Adjusted R-squared:  0.6053 
## F-statistic:  1171 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -107.653   -49.070     6.336    38.024    79.382  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.385e+00  7.132e-04   11756   <2e-16 ***
## x.var       3.724e-03  1.244e-06    2994   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13324854  on 763  degrees of freedom
## Residual deviance:  2591077  on 762  degrees of freedom
## AIC: 2599098
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALAWI  --  85257 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17339.0  -5668.4   -412.3   6231.1  17148.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17267.446    602.631  -28.65   <2e-16 ***
## x.var          119.030      1.365   87.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8320 on 762 degrees of freedom
## Multiple R-squared:  0.9089, Adjusted R-squared:  0.9088 
## F-statistic:  7605 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3146 -1.1629  0.3079  1.4464  2.4165 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.3729718  0.1288374   26.18   <2e-16 ***
## x.var       0.0132617  0.0002918   45.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.779 on 762 degrees of freedom
## Multiple R-squared:  0.7305, Adjusted R-squared:  0.7302 
## F-statistic:  2066 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -90.68  -60.76  -28.63   38.47   97.86  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.660e+00  8.208e-04    9333   <2e-16 ***
## x.var       5.220e-03  1.349e-06    3871   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 23191704  on 763  degrees of freedom
## Residual deviance:  2577148  on 762  degrees of freedom
## AIC: 2584875
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALAYSIA  --  3305157 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -721660 -532014  -16290  552782  946947 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -793535.54   37025.88  -21.43   <2e-16 ***
## x.var          4125.32      83.86   49.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 511200 on 762 degrees of freedom
## Multiple R-squared:  0.7605, Adjusted R-squared:  0.7602 
## F-statistic:  2420 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1485 -0.2113  0.2532  0.7555  1.2675 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.1074254  0.0854172   71.50   <2e-16 ***
## x.var       0.0137006  0.0001935   70.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.179 on 762 degrees of freedom
## Multiple R-squared:  0.8681, Adjusted R-squared:  0.8679 
## F-statistic:  5015 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -667.08  -217.88  -151.11    25.81   578.67  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.395e+00  2.174e-04   43218   <2e-16 ***
## x.var       7.803e-03  3.345e-07   23326   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1017731022  on 763  degrees of freedom
## Residual deviance:   54953524  on 762  degrees of freedom
## AIC: 54963594
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALDIVES  --  167496 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25737 -11534    825   7976  60829 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -27496.961   1194.092  -23.03   <2e-16 ***
## x.var          175.607      2.704   64.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16490 on 762 degrees of freedom
## Multiple R-squared:  0.8469, Adjusted R-squared:  0.8467 
## F-statistic:  4216 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8913 -0.9773  0.4994  1.3417  1.9518 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3345080  0.1210711   35.80   <2e-16 ***
## x.var       0.0121052  0.0002742   44.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.672 on 762 degrees of freedom
## Multiple R-squared:  0.7189, Adjusted R-squared:  0.7185 
## F-statistic:  1949 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -104.843   -50.808   -18.968     5.878   131.910  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.746e+00  7.337e-04   10557   <2e-16 ***
## x.var       5.648e-03  1.189e-06    4750   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 35063469  on 763  degrees of freedom
## Residual deviance:  2687636  on 762  degrees of freedom
## AIC: 2695806
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALI  --  30355 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2653.0 -1740.9  -764.4   922.7  8195.1 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3865.0644   180.7254  -21.39   <2e-16 ***
## x.var          34.7611     0.4093   84.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2495 on 762 degrees of freedom
## Multiple R-squared:  0.9044, Adjusted R-squared:  0.9043 
## F-statistic:  7212 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8830 -0.9175  0.6497  1.2085  1.8687 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.269860   0.123176   34.66   <2e-16 ***
## x.var       0.009732   0.000279   34.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 762 degrees of freedom
## Multiple R-squared:  0.6149, Adjusted R-squared:  0.6144 
## F-statistic:  1217 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -56.090  -24.910   -5.669   18.390   48.990  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.090e+00  1.255e-03    5647   <2e-16 ***
## x.var       4.304e-03  2.134e-06    2017   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5721619  on 763  degrees of freedom
## Residual deviance:  592935  on 762  degrees of freedom
## AIC: 600333
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MALTA  --  70908 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -7144  -4973  -1606   3136  18114 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11247.39     441.67  -25.47   <2e-16 ***
## x.var           83.87       1.00   83.84   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6098 on 762 degrees of freedom
## Multiple R-squared:  0.9022, Adjusted R-squared:  0.9021 
## F-statistic:  7029 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7822 -0.7332  0.4908  1.0162  1.4475 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2855232  0.1070862   40.02   <2e-16 ***
## x.var       0.0110362  0.0002425   45.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.479 on 762 degrees of freedom
## Multiple R-squared:  0.731,  Adjusted R-squared:  0.7306 
## F-statistic:  2071 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -74.02  -60.64  -18.78   31.09   94.34  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.555e+00  9.128e-04    8277   <2e-16 ***
## x.var       4.878e-03  1.518e-06    3214   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 15913404  on 763  degrees of freedom
## Residual deviance:  2170281  on 762  degrees of freedom
## AIC: 2178104
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MARSHALL ISLANDS  --  7 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.75278 -0.86805 -0.04434  0.80931  2.06168 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.7210412  0.0735148  -9.808   <2e-16 ***
## x.var        0.0088351  0.0001665  53.063   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.015 on 762 degrees of freedom
## Multiple R-squared:  0.787,  Adjusted R-squared:  0.7867 
## F-statistic:  2816 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7027 -0.2884 -0.0540  0.2561  0.8397 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.913e-01  2.788e-02  -6.863  1.4e-11 ***
## x.var        3.193e-03  6.314e-05  50.571  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3849 on 762 degrees of freedom
## Multiple R-squared:  0.7704, Adjusted R-squared:  0.7701 
## F-statistic:  2557 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6193  -1.1334  -0.3486   0.6083   1.7692  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.7804985  0.0694303  -11.24   <2e-16 ***
## x.var        0.0037547  0.0001209   31.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1988.09  on 763  degrees of freedom
## Residual deviance:  830.58  on 762  degrees of freedom
## AIC: 2421.4
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MAURITANIA  --  58621 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7484.6 -3371.9   -96.9  1489.9 15082.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8355.1765   358.2060  -23.32   <2e-16 ***
## x.var          69.5558     0.8113   85.73   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4946 on 762 degrees of freedom
## Multiple R-squared:  0.9061, Adjusted R-squared:  0.9059 
## F-statistic:  7351 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4094 -1.3420  0.1476  1.6460  2.7594 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.795466   0.140831   26.95   <2e-16 ***
## x.var       0.011806   0.000319   37.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.944 on 762 degrees of freedom
## Multiple R-squared:  0.6426, Adjusted R-squared:  0.6421 
## F-statistic:  1370 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -82.028  -23.195    2.786   18.570   49.659  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.634e+00  9.277e-04    8229   <2e-16 ***
## x.var       4.508e-03  1.564e-06    2882   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 11728641  on 763  degrees of freedom
## Residual deviance:  1054327  on 762  degrees of freedom
## AIC: 1061979
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MAURITIUS  --  70862 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -20086 -12695  -3095   9701  34578 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -15600.193   1142.292  -13.66   <2e-16 ***
## x.var           70.130      2.587   27.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15770 on 762 degrees of freedom
## Multiple R-squared:  0.4909, Adjusted R-squared:  0.4902 
## F-statistic: 734.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2622 -0.6419  0.1077  0.6720  2.2235 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.6360768  0.0851011   30.98   <2e-16 ***
## x.var       0.0111799  0.0001927   58.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.175 on 762 degrees of freedom
## Multiple R-squared:  0.8153, Adjusted R-squared:  0.8151 
## F-statistic:  3365 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -123.651   -18.541    -1.148    22.162   105.599  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.822e+00  3.027e-03   601.7   <2e-16 ***
## x.var       1.280e-02  4.382e-06  2920.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 22945602  on 763  degrees of freedom
## Residual deviance:   979636  on 762  degrees of freedom
## AIC: 986220
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MEXICO  --  5455237 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -346811 -195340  -61542  108008  979718 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -727664.93   19986.23  -36.41   <2e-16 ***
## x.var          6810.45      45.27  150.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 275900 on 762 degrees of freedom
## Multiple R-squared:  0.9674, Adjusted R-squared:  0.9674 
## F-statistic: 2.264e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3186 -1.1333  0.5963  1.9472  2.6298 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.8364042  0.1879260   41.70   <2e-16 ***
## x.var       0.0130336  0.0004256   30.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.595 on 762 degrees of freedom
## Multiple R-squared:  0.5517, Adjusted R-squared:  0.5511 
## F-statistic: 937.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -814.5  -412.1    29.8   210.0   566.4  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.243e+01  8.788e-05  141497   <2e-16 ***
## x.var       4.212e-03  1.500e-07   28091   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1127265589  on 763  degrees of freedom
## Residual deviance:  140868551  on 762  degrees of freedom
## AIC: 140879684
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MICRONESIA  --  1 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4880 -0.1924 -0.0056  0.1812  0.5101 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.272e-01  1.817e-02  -12.51   <2e-16 ***
## x.var        1.959e-03  4.114e-05   47.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2508 on 762 degrees of freedom
## Multiple R-squared:  0.7485, Adjusted R-squared:  0.7482 
## F-statistic:  2268 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33823 -0.13336 -0.00388  0.12561  0.35356 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.575e-01  1.259e-02  -12.51   <2e-16 ***
## x.var        1.358e-03  2.852e-05   47.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1739 on 762 degrees of freedom
## Multiple R-squared:  0.7485, Adjusted R-squared:  0.7482 
## F-statistic:  2268 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7911  -0.5463  -0.3842   0.3003   0.9713  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.7718504  0.1711520  -16.20   <2e-16 ***
## x.var        0.0044109  0.0002897   15.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 518.39  on 763  degrees of freedom
## Residual deviance: 223.19  on 762  degrees of freedom
## AIC: 1025.2
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MOLDOVA  --  497946 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37991 -23026  -3776  15093  88392 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -67602.969   2011.071  -33.62   <2e-16 ***
## x.var          624.550      4.555  137.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27770 on 762 degrees of freedom
## Multiple R-squared:  0.9611, Adjusted R-squared:  0.961 
## F-statistic: 1.88e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.6733 -0.9486  0.7558  1.6893  1.8213 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.1356060  0.1536230   39.94   <2e-16 ***
## x.var       0.0116889  0.0003479   33.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.121 on 762 degrees of freedom
## Multiple R-squared:  0.597,  Adjusted R-squared:  0.5964 
## F-statistic:  1129 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -239.5  -141.4   -29.7   113.4   218.4  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.002e+01  2.921e-04   34320   <2e-16 ***
## x.var       4.241e-03  4.978e-07    8520   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 106454273  on 763  degrees of freedom
## Residual deviance:  15460971  on 762  degrees of freedom
## AIC: 15470396
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MONACO  --  9305 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -834.1 -601.9 -310.5  250.6 4137.4 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1307.3229    70.2691  -18.61   <2e-16 ***
## x.var           8.4751     0.1591   53.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 970.2 on 762 degrees of freedom
## Multiple R-squared:  0.7882, Adjusted R-squared:  0.7879 
## F-statistic:  2836 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1402 -0.5578  0.2473  0.7096  1.0733 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.7867930  0.0707664   39.38   <2e-16 ***
## x.var       0.0093016  0.0001603   58.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9771 on 762 degrees of freedom
## Multiple R-squared:  0.8155, Adjusted R-squared:  0.8153 
## F-statistic:  3368 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.631  -13.860   -7.756    8.424   23.910  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.781e+00  3.282e-03    1457   <2e-16 ***
## x.var       5.555e-03  5.334e-06    1042   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1686429  on 763  degrees of freedom
## Residual deviance:  144173  on 762  degrees of freedom
## AIC: 150349
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MONGOLIA  --  903150 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -210875 -158871  -17683  136780  377586 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -205415.21   11782.68  -17.43   <2e-16 ***
## x.var           956.78      26.69   35.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 162700 on 762 degrees of freedom
## Multiple R-squared:  0.6278, Adjusted R-squared:  0.6273 
## F-statistic:  1285 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.18313 -0.56449  0.08513  0.65640  1.54802 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.3168733  0.0591950   22.25   <2e-16 ***
## x.var       0.0180470  0.0001341  134.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8173 on 762 degrees of freedom
## Multiple R-squared:  0.9596, Adjusted R-squared:  0.9596 
## F-statistic: 1.812e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -386.40  -119.32   -52.51    -3.66   315.35  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.819e+00  6.695e-04    8693   <2e-16 ***
## x.var       1.083e-02  9.866e-07   10978   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 286865272  on 763  degrees of freedom
## Residual deviance:  13183156  on 762  degrees of freedom
## AIC: 13190749
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MONTENEGRO  --  229430 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23695 -12842  -3558   5674  50191 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -39449.347   1304.714  -30.24   <2e-16 ***
## x.var          286.553      2.955   96.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18010 on 762 degrees of freedom
## Multiple R-squared:  0.925,  Adjusted R-squared:  0.9249 
## F-statistic:  9404 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9486 -0.9083  0.2449  1.4477  2.0595 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2118091  0.1280660   32.89   <2e-16 ***
## x.var       0.0133961  0.0002901   46.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.768 on 762 degrees of freedom
## Multiple R-squared:  0.7368, Adjusted R-squared:  0.7365 
## F-statistic:  2133 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -150.65  -119.08   -37.03    61.64   150.95  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.708e+00  5.045e-04   17260   <2e-16 ***
## x.var       4.983e-03  8.357e-07    5962   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 55069157  on 763  degrees of freedom
## Residual deviance:  7280455  on 762  degrees of freedom
## AIC: 7288893
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MOROCCO  --  1159941 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -151745  -75993    6330   61029  187731 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -189374.33    6086.96  -31.11   <2e-16 ***
## x.var          1643.57      13.79  119.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 84040 on 762 degrees of freedom
## Multiple R-squared:  0.9491, Adjusted R-squared:  0.949 
## F-statistic: 1.421e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8142 -0.9277  0.6519  1.5915  2.3955 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.290571   0.159406   39.46   <2e-16 ***
## x.var       0.013092   0.000361   36.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.201 on 762 degrees of freedom
## Multiple R-squared:  0.6331, Adjusted R-squared:  0.6326 
## F-statistic:  1315 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -377.32  -330.46   -26.47   166.46   360.15  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.088e+01  1.862e-04   58439   <2e-16 ***
## x.var       4.394e-03  3.153e-07   13934   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 295766470  on 763  degrees of freedom
## Residual deviance:  48981960  on 762  degrees of freedom
## AIC: 48991922
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## MOZAMBIQUE  --  224983 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36526 -20551  -3523  17633  52772 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -48332.508   1798.233  -26.88   <2e-16 ***
## x.var          297.781      4.073   73.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24830 on 762 degrees of freedom
## Multiple R-squared:  0.8752, Adjusted R-squared:  0.8751 
## F-statistic:  5346 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.313 -1.139  0.251  1.468  1.952 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.4383180  0.1219920   28.18   <2e-16 ***
## x.var       0.0145744  0.0002763   52.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.684 on 762 degrees of freedom
## Multiple R-squared:  0.785,  Adjusted R-squared:  0.7847 
## F-statistic:  2783 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -139.36   -93.05   -41.12    52.55   130.05  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.100e+00  5.897e-04   13736   <2e-16 ***
## x.var       5.895e-03  9.488e-07    6212   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 61909385  on 763  degrees of freedom
## Residual deviance:  5163274  on 762  degrees of freedom
## AIC: 5171454
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NAMIBIA  --  157060 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26974 -19409   2395  16517  36762 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -37001.214   1338.422  -27.64   <2e-16 ***
## x.var          238.767      3.031   78.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18480 on 762 degrees of freedom
## Multiple R-squared:  0.8906, Adjusted R-squared:  0.8905 
## F-statistic:  6204 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7885 -1.6173  0.4135  1.7232  2.6319 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.0071156  0.1289520   23.32   <2e-16 ***
## x.var       0.0150258  0.0002921   51.45   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.78 on 762 degrees of freedom
## Multiple R-squared:  0.7765, Adjusted R-squared:  0.7762 
## F-statistic:  2647 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -170.00   -90.28   -24.26    35.91   155.01  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.099e+00  6.216e-04   13030   <2e-16 ***
## x.var       5.583e-03  1.009e-06    5531   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 48530083  on 763  degrees of freedom
## Residual deviance:  4907356  on 762  degrees of freedom
## AIC: 4915350
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NEPAL  --  976361 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -182209  -68772     153   68542  193019 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -194478.53    6325.70  -30.74   <2e-16 ***
## x.var          1459.44      14.33  101.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 87340 on 762 degrees of freedom
## Multiple R-squared:  0.9316, Adjusted R-squared:  0.9315 
## F-statistic: 1.038e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0971 -1.6978  0.6139  2.1077  2.8581 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.8562122  0.1645972   29.50   <2e-16 ***
## x.var       0.0153116  0.0003728   41.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.273 on 762 degrees of freedom
## Multiple R-squared:  0.6889, Adjusted R-squared:  0.6884 
## F-statistic:  1687 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -362.20  -270.59   -26.85   159.18   306.62  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.043e+01  2.177e-04   47903   <2e-16 ***
## x.var       4.853e-03  3.623e-07   13394   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 275741989  on 763  degrees of freedom
## Residual deviance:  37577422  on 762  degrees of freedom
## AIC: 37586975
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NETHERLANDS  --  6242767 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -483014 -314104 -201608  157232 2998103 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -799572.77   40168.51  -19.91   <2e-16 ***
## x.var          5300.34      90.98   58.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 554600 on 762 degrees of freedom
## Multiple R-squared:  0.8167, Adjusted R-squared:  0.8164 
## F-statistic:  3394 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.0207 -0.8969  0.8431  1.3864  1.7744 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.5698983  0.1560968   48.49   <2e-16 ***
## x.var       0.0125218  0.0003535   35.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.155 on 762 degrees of freedom
## Multiple R-squared:  0.6221, Adjusted R-squared:  0.6216 
## F-statistic:  1254 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -457.8  -378.3  -213.8   308.5   475.3  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.131e+01  1.280e-04   88359   <2e-16 ***
## x.var       5.425e-03  2.089e-07   25969   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1040050122  on 763  degrees of freedom
## Residual deviance:   93376914  on 762  degrees of freedom
## AIC: 93387697
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NEW ZEALAND  --  45475 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -4330  -2606   -369   1712  35047 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2347.6023   272.4692  -8.616   <2e-16 ***
## x.var          16.7216     0.6171  27.097   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3762 on 762 degrees of freedom
## Multiple R-squared:  0.4907, Adjusted R-squared:  0.4901 
## F-statistic: 734.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0263 -0.4517  0.0401  0.9716  1.8820 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7708661  0.1073949   44.42   <2e-16 ***
## x.var       0.0069047  0.0002432   28.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.483 on 762 degrees of freedom
## Multiple R-squared:  0.514,  Adjusted R-squared:  0.5133 
## F-statistic: 805.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -45.40  -26.40    4.29   18.82  190.21  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.807e+00  2.124e-03    2733   <2e-16 ***
## x.var       5.067e-03  3.508e-06    1444   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3404677  on 763  degrees of freedom
## Residual deviance:  577536  on 762  degrees of freedom
## AIC: 584533
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## NICARAGUA  --  18004 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3009.4 -1231.2   232.4  1147.8  2691.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2035.4201   116.0746  -17.54   <2e-16 ***
## x.var          24.6438     0.2629   93.74   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1603 on 762 degrees of freedom
## Multiple R-squared:  0.9202, Adjusted R-squared:  0.9201 
## F-statistic:  8787 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3707 -1.0520  0.0454  1.6009  2.5031 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.7917850  0.1341014   28.28   <2e-16 ***
## x.var       0.0101560  0.0003037   33.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.851 on 762 degrees of freedom
## Multiple R-squared:  0.5947, Adjusted R-squared:  0.5942 
## F-statistic:  1118 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -60.605  -12.411    0.634   16.591   30.845  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.141e+00  1.320e-03    5412   <2e-16 ***
## x.var       3.770e-03  2.296e-06    1642   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3812494  on 763  degrees of freedom
## Residual deviance:  571879  on 762  degrees of freedom
## AIC: 579040
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NIGER  --  8746 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1421.64  -370.70    68.95   515.34   922.66 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -844.80775   43.71574  -19.32   <2e-16 ***
## x.var         11.86049    0.09901  119.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 603.6 on 762 degrees of freedom
## Multiple R-squared:  0.9496, Adjusted R-squared:  0.9495 
## F-statistic: 1.435e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6746 -0.6764  0.5499  1.0811  1.6551 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.204431   0.113907   36.91   <2e-16 ***
## x.var       0.008106   0.000258   31.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.573 on 762 degrees of freedom
## Multiple R-squared:  0.5644, Adjusted R-squared:  0.5638 
## F-statistic: 987.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -41.325  -13.447   -6.446   12.029   32.469  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.5411661  0.0018242    3586   <2e-16 ***
## x.var       0.0035968  0.0000032    1124   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1789592  on 763  degrees of freedom
## Residual deviance:  293898  on 762  degrees of freedom
## AIC: 300778
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NIGERIA  --  254352 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23967  -9119  -2350   5786  31484 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -26650.722    979.319  -27.21   <2e-16 ***
## x.var          374.603      2.218  168.89   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13520 on 762 degrees of freedom
## Multiple R-squared:  0.974,  Adjusted R-squared:  0.9739 
## F-statistic: 2.852e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5080 -1.1061  0.5539  1.5263  2.4484 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.0980014  0.1607894   37.92   <2e-16 ***
## x.var       0.0110813  0.0003642   30.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.22 on 762 degrees of freedom
## Multiple R-squared:  0.5486, Adjusted R-squared:  0.548 
## F-statistic: 925.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -235.200   -92.629     6.373    44.179   185.619  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.995e+00  3.245e-04   30801   <2e-16 ***
## x.var       3.596e-03  5.693e-07    6315   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 58432275  on 763  degrees of freedom
## Residual deviance: 11206508  on 762  degrees of freedom
## AIC: 11215737
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NORTH MACEDONIA  --  295082 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35185  -9211  -2062  10481  46161 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -46466.194   1331.076  -34.91   <2e-16 ***
## x.var          386.632      3.015  128.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18380 on 762 degrees of freedom
## Multiple R-squared:  0.9557, Adjusted R-squared:  0.9557 
## F-statistic: 1.645e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9267 -0.8568  0.5954  1.3599  1.8393 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.5137192  0.1346410   40.95   <2e-16 ***
## x.var       0.0117996  0.0003049   38.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.859 on 762 degrees of freedom
## Multiple R-squared:  0.6627, Adjusted R-squared:  0.6623 
## F-statistic:  1497 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -170.90  -116.28   -45.95    99.14   179.81  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.349e+00  3.936e-04   23751   <2e-16 ***
## x.var       4.510e-03  6.636e-07    6796   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 69707137  on 763  degrees of freedom
## Residual deviance: 10356961  on 762  degrees of freedom
## AIC: 10365959
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## NORWAY  --  1186422 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -110830  -74920  -45743   42345  804816 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -122916.3    10065.8  -12.21   <2e-16 ***
## x.var           660.4       22.8   28.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 139000 on 762 degrees of freedom
## Multiple R-squared:  0.5241, Adjusted R-squared:  0.5234 
## F-statistic: 839.1 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.6044 -0.4803  0.6185  0.9060  1.6964 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.2351356  0.1210882   51.49   <2e-16 ***
## x.var       0.0105497  0.0002742   38.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.672 on 762 degrees of freedom
## Multiple R-squared:  0.6601, Adjusted R-squared:  0.6596 
## F-statistic:  1480 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -215.34   -77.38    -4.39    68.51   499.54  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.922e+00  5.018e-04   15787   <2e-16 ***
## x.var       7.287e-03  7.802e-07    9341   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 156574006  on 763  degrees of freedom
## Residual deviance:   9525913  on 762  degrees of freedom
## AIC: 9535102
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## OMAN  --  378922 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34759 -16267   -578  12753  44778 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -36046.045   1472.925  -24.47   <2e-16 ***
## x.var          518.133      3.336  155.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20340 on 762 degrees of freedom
## Multiple R-squared:  0.9694, Adjusted R-squared:  0.9693 
## F-statistic: 2.412e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.9409 -1.0260  0.3761  1.6205  2.6399 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.5850288  0.1570868   41.92   <2e-16 ***
## x.var       0.0107850  0.0003558   30.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.169 on 762 degrees of freedom
## Multiple R-squared:  0.5467, Adjusted R-squared:  0.5461 
## F-statistic: 918.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -277.67  -139.72    40.32    78.98   156.05  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.034e+01  2.744e-04   37671   <2e-16 ***
## x.var       3.573e-03  4.820e-07    7413   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 79390020  on 763  degrees of freedom
## Residual deviance: 14437312  on 762  degrees of freedom
## AIC: 14446840
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PAKISTAN  --  1505328 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -111661  -54740    5421   47003  172120 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -174253.91    4846.27  -35.96   <2e-16 ***
## x.var          2133.55      10.98  194.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66910 on 762 degrees of freedom
## Multiple R-squared:  0.9802, Adjusted R-squared:  0.9802 
## F-statistic: 3.778e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.1914 -0.8943  0.5834  1.6170  2.7036 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.8143105  0.1760270   44.39   <2e-16 ***
## x.var       0.0110914  0.0003987   27.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.43 on 762 degrees of freedom
## Multiple R-squared:  0.5039, Adjusted R-squared:  0.5032 
## F-statistic:   774 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -518.54  -234.60    67.57   132.88   243.89  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.161e+01  1.413e-04   82173   <2e-16 ***
## x.var       3.756e-03  2.461e-07   15262   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 323695794  on 763  degrees of freedom
## Residual deviance:  44109006  on 762  degrees of freedom
## AIC: 44119560
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PALAU  --  3667 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -358.4 -229.0  -79.4   72.6 3255.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -200.58857   36.32677  -5.522  4.6e-08 ***
## x.var          0.80111    0.08228   9.737  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 501.6 on 762 degrees of freedom
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.1095 
## F-statistic: 94.81 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7359 -0.7922 -0.1139  0.4353  5.5415 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.1540681  0.0968672  -11.91   <2e-16 ***
## x.var        0.0050000  0.0002194   22.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.337 on 762 degrees of freedom
## Multiple R-squared:  0.4053, Adjusted R-squared:  0.4046 
## F-statistic: 519.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -28.1601   -0.0015    0.0000    0.0000   22.4686  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.666e+01  1.912e-01  -244.1   <2e-16 ***
## x.var        7.238e-02  2.546e-04   284.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 503903  on 763  degrees of freedom
## Residual deviance:  16707  on 762  degrees of freedom
## AIC: 17611
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## PANAMA  --  752907 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -74930 -33491   -691  27726 137788 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -78597.174   3171.860  -24.78   <2e-16 ***
## x.var          909.017      7.184  126.54   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43790 on 762 degrees of freedom
## Multiple R-squared:  0.9546, Adjusted R-squared:  0.9545 
## F-statistic: 1.601e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.2997 -0.9857  0.6590  1.6952  2.1874 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.7517507  0.1652111   40.87   <2e-16 ***
## x.var       0.0114164  0.0003742   30.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.281 on 762 degrees of freedom
## Multiple R-squared:  0.5499, Adjusted R-squared:  0.5493 
## F-statistic: 930.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -330.86  -196.15   -21.73   118.24   278.32  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.070e+01  2.206e-04   48518   <2e-16 ***
## x.var       3.834e-03  3.827e-07   10019   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 148639848  on 763  degrees of freedom
## Residual deviance:  27333444  on 762  degrees of freedom
## AIC: 27343258
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PAPUA NEW GUINEA  --  40621 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10430.6  -3823.7   -896.2   5717.3   9941.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -9897.8629   427.0523  -23.18   <2e-16 ***
## x.var          53.1115     0.9672   54.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5896 on 762 degrees of freedom
## Multiple R-squared:  0.7983, Adjusted R-squared:  0.798 
## F-statistic:  3015 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88820 -0.92228  0.01439  0.92483  1.79930 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.0179864  0.0748707   13.60   <2e-16 ***
## x.var       0.0150037  0.0001696   88.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.034 on 762 degrees of freedom
## Multiple R-squared:  0.9113, Adjusted R-squared:  0.9112 
## F-statistic:  7829 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -76.88  -35.95  -25.16   17.17   76.21  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.390e+00  1.774e-03    3038   <2e-16 ***
## x.var       7.305e-03  2.758e-06    2649   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 13000226  on 763  degrees of freedom
## Residual deviance:  1153934  on 762  degrees of freedom
## AIC: 1160376
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PARAGUAY  --  638153 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -78350 -57383  -7893  54535 123400 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.243e+05  4.275e+03  -29.07   <2e-16 ***
## x.var        8.889e+02  9.683e+00   91.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 59030 on 762 degrees of freedom
## Multiple R-squared:  0.9171, Adjusted R-squared:  0.917 
## F-statistic:  8427 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3806 -1.0780  0.4501  1.4341  2.2655 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7119922  0.1385290   34.01   <2e-16 ***
## x.var       0.0145339  0.0003137   46.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.913 on 762 degrees of freedom
## Multiple R-squared:  0.738,  Adjusted R-squared:  0.7376 
## F-statistic:  2146 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -284.71  -218.62   -52.64    96.12   304.67  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.793e+00  2.903e-04   33732   <2e-16 ***
## x.var       5.049e-03  4.798e-07   10524   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 176682977  on 763  degrees of freedom
## Residual deviance:  26827030  on 762  degrees of freedom
## AIC: 26836198
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PERU  --  3503892 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -350034 -100973  -24681   95718  643346 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -285744.8    12762.3  -22.39   <2e-16 ***
## x.var          4124.1       28.9  142.68   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 176200 on 762 degrees of freedom
## Multiple R-squared:  0.9639, Adjusted R-squared:  0.9639 
## F-statistic: 2.036e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.5893 -1.2031  0.7038  2.0029  2.6528 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.0636428  0.1957778   41.19   <2e-16 ***
## x.var       0.0119459  0.0004434   26.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.703 on 762 degrees of freedom
## Multiple R-squared:  0.4878, Adjusted R-squared:  0.4872 
## F-statistic: 725.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -787.06  -336.76    64.14   251.12   420.17  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.241e+01  9.716e-05  127765   <2e-16 ***
## x.var       3.569e-03  1.707e-07   20908   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 628151959  on 763  degrees of freedom
## Residual deviance: 111683940  on 762  degrees of freedom
## AIC: 111694918
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PHILIPPINES  --  3655709 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -613766 -341618  -78280  354535  820344 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -742671.56   29110.73  -25.51   <2e-16 ***
## x.var          4730.90      65.93   71.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 401900 on 762 degrees of freedom
## Multiple R-squared:  0.8711, Adjusted R-squared:  0.8709 
## F-statistic:  5149 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.2129 -0.7023  0.5505  1.3291  2.2706 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.1077715  0.1481814   47.97   <2e-16 ***
## x.var       0.0131349  0.0003356   39.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 762 degrees of freedom
## Multiple R-squared:  0.6678, Adjusted R-squared:  0.6674 
## F-statistic:  1532 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -486.32  -337.77    21.09   128.46   345.87  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.103e+01  1.418e-04   77773   <2e-16 ***
## x.var       5.665e-03  2.297e-07   24662   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 920511605  on 763  degrees of freedom
## Residual deviance:  46316961  on 762  degrees of freedom
## AIC: 46327613
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## POLAND  --  5602680 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -783520 -314468  -86131  312651 1302199 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -887889.98   31975.01  -27.77   <2e-16 ***
## x.var          6791.06      72.42   93.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 441500 on 762 degrees of freedom
## Multiple R-squared:  0.9203, Adjusted R-squared:  0.9202 
## F-statistic:  8794 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4481 -1.1197  0.9548  1.3524  2.4372 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.8408494  0.1648421   41.50   <2e-16 ***
## x.var       0.0144572  0.0003733   38.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.276 on 762 degrees of freedom
## Multiple R-squared:  0.6631, Adjusted R-squared:  0.6626 
## F-statistic:  1500 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -776.8  -656.4  -333.3   441.2   925.6  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.202e+01  9.947e-05  120815   <2e-16 ***
## x.var       4.782e-03  1.660e-07   28816   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1340919379  on 763  degrees of freedom
## Residual deviance:  246029771  on 762  degrees of freedom
## AIC: 246040552
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## PORTUGAL  --  3219439 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -295576 -174287  -94099   92243 1551868 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -372613.25   22833.99  -16.32   <2e-16 ***
## x.var          2670.40      51.72   51.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 315300 on 762 degrees of freedom
## Multiple R-squared:  0.7777, Adjusted R-squared:  0.7774 
## F-statistic:  2666 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.6535 -0.9350  0.9812  1.5139  1.7652 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.1690409  0.1581281   45.34   <2e-16 ***
## x.var       0.0121103  0.0003581   33.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.183 on 762 degrees of freedom
## Multiple R-squared:  0.6001, Adjusted R-squared:  0.5996 
## F-statistic:  1143 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -478.7  -300.2  -185.5   177.3   563.9  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.090e+01  1.672e-04   65186   <2e-16 ***
## x.var       5.040e-03  2.764e-07   18236   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 521053575  on 763  degrees of freedom
## Residual deviance:  71522056  on 762  degrees of freedom
## AIC: 71532405
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## QATAR  --  355397 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37877 -16157     46  15596  46086 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5507.634   1506.073   3.657 0.000273 ***
## x.var        397.696      3.411 116.591  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20790 on 762 degrees of freedom
## Multiple R-squared:  0.9469, Adjusted R-squared:  0.9468 
## F-statistic: 1.359e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8581 -0.9479  0.4874  1.5878  2.5111 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.5166879  0.1670742   44.99   <2e-16 ***
## x.var       0.0089854  0.0003784   23.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.307 on 762 degrees of freedom
## Multiple R-squared:  0.4253, Adjusted R-squared:  0.4245 
## F-statistic: 563.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -328.17   -75.62    52.66    84.36   105.42  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.077e+01  2.480e-04   43406   <2e-16 ***
## x.var       2.696e-03  4.566e-07    5905   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 52686712  on 763  degrees of freedom
## Residual deviance: 14162372  on 762  degrees of freedom
## AIC: 14172077
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ROMANIA  --  2697566 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -292106 -146540  -14907   93670  793848 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -388090.56   14223.61  -27.29   <2e-16 ***
## x.var          2999.75      32.21   93.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 196400 on 762 degrees of freedom
## Multiple R-squared:  0.9192, Adjusted R-squared:  0.9191 
## F-statistic:  8671 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3994 -0.9339  0.8979  1.5194  2.0883 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.9490183  0.1579782   43.99   <2e-16 ***
## x.var       0.0128671  0.0003578   35.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.181 on 762 degrees of freedom
## Multiple R-squared:  0.6292, Adjusted R-squared:  0.6288 
## F-statistic:  1293 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -441.9  -374.5  -113.6   250.0   444.5  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.123e+01  1.485e-04   75603   <2e-16 ***
## x.var       4.745e-03  2.481e-07   19123   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 554234315  on 763  degrees of freedom
## Residual deviance:  73746118  on 762  degrees of freedom
## AIC: 73756528
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## RUSSIA  --  15566425 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1020047  -691194  -319458   481961  5334306 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1970438.0    72735.9  -27.09   <2e-16 ***
## x.var          15971.9      164.7   96.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1004000 on 762 degrees of freedom
## Multiple R-squared:  0.925,  Adjusted R-squared:  0.9249 
## F-statistic:  9400 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.8024 -1.1087  0.6348  1.9191  2.6291 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 8.6862229  0.1905351   45.59   <2e-16 ***
## x.var       0.0129089  0.0004315   29.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.631 on 762 degrees of freedom
## Multiple R-squared:  0.5401, Adjusted R-squared:  0.5395 
## F-statistic: 894.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1098.2   -359.1   -140.9    258.0    764.5  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.301e+01  6.227e-05  208961   <2e-16 ***
## x.var       4.588e-03  1.047e-07   43837   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2702711204  on 763  degrees of freedom
## Residual deviance:  215112567  on 762  degrees of freedom
## AIC: 215124356
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## RWANDA  --  129436 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -30557 -20002   2013  16489  31147 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -31323.569   1363.773  -22.97   <2e-16 ***
## x.var          176.957      3.089   57.29   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18830 on 762 degrees of freedom
## Multiple R-squared:  0.8116, Adjusted R-squared:  0.8113 
## F-statistic:  3282 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4281 -0.4434  0.2849  0.9485  1.7073 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.7631802  0.1026966   36.64   <2e-16 ***
## x.var       0.0127863  0.0002326   54.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.418 on 762 degrees of freedom
## Multiple R-squared:  0.7986, Adjusted R-squared:  0.7984 
## F-statistic:  3022 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -138.21   -50.47   -26.97     9.50   117.11  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.039e+00  8.759e-04    8036   <2e-16 ***
## x.var       6.666e-03  1.381e-06    4827   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 39189709  on 763  degrees of freedom
## Residual deviance:  2213340  on 762  degrees of freedom
## AIC: 2221262
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT KITTS AND NEVIS  --  5524 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1343.08  -817.97   -67.29   539.36  2869.63 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1079.5803    70.6034  -15.29   <2e-16 ***
## x.var           4.9421     0.1599   30.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 974.8 on 762 degrees of freedom
## Multiple R-squared:  0.5563, Adjusted R-squared:  0.5557 
## F-statistic: 955.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6218 -0.5816  0.1770  0.5596  1.4766 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.3492534  0.0551062   6.338 3.99e-10 ***
## x.var       0.0105189  0.0001248  84.281  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7608 on 762 degrees of freedom
## Multiple R-squared:  0.9031, Adjusted R-squared:  0.903 
## F-statistic:  7103 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -20.543   -4.614   -1.349    3.091   22.670  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.326e-02  1.021e-02  -4.236 2.27e-05 ***
## x.var        1.168e-02  1.492e-05 782.857  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1525592  on 763  degrees of freedom
## Residual deviance:   51204  on 762  degrees of freedom
## AIC: 55819
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT LUCIA  --  22594 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3568.3 -2282.4  -541.1  1558.0  8881.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4432.3789   204.1791  -21.71   <2e-16 ***
## x.var          23.7506     0.4624   51.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2819 on 762 degrees of freedom
## Multiple R-squared:  0.7759, Adjusted R-squared:  0.7756 
## F-statistic:  2638 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61776 -0.70490  0.04092  0.56194  1.77597 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.9062534  0.0612138   14.80   <2e-16 ***
## x.var       0.0136829  0.0001386   98.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8452 on 762 degrees of freedom
## Multiple R-squared:  0.9274, Adjusted R-squared:  0.9273 
## F-statistic:  9740 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -33.77  -24.42  -15.54   13.29   38.24  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.571e+00  2.662e-03    1717   <2e-16 ***
## x.var       7.325e-03  4.135e-06    1771   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 5698046  on 763  degrees of freedom
## Residual deviance:  390412  on 762  degrees of freedom
## AIC: 396403
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAINT VINCENT AND THE GRENADINES  --  8301 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1410.0  -850.8  -416.6   875.9  4377.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1667.9996    80.0925  -20.83   <2e-16 ***
## x.var           9.1417     0.1814   50.40   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1106 on 762 degrees of freedom
## Multiple R-squared:  0.7692, Adjusted R-squared:  0.7689 
## F-statistic:  2540 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8170 -0.4470  0.0085  0.4799  1.5083 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.2100777  0.0556729   21.74   <2e-16 ***
## x.var       0.0116718  0.0001261   92.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7687 on 762 degrees of freedom
## Multiple R-squared:  0.9183, Adjusted R-squared:  0.9182 
## F-statistic:  8569 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.487  -11.677   -9.950    6.806   26.069  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 3.828e+00  4.087e-03   936.9   <2e-16 ***
## x.var       7.021e-03  6.390e-06  1098.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2121642  on 763  degrees of freedom
## Residual deviance:  138826  on 762  degrees of freedom
## AIC: 144456
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAMOA  --  33 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1103 -2.2524 -0.7446  0.4635 24.6799 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.8119952  0.3789832   -7.42 3.14e-13 ***
## x.var        0.0149826  0.0008583   17.45  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.233 on 762 degrees of freedom
## Multiple R-squared:  0.2856, Adjusted R-squared:  0.2847 
## F-statistic: 304.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66046 -0.32517 -0.00817  0.25519  1.43304 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.780e-01  3.080e-02  -12.27   <2e-16 ***
## x.var        3.326e-03  6.975e-05   47.68   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4252 on 762 degrees of freedom
## Multiple R-squared:  0.749,  Adjusted R-squared:  0.7487 
## F-statistic:  2274 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.2877  -0.8414  -0.4584   0.7720   4.2690  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.8552499  0.1073692  -26.59   <2e-16 ***
## x.var        0.0074078  0.0001665   44.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 4815.5  on 763  degrees of freedom
## Residual deviance: 1441.0  on 762  degrees of freedom
## AIC: 2868.7
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAN MARINO  --  14262 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1628.3  -905.1  -287.2   550.4  5668.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1552.8546    98.3171  -15.79   <2e-16 ***
## x.var          13.2808     0.2227   59.64   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1357 on 762 degrees of freedom
## Multiple R-squared:  0.8236, Adjusted R-squared:  0.8233 
## F-statistic:  3557 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6573 -0.5364  0.3956  0.7569  1.2585 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.3670662  0.0901367   48.45   <2e-16 ***
## x.var       0.0076378  0.0002041   37.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.244 on 762 degrees of freedom
## Multiple R-squared:  0.6475, Adjusted R-squared:  0.647 
## F-statistic:  1400 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -31.424  -15.620   -4.363   13.289   32.525  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.034e+00  2.089e-03    2889   <2e-16 ***
## x.var       4.433e-03  3.532e-06    1255   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2249399  on 763  degrees of freedom
## Residual deviance:  240211  on 762  degrees of freedom
## AIC: 247115
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## SAO TOME AND PRINCIPE  --  5932 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -645.65 -353.59  -39.83  197.18 1665.87 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -651.35848   36.39541  -17.90   <2e-16 ***
## x.var          6.58794    0.08243   79.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 502.5 on 762 degrees of freedom
## Multiple R-squared:  0.8934, Adjusted R-squared:  0.8933 
## F-statistic:  6387 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7652 -0.8734  0.3418  1.1343  2.1153 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.0999707  0.1117936   27.73   <2e-16 ***
## x.var       0.0088697  0.0002532   35.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.544 on 762 degrees of freedom
## Multiple R-squared:  0.6169, Adjusted R-squared:  0.6164 
## F-statistic:  1227 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -27.6511   -6.1612    0.8409    6.8583   15.2613  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.610e+00  2.727e-03  2057.3   <2e-16 ***
## x.var       4.055e-03  4.685e-06   865.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1027935  on 763  degrees of freedom
## Residual deviance:  104706  on 762  degrees of freedom
## AIC: 110932
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SAUDI ARABIA  --  741864 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -96790 -43262   -777  40508  95179 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29277.735   3920.346   7.468 2.23e-13 ***
## x.var         858.096      8.879  96.643  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 54130 on 762 degrees of freedom
## Multiple R-squared:  0.9246, Adjusted R-squared:  0.9245 
## F-statistic:  9340 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3258 -1.0123  0.4311  1.8407  2.7443 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.9364532  0.1842118   43.08   <2e-16 ***
## x.var       0.0097343  0.0004172   23.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.543 on 762 degrees of freedom
## Multiple R-squared:  0.4167, Adjusted R-squared:  0.4159 
## F-statistic: 544.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -514.83  -130.47    61.66   127.43   230.74  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.166e+01  1.616e-04   72151   <2e-16 ***
## x.var       2.548e-03  3.001e-07    8490   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 117323596  on 763  degrees of freedom
## Residual deviance:  38501692  on 762  degrees of freedom
## AIC: 38511933
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SENEGAL  --  85619 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10843.8  -5048.7   -227.7   4611.0  13712.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13838.811    473.606  -29.22   <2e-16 ***
## x.var          126.782      1.073  118.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6539 on 762 degrees of freedom
## Multiple R-squared:  0.9483, Adjusted R-squared:  0.9482 
## F-statistic: 1.397e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7917 -0.7304  0.3773  1.1954  1.9179 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.387721   0.130230   41.37   <2e-16 ***
## x.var       0.010099   0.000295   34.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.798 on 762 degrees of freedom
## Multiple R-squared:  0.606,  Adjusted R-squared:  0.6055 
## F-statistic:  1172 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -105.732   -54.799     2.945    28.737    79.160  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.416e+00  6.510e-04   12926   <2e-16 ***
## x.var       4.261e-03  1.109e-06    3843   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 20719259  on 763  degrees of freedom
## Residual deviance:  2176094  on 762  degrees of freedom
## AIC: 2184493
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SERBIA  --  1896575 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -244689 -110595  -36225   98410  565044 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -327496.52   12106.58  -27.05   <2e-16 ***
## x.var          2171.50      27.42   79.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 167200 on 762 degrees of freedom
## Multiple R-squared:  0.8917, Adjusted R-squared:  0.8915 
## F-statistic:  6272 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7065 -0.8577  0.8065  1.3939  1.9885 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.1119374  0.1486959   41.10   <2e-16 ***
## x.var       0.0135129  0.0003368   40.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.053 on 762 degrees of freedom
## Multiple R-squared:  0.6787, Adjusted R-squared:  0.6783 
## F-statistic:  1610 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -345.6  -274.0  -110.3   184.7   395.5  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.042e+01  1.999e-04   52113   <2e-16 ***
## x.var       5.424e-03  3.263e-07   16622   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 433343445  on 763  degrees of freedom
## Residual deviance:  45578768  on 762  degrees of freedom
## AIC: 45588711
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SEYCHELLES  --  39181 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -7531  -4274    974   2096  13111 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -8520.0995   370.2512  -23.01   <2e-16 ***
## x.var          45.3938     0.8386   54.13   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5112 on 762 degrees of freedom
## Multiple R-squared:  0.7936, Adjusted R-squared:  0.7934 
## F-statistic:  2930 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88988 -0.59771  0.08325  0.81636  1.14439 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.1292907  0.0597473    18.9   <2e-16 ***
## x.var       0.0143507  0.0001353   106.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8249 on 762 degrees of freedom
## Multiple R-squared:  0.9365, Adjusted R-squared:  0.9365 
## F-statistic: 1.125e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -54.866  -34.346  -23.899    1.556   70.503  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.161e+00  1.951e-03    2645   <2e-16 ***
## x.var       7.409e-03  3.025e-06    2449   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 11291915  on 763  degrees of freedom
## Residual deviance:  1067162  on 762  degrees of freedom
## AIC: 1073520
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SIERRA LEONE  --  7663 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -685.9 -311.5   23.0  236.6  854.2 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -576.99955   26.55092  -21.73   <2e-16 ***
## x.var         10.86178    0.06013  180.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 366.6 on 762 degrees of freedom
## Multiple R-squared:  0.9772, Adjusted R-squared:  0.9771 
## F-statistic: 3.263e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4364 -0.8736  0.2501  1.2667  2.0653 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.8313875  0.1242816   30.83   <2e-16 ***
## x.var       0.0087678  0.0002815   31.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.716 on 762 degrees of freedom
## Multiple R-squared:  0.5601, Adjusted R-squared:  0.5595 
## F-statistic: 970.2 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -43.909  -13.364    4.747    9.970   20.028  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.640e+00  1.794e-03    3701   <2e-16 ***
## x.var       3.354e-03  3.186e-06    1053   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1558001  on 763  degrees of freedom
## Residual deviance:  271733  on 762  degrees of freedom
## AIC: 278501
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SINGAPORE  --  642605 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -97388 -50726   7219  29415 416595 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -42480.73    4996.80  -8.502   <2e-16 ***
## x.var          351.43      11.32  31.053   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 68990 on 762 degrees of freedom
## Multiple R-squared:  0.5586, Adjusted R-squared:  0.558 
## F-statistic: 964.3 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.7529 -0.4932 -0.0667  1.1292  1.8143 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.7456033  0.1079988   71.72   <2e-16 ***
## x.var       0.0072535  0.0002446   29.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.491 on 762 degrees of freedom
## Multiple R-squared:  0.5358, Adjusted R-squared:  0.5351 
## F-statistic: 879.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -239.47  -139.77    16.63    92.46   485.75  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.241e+00  4.144e-04   22302   <2e-16 ***
## x.var       4.527e-03  6.981e-07    6484   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 67095763  on 763  degrees of freedom
## Residual deviance: 12979073  on 762  degrees of freedom
## AIC: 12988514
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SLOVAKIA  --  2052260 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -233134 -150889  -15944   96826  740712 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -317554.08   12852.60  -24.71   <2e-16 ***
## x.var          2132.33      29.11   73.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 177500 on 762 degrees of freedom
## Multiple R-squared:  0.8757, Adjusted R-squared:  0.8755 
## F-statistic:  5366 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4661 -1.0481  0.2116  1.4314  2.3758 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.7751882  0.1350006   35.37   <2e-16 ***
## x.var       0.0157025  0.0003058   51.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.864 on 762 degrees of freedom
## Multiple R-squared:  0.7758, Adjusted R-squared:  0.7756 
## F-statistic:  2637 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -449.8  -316.5  -169.2   172.6   522.2  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.045e+01  1.991e-04   52491   <2e-16 ***
## x.var       5.355e-03  3.257e-07   16444   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 451298997  on 763  degrees of freedom
## Residual deviance:  74394994  on 762  degrees of freedom
## AIC: 74404557
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SLOVENIA  --  884830 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -80634 -54015 -13383  17254 398230 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -122566.13    6051.74  -20.25   <2e-16 ***
## x.var           797.34      13.71   58.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 83550 on 762 degrees of freedom
## Multiple R-squared:  0.8162, Adjusted R-squared:  0.816 
## F-statistic:  3384 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5494 -0.8396  0.2280  1.0885  2.0725 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9651911  0.1241338   40.00   <2e-16 ***
## x.var       0.0135859  0.0002811   48.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.714 on 762 degrees of freedom
## Multiple R-squared:  0.754,  Adjusted R-squared:  0.7536 
## F-statistic:  2335 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -242.71  -169.00   -97.87   121.16   232.16  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.340e+00  3.370e-04   27715   <2e-16 ***
## x.var       5.534e-03  5.481e-07   10098   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 165943570  on 763  degrees of freedom
## Residual deviance:  21278387  on 762  degrees of freedom
## AIC: 21287479
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOLOMON ISLANDS  --  6348 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -492.6 -297.1  -98.6   89.2 5790.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -262.7810    52.6251  -4.993 7.36e-07 ***
## x.var          1.0734     0.1192   9.006  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 726.6 on 762 degrees of freedom
## Multiple R-squared:  0.0962, Adjusted R-squared:  0.09501 
## F-statistic:  81.1 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3856 -0.7467 -0.0865  0.5000  3.9061 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.5864653  0.0713373  -8.221 8.67e-16 ***
## x.var        0.0071157  0.0001616  44.041  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9849 on 762 degrees of freedom
## Multiple R-squared:  0.7179, Adjusted R-squared:  0.7176 
## F-statistic:  1940 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -44.546   -0.005    0.000   13.258   39.061  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.835e+01  7.633e-02  -240.4   <2e-16 ***
## x.var        3.483e-02  1.037e-04   336.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 650502  on 763  degrees of freedom
## Residual deviance: 135370  on 762  degrees of freedom
## AIC: 137901
## 
## Number of Fisher Scoring iterations: 7
## 
## --------------------------------------------------------------------------------

## SOMALIA  --  26313 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5241.1 -1200.1   559.8  1480.4  4227.2 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4264.4693   157.5287  -27.07   <2e-16 ***
## x.var          37.3184     0.3568  104.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2175 on 762 degrees of freedom
## Multiple R-squared:  0.9349, Adjusted R-squared:  0.9348 
## F-statistic: 1.094e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7775 -0.7069  0.5026  1.2189  2.1941 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2440547  0.1271090   33.39   <2e-16 ***
## x.var       0.0098786  0.0002879   34.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.755 on 762 degrees of freedom
## Multiple R-squared:  0.6071, Adjusted R-squared:  0.6066 
## F-statistic:  1177 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -57.700  -19.461   -1.145   11.387   45.258  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.111e+00  1.230e-03    5782   <2e-16 ***
## x.var       4.372e-03  2.085e-06    2097   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 6128521  on 763  degrees of freedom
## Residual deviance:  548362  on 762  degrees of freedom
## AIC: 555792
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOUTH AFRICA  --  3665149 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -389844 -148813   -8243  174295  548994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -554248.40   15702.36   -35.3   <2e-16 ***
## x.var          5254.27      35.56   147.7   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 216800 on 762 degrees of freedom
## Multiple R-squared:  0.9663, Adjusted R-squared:  0.9662 
## F-statistic: 2.183e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.0092 -1.1101  0.3365  1.8642  3.1297 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.4378644  0.1875107   39.67   <2e-16 ***
## x.var       0.0132859  0.0004247   31.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.589 on 762 degrees of freedom
## Multiple R-squared:  0.5622, Adjusted R-squared:  0.5617 
## F-statistic: 978.7 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -738.74  -367.27    79.07   201.27   474.24  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.220e+01  9.943e-05  122658   <2e-16 ***
## x.var       4.184e-03  1.699e-07   24634   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 867753954  on 763  degrees of freedom
## Residual deviance: 111082322  on 762  degrees of freedom
## AIC: 111093214
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SOUTH SUDAN  --  16946 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2796.65  -840.75    43.35  1084.99  2218.60 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2242.2652    93.8535  -23.89   <2e-16 ***
## x.var          23.6621     0.2126  111.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1296 on 762 degrees of freedom
## Multiple R-squared:  0.9421, Adjusted R-squared:  0.942 
## F-statistic: 1.239e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3240 -1.1097  0.5585  1.2432  2.4128 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5506850  0.1315396   26.99   <2e-16 ***
## x.var       0.0104505  0.0002979   35.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.816 on 762 degrees of freedom
## Multiple R-squared:  0.6176, Adjusted R-squared:  0.6171 
## F-statistic:  1230 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -54.548  -19.711   -4.752    8.030   51.621  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.945e+00  1.414e-03    4912   <2e-16 ***
## x.var       3.978e-03  2.437e-06    1632   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3875690  on 763  degrees of freedom
## Residual deviance:  614003  on 762  degrees of freedom
## AIC: 621036
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SPAIN  --  10914105 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -950678 -526083 -280925  153243 3893262 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1365810.0    66395.4  -20.57   <2e-16 ***
## x.var          10977.3      150.4   73.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 916700 on 762 degrees of freedom
## Multiple R-squared:  0.8749, Adjusted R-squared:  0.8747 
## F-statistic:  5329 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.3609 -0.7919  0.8294  1.4199  1.9871 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.2498911  0.1680248   55.05   <2e-16 ***
## x.var       0.0110987  0.0003806   29.16   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.32 on 762 degrees of freedom
## Multiple R-squared:  0.5275, Adjusted R-squared:  0.5268 
## F-statistic: 850.6 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -860.74  -527.19   -98.06   306.29   841.54  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.262e+01  7.554e-05  167042   <2e-16 ***
## x.var       4.615e-03  1.268e-07   36385   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1914425489  on 763  degrees of freedom
## Residual deviance:  196427384  on 762  degrees of freedom
## AIC: 196439074
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SRI LANKA  --  640578 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -155561  -95476   -4844   99519  167621 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -168546       7067  -23.85   <2e-16 ***
## x.var            925         16   57.79   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 97570 on 762 degrees of freedom
## Multiple R-squared:  0.8142, Adjusted R-squared:  0.814 
## F-statistic:  3340 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3481 -0.4665  0.4832  0.9419  1.3697 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2750700  0.1003915   42.58   <2e-16 ***
## x.var       0.0146136  0.0002274   64.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.386 on 762 degrees of freedom
## Multiple R-squared:  0.8443, Adjusted R-squared:  0.8441 
## F-statistic:  4131 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -378.04  -127.59   -81.50    35.63   285.70  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.457e+00  4.052e-04   20871   <2e-16 ***
## x.var       7.004e-03  6.338e-07   11051   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 216301463  on 763  degrees of freedom
## Residual deviance:  16016618  on 762  degrees of freedom
## AIC: 16025529
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SUDAN  --  61350 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4925.9 -2711.3    67.7  2372.2  8060.5 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3469.1908   218.9102  -15.85   <2e-16 ***
## x.var          74.3198     0.4958  149.90   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3022 on 762 degrees of freedom
## Multiple R-squared:  0.9672, Adjusted R-squared:  0.9672 
## F-statistic: 2.247e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5276 -1.1704  0.4815  1.6181  2.5198 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9949111  0.1533921   32.56   <2e-16 ***
## x.var       0.0102436  0.0003474   29.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.118 on 762 degrees of freedom
## Multiple R-squared:  0.5329, Adjusted R-squared:  0.5323 
## F-statistic: 869.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -119.902   -37.106     2.019    35.337    69.639  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.623e+00  6.723e-04   12828   <2e-16 ***
## x.var       3.276e-03  1.199e-06    2733   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 10922114  on 763  degrees of freedom
## Residual deviance:  2307030  on 762  degrees of freedom
## AIC: 2315145
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SUMMER OLYMPICS 2020  --  865 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -422.35 -202.51   33.73  204.82  363.86 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -273.54999   16.74250  -16.34   <2e-16 ***
## x.var          1.29982    0.03792   34.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 231.2 on 762 degrees of freedom
## Multiple R-squared:  0.6066, Adjusted R-squared:  0.6061 
## F-statistic:  1175 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4018 -1.5586  0.4035  1.5073  2.4870 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.2106069  0.1256661  -17.59   <2e-16 ***
## x.var        0.0109191  0.0002846   38.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.735 on 762 degrees of freedom
## Multiple R-squared:  0.6589, Adjusted R-squared:  0.6584 
## F-statistic:  1472 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -22.539   -9.183   -3.937   -1.590   25.428  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.254e-01  1.656e-02  -13.62   <2e-16 ***
## x.var        1.004e-02  2.462e-05  407.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 440053  on 763  degrees of freedom
## Residual deviance:  84305  on 762  degrees of freedom
## AIC: 86264
## 
## Number of Fisher Scoring iterations: 8
## 
## --------------------------------------------------------------------------------

## SURINAME  --  77935 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14668.9  -8231.2   -221.8   7230.8  27493.6 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -14928.477    723.405  -20.64   <2e-16 ***
## x.var           85.731      1.638   52.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9988 on 762 degrees of freedom
## Multiple R-squared:  0.7823, Adjusted R-squared:  0.782 
## F-statistic:  2738 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5674 -1.3031  0.1735  1.2942  2.4912 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.8828422  0.1130554   25.50   <2e-16 ***
## x.var       0.0131645  0.0002561   51.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.561 on 762 degrees of freedom
## Multiple R-squared:  0.7762, Adjusted R-squared:  0.7759 
## F-statistic:  2643 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -52.325  -32.107    1.217   15.131   40.585  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.433e+00  1.222e-03    5262   <2e-16 ***
## x.var       6.496e-03  1.936e-06    3356   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 18135167  on 763  degrees of freedom
## Residual deviance:   557766  on 762  degrees of freedom
## AIC: 565118
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

## SWEDEN  --  2434783 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -277139 -144164  -33152   96107  813498 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -328842.99   14684.35  -22.39   <2e-16 ***
## x.var          2553.62      33.26   76.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 202700 on 762 degrees of freedom
## Multiple R-squared:  0.8855, Adjusted R-squared:  0.8854 
## F-statistic:  5896 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.6818 -0.8085  0.8789  1.2882  1.7259 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.567613   0.146159   51.78   <2e-16 ***
## x.var       0.011414   0.000331   34.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.018 on 762 degrees of freedom
## Multiple R-squared:  0.6094, Adjusted R-squared:  0.6089 
## F-statistic:  1189 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -439.9  -295.7  -188.9   240.6   477.6  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.108e+01  1.604e-04   69059   <2e-16 ***
## x.var       4.729e-03  2.682e-07   17633   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 473746269  on 763  degrees of freedom
## Residual deviance:  65876841  on 762  degrees of freedom
## AIC: 65887334
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SWITZERLAND  --  2730037 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -258398 -167487  -57081   44150 1343580 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -315656.93   19525.82  -16.17   <2e-16 ***
## x.var          2227.90      44.22   50.38   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 269600 on 762 degrees of freedom
## Multiple R-squared:  0.7691, Adjusted R-squared:  0.7688 
## F-statistic:  2538 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8419 -0.8206  0.6591  1.2929  1.7731 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.4613256  0.1481844   50.35   <2e-16 ***
## x.var       0.0111943  0.0003356   33.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.046 on 762 degrees of freedom
## Multiple R-squared:  0.5935, Adjusted R-squared:  0.593 
## F-statistic:  1113 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -346.7  -277.1  -160.7   233.2   392.6  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.067e+01  1.856e-04   57496   <2e-16 ***
## x.var       5.108e-03  3.060e-07   16692   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 429937373  on 763  degrees of freedom
## Residual deviance:  50781068  on 762  degrees of freedom
## AIC: 50791384
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## SYRIA  --  54040 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -5934  -3864  -2311   5131  10752 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.083e+04  3.560e+02  -30.42   <2e-16 ***
## x.var        7.533e+01  8.062e-01   93.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4915 on 762 degrees of freedom
## Multiple R-squared:  0.9197, Adjusted R-squared:  0.9196 
## F-statistic:  8731 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9503 -0.8703  0.1618  1.3975  1.9782 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.1829345  0.1141783   27.88   <2e-16 ***
## x.var       0.0127896  0.0002586   49.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.576 on 762 degrees of freedom
## Multiple R-squared:  0.7625, Adjusted R-squared:  0.7622 
## F-statistic:  2446 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -71.205  -56.140   -4.978   28.187   55.232  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.235e+00  1.023e-03    7074   <2e-16 ***
## x.var       5.175e-03  1.683e-06    3076   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 14305378  on 763  degrees of freedom
## Residual deviance:  1349160  on 762  degrees of freedom
## AIC: 1356623
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TAIWAN*  --  20156 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -7748  -3132   1315   3074   5137 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5165.6903   270.5036  -19.10   <2e-16 ***
## x.var          29.6448     0.6127   48.39   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3735 on 762 degrees of freedom
## Multiple R-squared:  0.7545, Adjusted R-squared:  0.7541 
## F-statistic:  2341 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4777 -0.4802 -0.0413  0.6170  1.1441 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.1540986  0.0533639   77.84   <2e-16 ***
## x.var       0.0083642  0.0001209   69.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7368 on 762 degrees of freedom
## Multiple R-squared:  0.8627, Adjusted R-squared:  0.8626 
## F-statistic:  4789 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -64.695  -31.756  -17.018    1.185   87.635  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 5.367e+00  2.081e-03    2578   <2e-16 ***
## x.var       6.503e-03  3.295e-06    1974   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 7262548  on 763  degrees of freedom
## Residual deviance: 1179691  on 762  degrees of freedom
## AIC: 1186713
## 
## Number of Fisher Scoring iterations: 5
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6711  -0.5932  -0.2444   0.4013   1.0557  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.5411101  0.0466025   97.44   <2e-16 ***
## x.var       0.0079609  0.0001055   75.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4139992)
## 
##     Null deviance: 2111.3  on 763  degrees of freedom
## Residual deviance:  366.6  on 762  degrees of freedom
## AIC: 12899
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TAJIKISTAN  --  17786 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3951.7 -1568.6   166.9  1539.1  3484.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1373.0888   142.5205   9.634   <2e-16 ***
## x.var         25.5940     0.3228  79.290   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1968 on 762 degrees of freedom
## Multiple R-squared:  0.8919, Adjusted R-squared:  0.8918 
## F-statistic:  6287 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2795 -1.2648  0.3601  2.0078  2.7971 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.2790658  0.1698465   25.19   <2e-16 ***
## x.var       0.0100042  0.0003847   26.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.345 on 762 degrees of freedom
## Multiple R-squared:  0.4702, Adjusted R-squared:  0.4695 
## F-statistic: 676.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -99.043  -25.058    6.107   25.909   47.293  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.256e+00  8.998e-04    9175   <2e-16 ***
## x.var       2.420e-03  1.684e-06    1437   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3666532  on 763  degrees of freedom
## Residual deviance: 1426843  on 762  degrees of freedom
## AIC: 1434260
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TANZANIA  --  33620 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13018.9  -6945.0    -13.1   6409.8  14083.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7781.683    573.063  -13.58   <2e-16 ***
## x.var          36.340      1.298   28.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7912 on 762 degrees of freedom
## Multiple R-squared:  0.5071, Adjusted R-squared:  0.5065 
## F-statistic:   784 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3716 -1.1334  0.3204  1.0941  2.3733 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.8543567  0.1035164   27.57   <2e-16 ***
## x.var       0.0095777  0.0002345   40.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.429 on 762 degrees of freedom
## Multiple R-squared:  0.6865, Adjusted R-squared:  0.6861 
## F-statistic:  1669 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -105.275   -31.556    -2.019    25.731   134.633  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.629e+00  3.391e-03   775.2   <2e-16 ***
## x.var       1.072e-02  5.004e-06  2141.8   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 12030410  on 763  degrees of freedom
## Residual deviance:  1697935  on 762  degrees of freedom
## AIC: 1704220
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## THAILAND  --  2794350 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -748847 -477736   18549  462853 1051965 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -665945.07   36685.19  -18.15   <2e-16 ***
## x.var          3152.26      83.09   37.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 506500 on 762 degrees of freedom
## Multiple R-squared:  0.6539, Adjusted R-squared:  0.6534 
## F-statistic:  1439 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2680 -0.5873 -0.0128  0.6347  1.8382 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.8491549  0.0647792   74.86   <2e-16 ***
## x.var       0.0141265  0.0001467   96.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8944 on 762 degrees of freedom
## Multiple R-squared:  0.924,  Adjusted R-squared:  0.9239 
## F-statistic:  9271 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -720.28  -263.88  -115.38   -32.77   621.83  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.471e+00  3.419e-04   21850   <2e-16 ***
## x.var       1.018e-02  5.076e-07   20048   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 936543065  on 763  degrees of freedom
## Residual deviance:  66742064  on 762  degrees of freedom
## AIC: 66751303
## 
## Number of Fisher Scoring iterations: 5
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5641  -0.7719  -0.2499   0.4120   1.4154  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.6336607  0.0548260   102.8   <2e-16 ***
## x.var       0.0129707  0.0001242   104.5   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5729989)
## 
##     Null deviance: 4507.30  on 763  degrees of freedom
## Residual deviance:  528.37  on 762  degrees of freedom
## AIC: 17640
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## TIMOR-LESTE  --  22485 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7269.6 -3373.5   203.6  3741.3  6303.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6336.4221   299.9839  -21.12   <2e-16 ***
## x.var          32.4899     0.6794   47.82   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4142 on 762 degrees of freedom
## Multiple R-squared:  0.7501, Adjusted R-squared:  0.7497 
## F-statistic:  2287 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57004 -0.82712 -0.08726  0.92869  1.73598 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.107274   0.068865   1.558     0.12    
## x.var       0.014668   0.000156  94.045   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9508 on 762 degrees of freedom
## Multiple R-squared:  0.9207, Adjusted R-squared:  0.9206 
## F-statistic:  8844 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -83.38  -39.52  -18.96   12.71   76.42  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.377e+00  2.542e-03    1721   <2e-16 ***
## x.var       8.053e-03  3.894e-06    2068   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8988934  on 763  degrees of freedom
## Residual deviance: 1244028  on 762  degrees of freedom
## AIC: 1249674
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## TOGO  --  36764 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5522.2 -3500.8  -786.8  2859.3  8650.8 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7703.4681   291.5869  -26.42   <2e-16 ***
## x.var          48.2687     0.6604   73.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4026 on 762 degrees of freedom
## Multiple R-squared:  0.8752, Adjusted R-squared:  0.875 
## F-statistic:  5342 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0232 -0.5798  0.4715  0.9789  1.2415 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.5326949  0.0958427   36.86   <2e-16 ***
## x.var       0.0111474  0.0002171   51.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.323 on 762 degrees of freedom
## Multiple R-squared:  0.7758, Adjusted R-squared:  0.7755 
## F-statistic:  2637 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -53.13  -25.40  -11.90   15.09   47.55  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.365e+00  1.433e-03    4442   <2e-16 ***
## x.var       5.775e-03  2.313e-06    2496   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9579731  on 763  degrees of freedom
## Residual deviance:  526075  on 762  degrees of freedom
## AIC: 533350
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TONGA  --  289 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.063  -8.000  -3.044   2.018 275.350 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6.733928   1.802379  -3.736 0.000201 ***
## x.var        0.026680   0.004082   6.536 1.16e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.88 on 762 degrees of freedom
## Multiple R-squared:  0.05308,    Adjusted R-squared:  0.05184 
## F-statistic: 42.72 on 1 and 762 DF,  p-value: 1.158e-10
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5889 -0.3106 -0.0344  0.1059  4.9088 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.3535797  0.0487051   -7.26 9.59e-13 ***
## x.var        0.0014590  0.0001103   13.23  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6725 on 762 degrees of freedom
## Multiple R-squared:  0.1867, Adjusted R-squared:  0.1856 
## F-statistic: 174.9 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -6.297   0.000   0.000   0.000   4.994  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -88.276279   1.808384  -48.81   <2e-16 ***
## x.var         0.123041   0.002391   51.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 19823  on 763  degrees of freedom
## Residual deviance:  1032  on 762  degrees of freedom
## AIC: 1357.3
## 
## Number of Fisher Scoring iterations: 10
## 
## --------------------------------------------------------------------------------

## TRINIDAD AND TOBAGO  --  125210 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25002 -10921  -3266  10188  51918 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -23907.820   1208.742  -19.78   <2e-16 ***
## x.var          127.225      2.738   46.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16690 on 762 degrees of freedom
## Multiple R-squared:  0.7392, Adjusted R-squared:  0.7389 
## F-statistic:  2160 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7365 -0.5306  0.1374  0.6156  2.0373 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.0491288  0.0927526   32.87   <2e-16 ***
## x.var       0.0132186  0.0002101   62.92   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.281 on 762 degrees of freedom
## Multiple R-squared:  0.8386, Adjusted R-squared:  0.8384 
## F-statistic:  3959 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -53.47  -35.11  -12.57   20.80   54.47  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.179e+00  1.169e-03    5287   <2e-16 ***
## x.var       7.426e-03  1.811e-06    4100   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 29487749  on 763  degrees of freedom
## Residual deviance:   783059  on 762  degrees of freedom
## AIC: 790562
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TUNISIA  --  990483 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -112477  -96935     167   81368  205801 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -207114.75    7033.37  -29.45   <2e-16 ***
## x.var          1313.88      15.93   82.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 97110 on 762 degrees of freedom
## Multiple R-squared:  0.8993, Adjusted R-squared:  0.8991 
## F-statistic:  6803 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2389 -0.7520  0.3678  1.2160  2.1708 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.6036610  0.1279976   35.97   <2e-16 ***
## x.var       0.0151242  0.0002899   52.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.767 on 762 degrees of freedom
## Multiple R-squared:  0.7813, Adjusted R-squared:  0.781 
## F-statistic:  2722 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -303.02  -230.56   -64.68   130.81   284.98  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.728e+00  2.704e-04   35972   <2e-16 ***
## x.var       5.692e-03  4.377e-07   13004   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 273936622  on 763  degrees of freedom
## Residual deviance:  30250253  on 762  degrees of freedom
## AIC: 30259517
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## TURKEY  --  13762181 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1969478  -844321  -219848   620832  4175763 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2417969.2    86022.0  -28.11   <2e-16 ***
## x.var          15712.5      194.8   80.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1188000 on 762 degrees of freedom
## Multiple R-squared:  0.8951, Adjusted R-squared:  0.895 
## F-statistic:  6504 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.4353 -0.9194  0.9916  1.6527  2.5445 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.7394812  0.1884553   41.07   <2e-16 ***
## x.var       0.0142012  0.0004268   33.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.602 on 762 degrees of freedom
## Multiple R-squared:  0.5923, Adjusted R-squared:  0.5918 
## F-statistic:  1107 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -808.3  -620.9  -433.5   416.1  1020.3  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.232e+01  7.601e-05  162034   <2e-16 ***
## x.var       5.541e-03  1.236e-07   44831   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3114993113  on 763  degrees of freedom
## Residual deviance:  261518390  on 762  degrees of freedom
## AIC: 261529770
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## US  --  78733498 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -6481470 -3419378 -1766591  2029817 19378597 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.035e+07  3.993e+05  -25.91   <2e-16 ***
## x.var        9.142e+04  9.044e+02  101.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5514000 on 762 degrees of freedom
## Multiple R-squared:  0.9306, Adjusted R-squared:  0.9305 
## F-statistic: 1.022e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.2293  -0.9585   0.8639   1.7450   2.1062 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.090e+01  1.859e-01   58.62   <2e-16 ***
## x.var       1.211e-02  4.211e-04   28.77   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.567 on 762 degrees of freedom
## Multiple R-squared:  0.5206, Adjusted R-squared:   0.52 
## F-statistic: 827.5 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2775.0  -1482.5   -470.8    889.4   2498.2  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.493e+01  2.471e-05  604197   <2e-16 ***
## x.var       4.348e-03  4.194e-08  103678   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1.5443e+10  on 763  degrees of freedom
## Residual deviance: 1.8380e+09  on 762  degrees of freedom
## AIC: 1.838e+09
## 
## Number of Fisher Scoring iterations: 5
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family Family: Gamma Link function: log  : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.0486  -0.6041  -0.1120   0.3591   0.8710  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.373e+01  4.304e-02  319.02   <2e-16 ***
## x.var       6.930e-03  9.748e-05   71.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3531055)
## 
##     Null deviance: 2284.3  on 763  degrees of freedom
## Residual deviance: 1309.1  on 762  degrees of freedom
## AIC: 26511
## 
## Number of Fisher Scoring iterations: 15
## 
## --------------------------------------------------------------------------------

## UGANDA  --  163152 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -32986 -14371     70  15547  35521 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -35751.235   1286.991  -27.78   <2e-16 ***
## x.var          230.180      2.915   78.97   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17770 on 762 degrees of freedom
## Multiple R-squared:  0.8911, Adjusted R-squared:  0.891 
## F-statistic:  6236 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4724 -0.8316  0.3332  1.2523  2.0792 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.6579970  0.1204276   30.38   <2e-16 ***
## x.var       0.0138042  0.0002728   50.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.663 on 762 degrees of freedom
## Multiple R-squared:  0.7707, Adjusted R-squared:  0.7704 
## F-statistic:  2561 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -139.45   -91.39   -21.35    55.00   125.00  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.053e+00  6.347e-04   12686   <2e-16 ***
## x.var       5.597e-03  1.030e-06    5433   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 46396107  on 763  degrees of freedom
## Residual deviance:  4259634  on 762  degrees of freedom
## AIC: 4267760
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UKRAINE  --  5012980 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -505097 -270118  -98707  256746 1211174 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -833141.23   26615.19   -31.3   <2e-16 ***
## x.var          6066.68      60.28   100.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 367500 on 762 degrees of freedom
## Multiple R-squared:   0.93,  Adjusted R-squared:  0.9299 
## F-statistic: 1.013e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.255 -1.183  0.959  1.724  2.297 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 6.6550093  0.1743472   38.17   <2e-16 ***
## x.var       0.0146363  0.0003949   37.07   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.407 on 762 degrees of freedom
## Multiple R-squared:  0.6432, Adjusted R-squared:  0.6428 
## F-statistic:  1374 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -601.2  -531.8  -153.4   369.8   658.3  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.177e+01  1.094e-04  107544   <2e-16 ***
## x.var       4.973e-03  1.813e-07   27424   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1152768668  on 763  degrees of freedom
## Residual deviance:  142724588  on 762  degrees of freedom
## AIC: 142735276
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UNITED ARAB EMIRATES  --  876624 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -109043  -50854    4140   52349  149017 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -150368.48    4808.54  -31.27   <2e-16 ***
## x.var          1351.68      10.89  124.11   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66390 on 762 degrees of freedom
## Multiple R-squared:  0.9529, Adjusted R-squared:  0.9528 
## F-statistic: 1.54e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4333 -0.8355  0.7090  1.3195  1.7317 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.3576941  0.1347218   54.61   <2e-16 ***
## x.var       0.0108042  0.0003051   35.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.86 on 762 degrees of freedom
## Multiple R-squared:  0.622,  Adjusted R-squared:  0.6215 
## F-statistic:  1254 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -353.12  -204.42   -73.22   183.61   277.25  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.075e+01  2.014e-04   53380   <2e-16 ***
## x.var       4.305e-03  3.423e-07   12578   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 234133452  on 763  degrees of freedom
## Residual deviance:  34657229  on 762  degrees of freedom
## AIC: 34667399
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## UNITED KINGDOM  --  18867585 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2289498 -1389520  -620933   891534  7192814 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3023828.5   145061.4  -20.84   <2e-16 ***
## x.var          19239.0      328.5   58.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2003000 on 762 degrees of freedom
## Multiple R-squared:  0.8182, Adjusted R-squared:  0.8179 
## F-statistic:  3429 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.2321 -0.7556  0.7432  1.3797  1.8338 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.1243401  0.1480842   61.62   <2e-16 ***
## x.var       0.0119749  0.0003354   35.70   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.045 on 762 degrees of freedom
## Multiple R-squared:  0.6259, Adjusted R-squared:  0.6254 
## F-statistic:  1275 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -803.3  -497.8  -159.3   144.7  1074.6  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.243e+01  7.042e-05  176452   <2e-16 ***
## x.var       5.673e-03  1.140e-07   49741   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3.772e+09  on 763  degrees of freedom
## Residual deviance: 2.131e+08  on 762  degrees of freedom
## AIC: 213116048
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## URUGUAY  --  827814 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -135523  -69937   -3506   58715  329148 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -150033.20    6688.00  -22.43   <2e-16 ***
## x.var           849.08      15.15   56.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 92340 on 762 degrees of freedom
## Multiple R-squared:  0.8048, Adjusted R-squared:  0.8046 
## F-statistic:  3142 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4796 -0.4824  0.4295  1.0751  1.3705 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.6966208  0.1036840   35.65   <2e-16 ***
## x.var       0.0153517  0.0002348   65.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.432 on 762 degrees of freedom
## Multiple R-squared:  0.8487, Adjusted R-squared:  0.8485 
## F-statistic:  4274 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -291.48  -178.60  -121.75    41.89   388.01  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.620e+00  3.986e-04   21627   <2e-16 ***
## x.var       6.647e-03  6.287e-07   10573   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 205048676  on 763  degrees of freedom
## Residual deviance:  27995416  on 762  degrees of freedom
## AIC: 28004040
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## UZBEKISTAN  --  235740 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -24450 -11171   2358  10379  29647 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29660.534   1053.548  -28.15   <2e-16 ***
## x.var          309.635      2.386  129.76   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14550 on 762 degrees of freedom
## Multiple R-squared:  0.9567, Adjusted R-squared:  0.9566 
## F-statistic: 1.684e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3730 -0.8604  0.4427  1.5256  2.3934 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.7827391  0.1536969   37.62   <2e-16 ***
## x.var       0.0111361  0.0003481   31.99   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.122 on 762 degrees of freedom
## Multiple R-squared:  0.5732, Adjusted R-squared:  0.5727 
## F-statistic:  1023 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -183.85   -80.21    12.80    43.29   114.15  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.503e+00  3.926e-04   24207   <2e-16 ***
## x.var       3.997e-03  6.761e-07    5912   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 49675563  on 763  degrees of freedom
## Residual deviance:  6818344  on 762  degrees of freedom
## AIC: 6827326
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VANUATU  --  15 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7011 -0.7681  0.0017  0.6493  8.5329 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.7087585  0.0744180  -22.96   <2e-16 ***
## x.var        0.0107295  0.0001685   63.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.027 on 762 degrees of freedom
## Multiple R-squared:  0.8417, Adjusted R-squared:  0.8415 
## F-statistic:  4053 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5910 -0.1536  0.0006  0.1923  0.5255 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.4435931  0.0177505  -24.99   <2e-16 ***
## x.var        0.0035311  0.0000402   87.83   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2451 on 762 degrees of freedom
## Multiple R-squared:  0.9101, Adjusted R-squared:   0.91 
## F-statistic:  7715 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1882  -0.7308  -0.3026   0.2171   1.4056  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.0360912  0.0958444  -21.24   <2e-16 ***
## x.var        0.0057603  0.0001548   37.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2395.64  on 763  degrees of freedom
## Residual deviance:  387.04  on 762  degrees of freedom
## AIC: 1828.7
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VENEZUELA  --  512560 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -60161 -25969  -8528  32230  92690 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -93405.55    2719.83  -34.34   <2e-16 ***
## x.var          715.11       6.16  116.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37550 on 762 degrees of freedom
## Multiple R-squared:  0.9465, Adjusted R-squared:  0.9464 
## F-statistic: 1.348e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9435 -1.0973  0.3528  1.5951  2.6182 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.2491859  0.1504938   34.88   <2e-16 ***
## x.var       0.0133531  0.0003408   39.18   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.078 on 762 degrees of freedom
## Multiple R-squared:  0.6682, Adjusted R-squared:  0.6678 
## F-statistic:  1535 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -236.29  -173.55    46.87    80.12   118.01  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.769e+00  3.063e-04   31892   <2e-16 ***
## x.var       4.779e-03  5.111e-07    9349   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 128093572  on 763  degrees of freedom
## Residual deviance:  12870060  on 762  degrees of freedom
## AIC: 12879285
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## VIETNAM  --  2972378 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -636296 -356175  -32066  257651 1819151 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -483999.17   33188.04  -14.58   <2e-16 ***
## x.var          2142.97      75.17   28.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 458200 on 762 degrees of freedom
## Multiple R-squared:  0.5161, Adjusted R-squared:  0.5155 
## F-statistic: 812.8 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8291 -0.9482  0.3587  0.8129  1.5340 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.8136879  0.0743354   37.85   <2e-16 ***
## x.var       0.0154048  0.0001684   91.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 762 degrees of freedom
## Multiple R-squared:  0.9166, Adjusted R-squared:  0.9165 
## F-statistic:  8372 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -393.12  -155.49   -23.37    -3.84   438.90  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 4.400e+00  6.093e-04    7222   <2e-16 ***
## x.var       1.399e-02  8.745e-07   15994   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 730392897  on 763  degrees of freedom
## Residual deviance:  28112867  on 762  degrees of freedom
## AIC: 28120918
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## WEST BANK AND GAZA  --  638172 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -70114 -34007  -2586  25972 128254 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.092e+05  3.189e+03  -34.25   <2e-16 ***
## x.var        8.104e+02  7.222e+00  112.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 44030 on 762 degrees of freedom
## Multiple R-squared:  0.9429, Adjusted R-squared:  0.9429 
## F-statistic: 1.259e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5684 -0.9495  0.1680  1.7026  2.1191 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.9675169  0.1378003   36.05   <2e-16 ***
## x.var       0.0139750  0.0003121   44.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.903 on 762 degrees of freedom
## Multiple R-squared:  0.7246, Adjusted R-squared:  0.7243 
## F-statistic:  2005 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -263.42  -198.29   -62.55   119.98   258.39  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.810e+00  2.948e-04   33276   <2e-16 ***
## x.var       4.897e-03  4.898e-07    9997   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 154334343  on 763  degrees of freedom
## Residual deviance:  21110848  on 762  degrees of freedom
## AIC: 21120055
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## WINTER OLYMPICS 2022  --  510 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -56.69 -34.21 -11.73  10.74 449.54 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29.56536    5.55788  -5.320 1.37e-07 ***
## x.var         0.11783    0.01259   9.361  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 76.74 on 762 degrees of freedom
## Multiple R-squared:  0.1031, Adjusted R-squared:  0.102 
## F-statistic: 87.62 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8783 -0.5308 -0.1833  0.1643  5.3066 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.4553221  0.0790982  -5.756 1.25e-08 ***
## x.var        0.0018219  0.0001791  10.170  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.092 on 762 degrees of freedom
## Multiple R-squared:  0.1195, Adjusted R-squared:  0.1184 
## F-statistic: 103.4 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## --------------------------------------------------------------------------------

## Warning: glm.fit: fitted rates numerically 0 occurred

##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -14.8246   -0.0078    0.0000    0.0000   12.6593  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -5.506e+01  5.600e-01  -98.32   <2e-16 ***
## x.var        8.100e-02  7.444e-04  108.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 77801  on 763  degrees of freedom
## Residual deviance:  3763  on 762  degrees of freedom
## AIC: 4010.3
## 
## Number of Fisher Scoring iterations: 9
## 
## --------------------------------------------------------------------------------

## YEMEN  --  11751 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2500.7  -261.6   275.6   575.8  1847.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1863.7606    72.4940  -25.71   <2e-16 ***
## x.var          16.4197     0.1642  100.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1001 on 762 degrees of freedom
## Multiple R-squared:  0.9292, Adjusted R-squared:  0.9291 
## F-statistic: 1e+04 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8824 -0.9867  0.4385  1.2290  2.4377 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.0499529  0.1250268   24.39   <2e-16 ***
## x.var       0.0105373  0.0002832   37.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.726 on 762 degrees of freedom
## Multiple R-squared:  0.645,  Adjusted R-squared:  0.6446 
## F-statistic:  1385 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -40.71  -18.00    0.66   11.22   31.44  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.302e+00  1.847e-03    3412   <2e-16 ***
## x.var       4.355e-03  3.133e-06    1390   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2767396  on 763  degrees of freedom
## Residual deviance:  320374  on 762  degrees of freedom
## AIC: 327036
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ZAMBIA  --  311888 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -57130 -28528  -3265  26966  66087 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -66504.556   2469.966  -26.93   <2e-16 ***
## x.var          417.270      5.594   74.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34100 on 762 degrees of freedom
## Multiple R-squared:  0.8795, Adjusted R-squared:  0.8794 
## F-statistic:  5564 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8644 -1.1570  0.4612  1.3426  2.2074 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.0749754  0.1294591   31.48   <2e-16 ***
## x.var       0.0140965  0.0002932   48.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.787 on 762 degrees of freedom
## Multiple R-squared:  0.7521, Adjusted R-squared:  0.7517 
## F-statistic:  2311 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -165.97  -110.06   -60.42    65.81   188.14  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 8.529e+00  4.865e-04   17531   <2e-16 ***
## x.var       5.766e-03  7.857e-07    7338   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 86001406  on 763  degrees of freedom
## Residual deviance:  7832249  on 762  degrees of freedom
## AIC: 7840784
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

## ZIMBABWE  --  234589 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53047 -26270  -1357  17575  72781 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -50359.193   2373.631  -21.22   <2e-16 ***
## x.var          281.176      5.376   52.30   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32770 on 762 degrees of freedom
## Multiple R-squared:  0.7821, Adjusted R-squared:  0.7818 
## F-statistic:  2736 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0569 -1.2107  0.1718  1.4034  2.3486 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.2083176  0.1188354   27.00   <2e-16 ***
## x.var       0.0146310  0.0002691   54.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.641 on 762 degrees of freedom
## Multiple R-squared:  0.795,  Adjusted R-squared:  0.7947 
## F-statistic:  2955 on 1 and 762 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -120.04   -62.73   -26.20    16.95   133.12  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.411e+00  7.102e-04   10434   <2e-16 ***
## x.var       6.797e-03  1.116e-06    6089   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 62321394  on 763  degrees of freedom
## Residual deviance:  2734644  on 762  degrees of freedom
## AIC: 2742668
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

# read time series data for confirmed cases
TS.data <- covid19.data("ts-confirmed")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:30:58 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

# compute changes and growth rates per location for all the countries
growth.rate(TS.data,"Libya")

## Processing...  LIBYA

## Loading required package: pheatmap

## Warning: package 'pheatmap' was built under R version 4.1.1

## Loading required package: gplots

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

## $Changes
##   geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1   LIBYA          0          0          0          0          0          0
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1          0          0          0          0          0          0          0
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1          0          0          0          0          0          0          0
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1          0          0          0          0          0          0          0
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1          0          0          0          0          0          0          0
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1          0          0          0          0          0          0          0
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1          0          0          0          0          0          0          0
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1          0          0          0          0          0          0          0
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1          0          0          0          0          0          0          1
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1          0          0          0          2          5          0          2
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1          0          1          0          7          0          1          1
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1          1          3          0          0          1          1          9
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1         13          1          0          0          2          0          0
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1          8          1          1          0          0          0          0
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1          0          0          2          0          0          0          0
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1          1          0          0          0          0          0          0
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1          0          0          0          1          0          0          3
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1          1          2          1          3          0          0          2
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1         22          6         13         12         26         12         14
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1         14         13         30         17          0         76         27
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1         19         15         16          9         36         13         17
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1         16         10         10         24         27         24         44
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1         31         28         15         14         35         40         22
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1         50         17         27         71         57         71         65
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1         86         74          0         47         44         79         51
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1         26         63         52         87         75        114        108
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1         88        138        110        123        122        158        190
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1        205        216        183         70        146        226        161
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1        251        404        200        153        219        478        373
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1        309        439        277        411        434        407        489
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1        395        244        414        316          0        572        272
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1        553        440        355        329        465        543        658
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1        532        617        672        649        655       1085        749
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1        879        477        969        440        433        734        629
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1        792        886        616        796        715        847        650
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1        651        535        658        538        536        849        801
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1        511        683        509        370        722        628       1031
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1       1045        779       1076        318       1026       1109       1164
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1        836        855       1169          0        945       1159        957
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1        719        995        764        990       1639       1210        752
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1        899        782        972        467        950        862        781
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1        899        853       1004        595       1078        923        970
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1        875        919        824          0        974        722        612
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1        529        541        802          0       1015        650        707
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1        617        610        866          0       1157        379        608
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1        670        762        680          0       1051        517        889
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1        536        661        697          0        899        578        660
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1        560        706        489          0        788        640        506
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1        640        846        461          0        728        532        437
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1        585        342        467          0        670        561        481
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1        424        635        487          0        743        633        652
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1        640        764        583          0       1071        781        596
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1        659        622        794          0       1148        741        870
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1        765        715        871          0        981        771       1032
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1        809        770        881          0       1132        856        679
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
## 1        467        333        520          0        473        351        331
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1        312        392        585          0        472        415        489
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1        561        571        625          0        880        789        840
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1        618       1002        895          0       1158       1018       1030
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1        910       1073        972          0       1350       1087       1041
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1       1054       1032       1134          0       1264        905        901
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1        909        912        884          0        733        696        706
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1        706       1023       1108          0       1206       1148        876
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1        969        869        732          0        937        851        828
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1        541        513        573          0        749        584        625
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1        594        533        536          0        534        467        501
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1        447        371        436          0        363        464        337
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1        255        266        504          0        273        256        466
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1        253        234          0          0        231        304        298
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1        338        299        250          0        412        281        340
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1        219        321        343          0        366        595        296
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1        251        244        386          0        328        404        243
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1        229        229        376          0        297        225        271
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1        333        258        280          0        322        290        215
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1        223        184        469          0        341        316        452
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1        236        431        418          0        719        782       1070
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1       1248       1384       1710          0       2854       2679       2640
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1       2604       2555       2866          0       4061       3425       1781
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1          0        732       2171          0       3845       3512       3348
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1       3161       2730       2914          0       4322       2892       2139
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1       2484       1997       1879          0       3019       2001       2134
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1       2472       2286       2360          0       2831       2688       2276
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1       2325       1949       2364          0       1722       1625       1894
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1       1682       1722       1613          0       2003       1678       1501
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1       1665       1479       1388          0       1914       1379       1272
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1       1499       1802       1117          0       1443       1291       1149
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1       1433       1053        968          0       1121       1081       1038
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1        985       1006        936          0        989        910        686
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1        693        815       1007          0        748        719        682
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1        692        915        604          0        725        637        551
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1        724        559        563          0        780        638        596
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1        532        436        689          0        745        589        651
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1        624        596        569          0        683        626        499
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1        556        648        599          0        648        795        609
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1        597        568        593          0        599        562        593
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1        408        551        429          0        593        581        595
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1        468        732        600          0        784        638        427
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1        574        529        541          0        709        479        401
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1        509        495        577          0        655        711        512
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1        488        648        559          0        726        592        543
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1        561        560        658          0        735        881        599
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1        665        640        551          0        916        634        651
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1        698        643        592          0        579        536        487
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1        599        618        765          0        867        736        885
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1       1173       1331       1700          0       2281       2333       3063
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1       2245       3157       3320          0       5694       4429       4266
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1       4371       3656       3917          0       4242       2832       3326
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1       3272       3773       3345          0       3648       2800       2490
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1       2884       2457       1208          0       2292       2307       1815
##   2022-02-23
## 1       1373
## 
## $Growth.Rate
##   geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1   LIBYA        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1        NaN        NaN        NaN        NaN        NaN         NA          0
##   2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1        NaN        NaN         NA        2.5          0         NA          0
##   2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1         NA          0         NA          0         NA          1          1
##   2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1          3          0        NaN         NA          1          9   1.444444
##   2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1 0.07692308          0        NaN         NA          0        NaN         NA
##   2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1      0.125          1          0        NaN        NaN        NaN        NaN
##   2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1        NaN         NA          0        NaN        NaN        NaN         NA
##   2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1          0        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1        NaN        NaN         NA          0        NaN         NA  0.3333333
##   2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1          2        0.5          3          0        NaN         NA         11
##   2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1  0.2727273   2.166667  0.9230769   2.166667  0.4615385   1.166667          1
##   2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1  0.9285714   2.307692  0.5666667          0         NA  0.3552632  0.7037037
##   2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1  0.7894737   1.066667     0.5625          4  0.3611111   1.307692  0.9411765
##   2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1      0.625          1        2.4      1.125  0.8888889   1.833333  0.7045455
##   2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1  0.9032258  0.5357143  0.9333333        2.5   1.142857       0.55   2.272727
##   2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1       0.34   1.588235    2.62963  0.8028169   1.245614   0.915493   1.323077
##   2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1  0.8604651          0         NA  0.9361702   1.795455  0.6455696  0.5098039
##   2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1   2.423077  0.8253968   1.673077   0.862069       1.52  0.9473684  0.8148148
##   2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1   1.568182  0.7971014   1.118182  0.9918699   1.295082   1.202532   1.078947
##   2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1   1.053659  0.8472222  0.3825137   2.085714   1.547945  0.7123894   1.559006
##   2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1   1.609562  0.4950495      0.765   1.431373   2.182648  0.7803347  0.8284182
##   2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1   1.420712  0.6309795   1.483755   1.055961   0.937788   1.201474   0.807771
##   2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1  0.6177215   1.696721   0.763285          0         NA  0.4755245   2.033088
##   2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02
## 1    0.79566  0.8068182  0.9267606   1.413374   1.167742   1.211786  0.8085106
##   2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09
## 1   1.159774   1.089141  0.9657738   1.009245   1.656489  0.6903226   1.173565
##   2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16
## 1  0.5426621   2.031447  0.4540764  0.9840909    1.69515  0.8569482   1.259141
##   2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23
## 1   1.118687  0.6952596   1.292208  0.8982412   1.184615  0.7674144   1.001538
##   2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30
## 1  0.8218126   1.229907  0.8176292  0.9962825   1.583955  0.9434629  0.6379526
##   2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07
## 1   1.336595  0.7452416  0.7269155   1.951351  0.8698061    1.64172   1.013579
##   2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14
## 1  0.7454545   1.381258   0.295539   3.226415   1.080897   1.049594  0.7182131
##   2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21
## 1   1.022727   1.367251          0         NA   1.226455  0.8257118  0.7513062
##   2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28
## 1   1.383866  0.7678392   1.295812   1.655556   0.738255  0.6214876   1.195479
##   2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04
## 1  0.8698554   1.242967  0.4804527   2.034261  0.9073684  0.9060325   1.151088
##   2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11
## 1   0.948832   1.177022  0.5926295   1.811765  0.8562152   1.050921  0.9020619
##   2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18
## 1   1.050286  0.8966268          0         NA  0.7412731  0.8476454  0.8643791
##   2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25
## 1   1.022684    1.48244          0         NA  0.6403941   1.087692  0.8727016
##   2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02
## 1  0.9886548   1.419672          0         NA  0.3275713   1.604222   1.101974
##   2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09
## 1   1.137313  0.8923885          0         NA  0.4919125   1.719536  0.6029246
##   2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16
## 1   1.233209   1.054463          0         NA  0.6429366   1.141869  0.8484848
##   2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23
## 1   1.260714  0.6926346          0         NA  0.8121827   0.790625   1.264822
##   2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30
## 1   1.321875  0.5449173          0         NA  0.7307692  0.8214286   1.338673
##   2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06
## 1  0.5846154   1.365497          0         NA  0.8373134  0.8573975  0.8814969
##   2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13
## 1   1.497642  0.7669291          0         NA  0.8519515   1.030016  0.9815951
##   2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20
## 1    1.19375   0.763089          0         NA   0.729225  0.7631242   1.105705
##   2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27
## 1  0.9438543   1.276527          0         NA  0.6454704   1.174089  0.8793103
##   2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03
## 1  0.9346405   1.218182          0         NA  0.7859327   1.338521  0.7839147
##   2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10
## 1  0.9517923   1.144156          0         NA  0.7561837  0.7932243  0.6877761
##   2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16 2021-02-17
## 1  0.7130621   1.561562          0         NA  0.7420719  0.9430199  0.9425982
##   2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24
## 1    1.25641   1.492347          0         NA  0.8792373   1.178313   1.147239
##   2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03
## 1   1.017825   1.094571          0         NA  0.8965909   1.064639  0.7357143
##   2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10
## 1   1.621359  0.8932136          0         NA  0.8791019   1.011788  0.8834951
##   2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17
## 1   1.179121  0.9058714          0         NA  0.8051852  0.9576817   1.012488
##   2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24
## 1  0.9791271   1.098837          0         NA   0.715981  0.9955801   1.008879
##   2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31
## 1     1.0033  0.9692982          0         NA  0.9495225   1.014368          1
##   2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07
## 1   1.449008   1.083089          0         NA  0.9519071  0.7630662   1.106164
##   2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14
## 1  0.8968008  0.8423475          0         NA  0.9082177   0.972973  0.6533816
##   2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21
## 1   0.948244   1.116959          0         NA  0.7797063   1.070205     0.9504
##   2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28
## 1  0.8973064   1.005629          0         NA  0.8745318   1.072805  0.8922156
##   2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05
## 1  0.8299776   1.175202          0         NA   1.278237  0.7262931  0.7566766
##   2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12
## 1   1.043137   1.894737          0         NA  0.9377289   1.820312  0.5429185
##   2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19
## 1  0.9249012          0        NaN         NA   1.316017  0.9802632   1.134228
##   2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26
## 1  0.8846154  0.8361204          0         NA  0.6820388   1.209964  0.6441176
##   2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02
## 1   1.465753   1.068536          0         NA   1.625683   0.497479   0.847973
##   2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09
## 1  0.9721116   1.581967          0         NA   1.231707  0.6014851  0.9423868
##   2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16
## 1          1   1.641921          0         NA  0.7575758   1.204444   1.228782
##   2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23
## 1  0.7747748   1.085271          0         NA  0.9006211  0.7413793   1.037209
##   2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30
## 1  0.8251121   2.548913          0         NA  0.9266862    1.43038  0.5221239
##   2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07
## 1   1.826271  0.9698376          0         NA   1.087622   1.368286   1.166355
##   2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14
## 1   1.108974   1.235549          0         NA  0.9386826  0.9854423  0.9863636
##   2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21
## 1  0.9811828   1.121722          0         NA  0.8433883       0.52          0
##   2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28
## 1         NA   2.965847          0         NA   0.913394   0.953303  0.9441458
##   2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04
## 1  0.8636507   1.067399          0         NA  0.6691347  0.7396266    1.16129
##   2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
## 1  0.8039452  0.9409114          0         NA  0.6628023   1.066467   1.158388
##   2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18
## 1  0.9247573   1.032371          0         NA  0.9494878  0.8467262   1.021529
##   2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25
## 1  0.8382796    1.21293          0         NA  0.9436702   1.165538  0.8880676
##   2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01
## 1   1.023781  0.9367015          0         NA  0.8377434  0.8945173    1.10926
##   2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08
## 1  0.8882883  0.9384719          0         NA  0.7204807  0.9224075   1.178459
##   2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15
## 1   1.202135  0.6198668          0         NA  0.8946639  0.8900077   1.247171
##   2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22
## 1  0.7348221  0.9192783          0         NA  0.9643176   0.960222  0.9489403
##   2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29
## 1    1.02132  0.9304175          0         NA  0.9201213  0.7538462   1.010204
##   2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06
## 1   1.176046   1.235583          0         NA  0.9612299  0.9485396   1.014663
##   2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13
## 1   1.322254  0.6601093          0         NA  0.8786207  0.8649922   1.313975
##   2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20
## 1  0.7720994   1.007156          0         NA  0.8179487  0.9341693  0.8926174
##   2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27
## 1  0.8195489   1.580275          0         NA   0.790604   1.105263  0.9585253
##   2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03
## 1  0.9551282   0.954698          0         NA  0.9165447  0.7971246   1.114228
##   2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10
## 1   1.165468  0.9243827          0         NA   1.226852  0.7660377  0.9802956
##   2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17
## 1  0.9514238   1.044014          0         NA  0.9382304    1.05516   0.688027
##   2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24
## 1    1.35049  0.7785844          0         NA  0.9797639   1.024096  0.7865546
##   2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01
## 1   1.564103  0.8196721          0         NA  0.8137755   0.669279   1.344262
##   2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08
## 1  0.9216028   1.022684          0         NA  0.6755994  0.8371608   1.269327
##   2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15
## 1  0.9724951   1.165657          0         NA   1.085496  0.7201125   0.953125
##   2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22
## 1   1.327869  0.8626543          0         NA   0.815427  0.9172297   1.033149
##   2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29
## 1  0.9982175      1.175          0         NA   1.198639  0.6799092   1.110184
##   2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05
## 1   0.962406  0.8609375          0         NA  0.6921397   1.026814   1.072197
##   2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12
## 1  0.9212034  0.9206843          0         NA   0.925734  0.9085821   1.229979
##   2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19
## 1    1.03172   1.237864          0         NA  0.8489043   1.202446   1.325424
##   2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26
## 1   1.134697   1.277235          0         NA   1.022797   1.312902  0.7329416
##   2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02
## 1   1.406236   1.051631          0         NA  0.7778363  0.9631971   1.024613
##   2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09
## 1  0.8364219   1.071389          0         NA  0.6676096   1.174435  0.9837643
##   2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16
## 1   1.153117  0.8865624          0         NA  0.7675439  0.8892857   1.158233
##   2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22 2022-02-23
## 1  0.8519417  0.4916565          0         NA   1.006545   0.786736  0.7564738
##   NA
## 1 NA

# compute changes and growth rates per location for 'Italy'
growth.rate(TS.data,geo.loc="Libya")

## Processing...  LIBYA

## $Changes
##   geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1   LIBYA          0          0          0          0          0          0
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1          0          0          0          0          0          0          0
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1          0          0          0          0          0          0          0
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1          0          0          0          0          0          0          0
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1          0          0          0          0          0          0          0
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1          0          0          0          0          0          0          0
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1          0          0          0          0          0          0          0
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1          0          0          0          0          0          0          0
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1          0          0          0          0          0          0          1
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1          0          0          0          2          5          0          2
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1          0          1          0          7          0          1          1
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1          1          3          0          0          1          1          9
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1         13          1          0          0          2          0          0
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1          8          1          1          0          0          0          0
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1          0          0          2          0          0          0          0
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1          1          0          0          0          0          0          0
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1          0          0          0          1          0          0          3
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1          1          2          1          3          0          0          2
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1         22          6         13         12         26         12         14
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1         14         13         30         17          0         76         27
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1         19         15         16          9         36         13         17
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1         16         10         10         24         27         24         44
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1         31         28         15         14         35         40         22
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1         50         17         27         71         57         71         65
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1         86         74          0         47         44         79         51
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1         26         63         52         87         75        114        108
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1         88        138        110        123        122        158        190
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1        205        216        183         70        146        226        161
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1        251        404        200        153        219        478        373
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1        309        439        277        411        434        407        489
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1        395        244        414        316          0        572        272
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1        553        440        355        329        465        543        658
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1        532        617        672        649        655       1085        749
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1        879        477        969        440        433        734        629
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1        792        886        616        796        715        847        650
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1        651        535        658        538        536        849        801
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1        511        683        509        370        722        628       1031
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1       1045        779       1076        318       1026       1109       1164
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1        836        855       1169          0        945       1159        957
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1        719        995        764        990       1639       1210        752
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1        899        782        972        467        950        862        781
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1        899        853       1004        595       1078        923        970
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1        875        919        824          0        974        722        612
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1        529        541        802          0       1015        650        707
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1        617        610        866          0       1157        379        608
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1        670        762        680          0       1051        517        889
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1        536        661        697          0        899        578        660
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1        560        706        489          0        788        640        506
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1        640        846        461          0        728        532        437
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1        585        342        467          0        670        561        481
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1        424        635        487          0        743        633        652
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1        640        764        583          0       1071        781        596
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1        659        622        794          0       1148        741        870
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1        765        715        871          0        981        771       1032
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1        809        770        881          0       1132        856        679
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
## 1        467        333        520          0        473        351        331
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1        312        392        585          0        472        415        489
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1        561        571        625          0        880        789        840
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1        618       1002        895          0       1158       1018       1030
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1        910       1073        972          0       1350       1087       1041
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1       1054       1032       1134          0       1264        905        901
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1        909        912        884          0        733        696        706
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1        706       1023       1108          0       1206       1148        876
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1        969        869        732          0        937        851        828
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1        541        513        573          0        749        584        625
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1        594        533        536          0        534        467        501
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1        447        371        436          0        363        464        337
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1        255        266        504          0        273        256        466
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1        253        234          0          0        231        304        298
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1        338        299        250          0        412        281        340
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1        219        321        343          0        366        595        296
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1        251        244        386          0        328        404        243
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1        229        229        376          0        297        225        271
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1        333        258        280          0        322        290        215
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1        223        184        469          0        341        316        452
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1        236        431        418          0        719        782       1070
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1       1248       1384       1710          0       2854       2679       2640
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1       2604       2555       2866          0       4061       3425       1781
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1          0        732       2171          0       3845       3512       3348
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1       3161       2730       2914          0       4322       2892       2139
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1       2484       1997       1879          0       3019       2001       2134
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1       2472       2286       2360          0       2831       2688       2276
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1       2325       1949       2364          0       1722       1625       1894
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1       1682       1722       1613          0       2003       1678       1501
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1       1665       1479       1388          0       1914       1379       1272
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1       1499       1802       1117          0       1443       1291       1149
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1       1433       1053        968          0       1121       1081       1038
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1        985       1006        936          0        989        910        686
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1        693        815       1007          0        748        719        682
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1        692        915        604          0        725        637        551
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1        724        559        563          0        780        638        596
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1        532        436        689          0        745        589        651
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1        624        596        569          0        683        626        499
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1        556        648        599          0        648        795        609
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1        597        568        593          0        599        562        593
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1        408        551        429          0        593        581        595
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1        468        732        600          0        784        638        427
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1        574        529        541          0        709        479        401
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1        509        495        577          0        655        711        512
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1        488        648        559          0        726        592        543
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1        561        560        658          0        735        881        599
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1        665        640        551          0        916        634        651
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1        698        643        592          0        579        536        487
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1        599        618        765          0        867        736        885
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1       1173       1331       1700          0       2281       2333       3063
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1       2245       3157       3320          0       5694       4429       4266
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1       4371       3656       3917          0       4242       2832       3326
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1       3272       3773       3345          0       3648       2800       2490
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1       2884       2457       1208          0       2292       2307       1815
##   2022-02-23
## 1       1373
## 
## $Growth.Rate
##   geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1   LIBYA        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1        NaN        NaN        NaN        NaN        NaN         NA          0
##   2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1        NaN        NaN         NA        2.5          0         NA          0
##   2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1         NA          0         NA          0         NA          1          1
##   2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1          3          0        NaN         NA          1          9   1.444444
##   2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1 0.07692308          0        NaN         NA          0        NaN         NA
##   2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1      0.125          1          0        NaN        NaN        NaN        NaN
##   2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1        NaN         NA          0        NaN        NaN        NaN         NA
##   2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1          0        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1        NaN        NaN         NA          0        NaN         NA  0.3333333
##   2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1          2        0.5          3          0        NaN         NA         11
##   2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1  0.2727273   2.166667  0.9230769   2.166667  0.4615385   1.166667          1
##   2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1  0.9285714   2.307692  0.5666667          0         NA  0.3552632  0.7037037
##   2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1  0.7894737   1.066667     0.5625          4  0.3611111   1.307692  0.9411765
##   2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1      0.625          1        2.4      1.125  0.8888889   1.833333  0.7045455
##   2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1  0.9032258  0.5357143  0.9333333        2.5   1.142857       0.55   2.272727
##   2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1       0.34   1.588235    2.62963  0.8028169   1.245614   0.915493   1.323077
##   2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1  0.8604651          0         NA  0.9361702   1.795455  0.6455696  0.5098039
##   2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1   2.423077  0.8253968   1.673077   0.862069       1.52  0.9473684  0.8148148
##   2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1   1.568182  0.7971014   1.118182  0.9918699   1.295082   1.202532   1.078947
##   2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1   1.053659  0.8472222  0.3825137   2.085714   1.547945  0.7123894   1.559006
##   2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1   1.609562  0.4950495      0.765   1.431373   2.182648  0.7803347  0.8284182
##   2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1   1.420712  0.6309795   1.483755   1.055961   0.937788   1.201474   0.807771
##   2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1  0.6177215   1.696721   0.763285          0         NA  0.4755245   2.033088
##   2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02
## 1    0.79566  0.8068182  0.9267606   1.413374   1.167742   1.211786  0.8085106
##   2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09
## 1   1.159774   1.089141  0.9657738   1.009245   1.656489  0.6903226   1.173565
##   2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16
## 1  0.5426621   2.031447  0.4540764  0.9840909    1.69515  0.8569482   1.259141
##   2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23
## 1   1.118687  0.6952596   1.292208  0.8982412   1.184615  0.7674144   1.001538
##   2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30
## 1  0.8218126   1.229907  0.8176292  0.9962825   1.583955  0.9434629  0.6379526
##   2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07
## 1   1.336595  0.7452416  0.7269155   1.951351  0.8698061    1.64172   1.013579
##   2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14
## 1  0.7454545   1.381258   0.295539   3.226415   1.080897   1.049594  0.7182131
##   2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21
## 1   1.022727   1.367251          0         NA   1.226455  0.8257118  0.7513062
##   2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28
## 1   1.383866  0.7678392   1.295812   1.655556   0.738255  0.6214876   1.195479
##   2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04
## 1  0.8698554   1.242967  0.4804527   2.034261  0.9073684  0.9060325   1.151088
##   2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11
## 1   0.948832   1.177022  0.5926295   1.811765  0.8562152   1.050921  0.9020619
##   2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18
## 1   1.050286  0.8966268          0         NA  0.7412731  0.8476454  0.8643791
##   2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25
## 1   1.022684    1.48244          0         NA  0.6403941   1.087692  0.8727016
##   2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02
## 1  0.9886548   1.419672          0         NA  0.3275713   1.604222   1.101974
##   2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09
## 1   1.137313  0.8923885          0         NA  0.4919125   1.719536  0.6029246
##   2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16
## 1   1.233209   1.054463          0         NA  0.6429366   1.141869  0.8484848
##   2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23
## 1   1.260714  0.6926346          0         NA  0.8121827   0.790625   1.264822
##   2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30
## 1   1.321875  0.5449173          0         NA  0.7307692  0.8214286   1.338673
##   2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06
## 1  0.5846154   1.365497          0         NA  0.8373134  0.8573975  0.8814969
##   2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13
## 1   1.497642  0.7669291          0         NA  0.8519515   1.030016  0.9815951
##   2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20
## 1    1.19375   0.763089          0         NA   0.729225  0.7631242   1.105705
##   2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27
## 1  0.9438543   1.276527          0         NA  0.6454704   1.174089  0.8793103
##   2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03
## 1  0.9346405   1.218182          0         NA  0.7859327   1.338521  0.7839147
##   2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10
## 1  0.9517923   1.144156          0         NA  0.7561837  0.7932243  0.6877761
##   2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16 2021-02-17
## 1  0.7130621   1.561562          0         NA  0.7420719  0.9430199  0.9425982
##   2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24
## 1    1.25641   1.492347          0         NA  0.8792373   1.178313   1.147239
##   2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03
## 1   1.017825   1.094571          0         NA  0.8965909   1.064639  0.7357143
##   2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10
## 1   1.621359  0.8932136          0         NA  0.8791019   1.011788  0.8834951
##   2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17
## 1   1.179121  0.9058714          0         NA  0.8051852  0.9576817   1.012488
##   2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24
## 1  0.9791271   1.098837          0         NA   0.715981  0.9955801   1.008879
##   2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31
## 1     1.0033  0.9692982          0         NA  0.9495225   1.014368          1
##   2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07
## 1   1.449008   1.083089          0         NA  0.9519071  0.7630662   1.106164
##   2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14
## 1  0.8968008  0.8423475          0         NA  0.9082177   0.972973  0.6533816
##   2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21
## 1   0.948244   1.116959          0         NA  0.7797063   1.070205     0.9504
##   2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28
## 1  0.8973064   1.005629          0         NA  0.8745318   1.072805  0.8922156
##   2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05
## 1  0.8299776   1.175202          0         NA   1.278237  0.7262931  0.7566766
##   2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12
## 1   1.043137   1.894737          0         NA  0.9377289   1.820312  0.5429185
##   2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19
## 1  0.9249012          0        NaN         NA   1.316017  0.9802632   1.134228
##   2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26
## 1  0.8846154  0.8361204          0         NA  0.6820388   1.209964  0.6441176
##   2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02
## 1   1.465753   1.068536          0         NA   1.625683   0.497479   0.847973
##   2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09
## 1  0.9721116   1.581967          0         NA   1.231707  0.6014851  0.9423868
##   2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16
## 1          1   1.641921          0         NA  0.7575758   1.204444   1.228782
##   2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23
## 1  0.7747748   1.085271          0         NA  0.9006211  0.7413793   1.037209
##   2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30
## 1  0.8251121   2.548913          0         NA  0.9266862    1.43038  0.5221239
##   2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07
## 1   1.826271  0.9698376          0         NA   1.087622   1.368286   1.166355
##   2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14
## 1   1.108974   1.235549          0         NA  0.9386826  0.9854423  0.9863636
##   2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21
## 1  0.9811828   1.121722          0         NA  0.8433883       0.52          0
##   2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28
## 1         NA   2.965847          0         NA   0.913394   0.953303  0.9441458
##   2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04
## 1  0.8636507   1.067399          0         NA  0.6691347  0.7396266    1.16129
##   2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
## 1  0.8039452  0.9409114          0         NA  0.6628023   1.066467   1.158388
##   2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18
## 1  0.9247573   1.032371          0         NA  0.9494878  0.8467262   1.021529
##   2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25
## 1  0.8382796    1.21293          0         NA  0.9436702   1.165538  0.8880676
##   2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01
## 1   1.023781  0.9367015          0         NA  0.8377434  0.8945173    1.10926
##   2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08
## 1  0.8882883  0.9384719          0         NA  0.7204807  0.9224075   1.178459
##   2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15
## 1   1.202135  0.6198668          0         NA  0.8946639  0.8900077   1.247171
##   2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22
## 1  0.7348221  0.9192783          0         NA  0.9643176   0.960222  0.9489403
##   2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29
## 1    1.02132  0.9304175          0         NA  0.9201213  0.7538462   1.010204
##   2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06
## 1   1.176046   1.235583          0         NA  0.9612299  0.9485396   1.014663
##   2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13
## 1   1.322254  0.6601093          0         NA  0.8786207  0.8649922   1.313975
##   2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20
## 1  0.7720994   1.007156          0         NA  0.8179487  0.9341693  0.8926174
##   2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27
## 1  0.8195489   1.580275          0         NA   0.790604   1.105263  0.9585253
##   2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03
## 1  0.9551282   0.954698          0         NA  0.9165447  0.7971246   1.114228
##   2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10
## 1   1.165468  0.9243827          0         NA   1.226852  0.7660377  0.9802956
##   2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17
## 1  0.9514238   1.044014          0         NA  0.9382304    1.05516   0.688027
##   2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24
## 1    1.35049  0.7785844          0         NA  0.9797639   1.024096  0.7865546
##   2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01
## 1   1.564103  0.8196721          0         NA  0.8137755   0.669279   1.344262
##   2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08
## 1  0.9216028   1.022684          0         NA  0.6755994  0.8371608   1.269327
##   2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15
## 1  0.9724951   1.165657          0         NA   1.085496  0.7201125   0.953125
##   2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22
## 1   1.327869  0.8626543          0         NA   0.815427  0.9172297   1.033149
##   2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29
## 1  0.9982175      1.175          0         NA   1.198639  0.6799092   1.110184
##   2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05
## 1   0.962406  0.8609375          0         NA  0.6921397   1.026814   1.072197
##   2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12
## 1  0.9212034  0.9206843          0         NA   0.925734  0.9085821   1.229979
##   2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19
## 1    1.03172   1.237864          0         NA  0.8489043   1.202446   1.325424
##   2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26
## 1   1.134697   1.277235          0         NA   1.022797   1.312902  0.7329416
##   2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02
## 1   1.406236   1.051631          0         NA  0.7778363  0.9631971   1.024613
##   2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09
## 1  0.8364219   1.071389          0         NA  0.6676096   1.174435  0.9837643
##   2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16
## 1   1.153117  0.8865624          0         NA  0.7675439  0.8892857   1.158233
##   2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22 2022-02-23
## 1  0.8519417  0.4916565          0         NA   1.006545   0.786736  0.7564738
##   NA
## 1 NA

# compute changes and growth rates per location for 'Italy' and 'Germany'
growth.rate(TS.data,geo.loc=c("Saudi Arabia","Egypt"))

## Processing...  SAUDI ARABIA

## Processing...  EGYPT

## $Changes
##        geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27
## 1 SAUDI ARABIA          0          0          0          0          0
## 2        EGYPT          0          0          0          0          0
##   2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17
## 1          0          0          0          0          0          0          0
## 2          0          0          0          1          0          0          0
##   2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02
## 1          0          0          0          0          0          0          1
## 2          0          0          0          0          0          1          0
##   2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
## 1          0          0          4          0          0          6          4
## 2          0          0          1         12          0         34          6
##   2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16
## 1          5          1         24         41         17          0         15
## 2          4          1          7         13         29          1         40
##   2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23
## 1         53          0        103         70         48        119         51
## 2         46          0         60         29          9         33         39
##   2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30
## 1        205        133        112         92         99         96        154
## 2         36         54         39         41         40         33         47
##   2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
## 1        110        157        165        154        140        223        203
## 2         54         69         86        120         85        103        149
##   2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13
## 1        190        137        355        364        382        429        472
## 2        128        110        139         95        145        126        125
##   2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20
## 1        435        493        518        762       1132       1088       1122
## 2        160        155        168        171        188        112        189
##   2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27
## 1       1147       1141       1158       1172       1197       1223       1289
## 2        157        169        232        201        227        215        248
##   2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04
## 1       1266       1325       1351       1344       1362       1552       1645
## 2        260        226        269        358        298        272        348
##   2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11
## 1       1595       1687       1793       1701       1704       1912       1966
## 2        388        387        393        495        488        436        346
##   2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18
## 1       1911       1905       2039       2307       2840       2736       2593
## 2        347        338        398        399        491        510        535
##   2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25
## 1       2509       2691       2532       2642       2442       2399       2235
## 2        720        745        774        783        727        752        702
##   2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01
## 1       1931       1815       1644       1581       1618       1877       1881
## 2        789        910       1127       1289       1367       1536       1399
##   2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08
## 1       1869       2171       1975       2591       3121       3045       3369
## 2       1152       1079       1152       1348       1497       1467       1365
##   2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15
## 1       3288       3717       3733       3921       3366       4233       4507
## 2       1385       1455       1442       1577       1677       1618       1691
##   2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22
## 1       4267       4919       4757       4301       3941       3379       3393
## 2       1567       1363       1218       1774       1547       1475       1576
##   2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29
## 1       3139       3123       3372       3938       3927       3989       3943
## 2       1332       1420       1569       1625       1168       1265       1566
##   2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06
## 1       4387       3402       3383       4193       4128       3580       4207
## 2       1557       1503       1485       1412       1324       1218        969
##   2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13
## 1       3392       3036       3183       3159       2994       2779       2852
## 2       1057       1025        950        981        923        912        931
##   2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20
## 1       2692       2671       2764       2613       2565       2504       2429
## 2        929        913        928        703        698        603        627
##   2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27
## 1       2476       2331       2238       2378       2201       1968       1993
## 2        676        667        668        659        511        479        420
##   2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03
## 1       1897       1759       1629       1686       1573       1357       1258
## 2        465        409        401        321        238        167        157
##   2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10
## 1       1363       1368       1402       1567       1469       1428       1257
## 2        112        123        131        141        167        178        174
##   2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17
## 1       1521       1569       1482       1383       1413       1227       1372
## 2        168        129        145        112        116        139        115
##   2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24
## 1       1409       1363       1287       1213       1184       1109       1175
## 2        163        161        111        123         89        103        138
##   2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31
## 1       1114       1068       1019       1069        987        910        951
## 2        141        206        237        223        212        230        212
##   2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07
## 1        898        816        833        822        791        756        768
## 2        176        165        145        157        130        151        178
##   2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14
## 1        781        775        708        687        643        601        607
## 2        187        175        154        151        148        153        168
##   2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21
## 1        672        621        593        576        551        483        492
## 2        163        160        141        131        128        115        126
##   2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28
## 1        552        561        498        472        461        403        455
## 2        113        121        138        112        111        104        115
##   2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05
## 1        539        418        492        481        419        390        379
## 2        124        119        119        149        109        108         98
##   2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12
## 1        477        468        421        407        405        323        348
## 2        121        133        121        106        125        129        132
##   2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19
## 1        474        501        472        433        359        348        381
## 2        139        128        118        126        138        127        123
##   2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26
## 1        385        405        401        383        395        323        357
## 2        158        178        177        170        167        143        167
##   2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02
## 1        399        416        435        398        402        374        381
## 2        170        153        179        167        179        181        189
##   2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09
## 1        473        426        450        436        407        363        392
## 2        197        207        201        224        208        239        221
##   2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16
## 1        471        394        311        441        349        305        301
## 2        232        227        214        224        228        220        242
##   2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23
## 1        362        290        319        286        221        224        231
## 2        275        329        342        363        358        351        354
##   2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30
## 1        252        326        322        302        220        217        232
## 2        361        365        368        357        351        358        370
##   2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07
## 1        263        249        230        234        190        187        209
## 2        392        421        432        427        431        418        415
##   2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14
## 1        193        159        141        168        166        139        125
## 2        434        421        445        464        478        486        511
##   2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21
## 1        142        180        181        174        158        162        168
## 2        523        544        548        579        611        664        718
##   2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28
## 1        181        177        189        178        163        154        119
## 2        788        911       1021       1133       1189       1226       1359
##   2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04
## 1        149        113        140        137        101         82         94
## 2       1333       1411       1418       1409       1407       1309       1277
##   2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11
## 1        104        118        108         97        110        117        140
## 2       1119       1007       1219       1001        989        993        961
##   2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18
## 1        147        175        169        173        140        176        170
## 2        970        996       1022        879        887        890        878
##   2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25
## 1        226        238        212        213        197        186        213
## 2        899        789        752        748        680        674        669
##   2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01
## 1        223        216        253        267        270        261        255
## 2        643        632        521        589        547        533        541
##   2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08
## 1        310        306        303        327        386        317        356
## 2        521        512        532        540        509        534        567
##   2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15
## 1        353        369        364        353        337        322        314
## 2        573        610        603        609        600        611        613
##   2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22
## 1        322        334        327        337        325        315        327
## 2        633        618        656        610        600        608        623
##   2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01
## 1        335        353        356        346        338        322        317
## 2        633        644        589        601        588        595        586
##   2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08
## 1        302        331        375        384        382        357        351
## 2        581        577        587        579        588        581        591
##   2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15
## 1        390        386        390        360        351        348        345
## 2        622        645        639        639        641        644        631
##   2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22
## 1        354        393        381        391        382        367        404
## 2        640        645        642        645        644        647        643
##   2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29
## 1        410        466        482        510        502        531        541
## 2        648        641        661        670        683        686        689
##   2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05
## 1        556        585        590        728        684        673        695
## 2        693        699        712        703        710        709        767
##   2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12
## 1        792        783        902        904        878        799        842
## 2        778        783        789        794        801        812        818
##   2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19
## 1        951        929        985        964        948        916        970
## 2        823        831        837        841        845        850        852
##   2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26
## 1       1070       1028       1055       1098       1072        953        958
## 2        855        861        872        884        912        953        991
##   2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03
## 1       1045       1062       1026       1056       1048        937        953
## 2       1003       1011       1003       1021       1032       1051       1078
##   2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10
## 1        999       1016       1090       1039        997        942        986
## 2       1090       1102       1110       1125       1132       1138       1150
##   2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17
## 1        999       1020       1116        927        837        825        886
## 2       1180       1187       1193       1197       1203       1201       1188
##   2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24
## 1       1047       1212       1330       1136       1142       1067       1157
## 2       1169       1160       1153       1148       1151       1145       1149
##   2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31
## 1       1389       1320       1183       1215       1106        907       1245
## 2       1140       1151       1132       1133       1119       1007        984
##   2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07
## 1       1251       1269       1261       1201       1144        984       1161
## 2        956        951        932        861        821        801        782
##   2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14
## 1       1261       1274          0       2461       1077       1017       1109
## 2        773        765        755        733        711        691        613
##   2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21
## 1       1269       1239       1309       1236       1153       1079       1212
## 2        609        606        591        589        566        532        509
##   2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28
## 1       1479       1253       1255       1312       1301       1218       1318
## 2        498        466        423        412        409        389        376
##   2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05
## 1       1567       1486       1534       1338       1148       1173       1247
## 2        261        251        242        198        181        179        175
##   2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12
## 1       1277       1207       1257       1133       1177       1112       1244
## 2        164        161        155        127        121        117        110
##   2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19
## 1       1295       1226       1165       1298       1098       1055       1293
## 2        108         89         81         77         69          0        126
##   2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26
## 1       1273       1142       1162       1247       1256       1194       1252
## 2         51         49         44         41         38         39         35
##   2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02
## 1       1379       1334          0          0       3622       1084          0
## 2         31         38         42         45         47         49         51
##   2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09
## 1          0          0          0       5121          0          0          0
## 2         53         57         51         57         61         65         83
##   2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16
## 1          0          0          0       5439          0          0       1755
## 2         86         91         95         97         99        101        107
##   2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23
## 1          0          0       1115          0        957          0        793
## 2        112        123        131        164        173        184        189
##   2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30
## 1          0          0          0       1324          0          0          0
## 2        194        203        221        234        251        255        263
##   2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06
## 1          0          0          0          0          0       1925          0
## 2        279        291        303        318        331        343        368
##   2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13
## 1        262          0        222        102          0          0          0
## 2        378        399        413        433        458        481        491
##   2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20
## 1          0          0        507         75          0          0        201
## 2        503        531        569        588        637        653        679
##   2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27
## 1         69         54          0          0        147         44         59
## 2        688        692        722        568        667        680        702
##   2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04
## 1         50          0         99          0          0          0          0
## 2        718        738        741        745        761        768        771
##   2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11
## 1        223         45         47         48         35         59         58
## 2        778        788        799        811        831        837        846
##   2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18
## 1         55         57         36         48         45         41         38
## 2        857        861        865        869        874        871        883
##   2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25
## 1         49         47         46         51         43         47         51
## 2        868        877        883        885        881        886        889
##   2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01
## 1         65         55         51         56         41         46         49
## 2        871        907        923        927        948        933        951
##   2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08
## 1         45         49         45         43         42         40         43
## 2        921        951        911        922        909        903        929
##   2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15
## 1         49         38         43         45         44         30         38
## 2        921        934        919        909        929          0       1881
##   2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22
## 1         37         42         38         35         31         36         39
## 2        941        960        911        892        902        884        870
##   2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29
## 1         38         34         28         24         29         24         25
## 2        856        901        899        913        931        951        911
##   2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06
## 1         32         34         24         38         29         35         43
## 2        949        938        919        933        892        902        871
##   2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13
## 1         42         46         45         48         53         51         64
## 2        889        909        803        822        879        863          0
##   2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20
## 1         65         88         85         80        116        104        146
## 2       1621        879        901        910        902        919        903
##   2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27
## 1        222        252        287        332        325        389        524
## 2        848        879        883        811        866        823        851
##   2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03
## 1        602        744        752        819        846       1024       1746
## 2        809        854        871        847        783        801        723
##   2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10
## 1       2585       3045       3168       3575       3068       3460       4778
## 2        769        803        840        821        830        912        951
##   2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17
## 1       4652       5362       5499       5628       5281       5477       5505
## 2        932        948       1011       1079       1101       1197       1232
##   2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24
## 1       5873       5928       5591       4884       4608       4535       4838
## 2       1303       1379       1403       1533       1569       1603       1651
##   2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31
## 1       4541       4526       4738       4474       3913       3669       4211
## 2       1809       1910       1985       2007       2018       2210       2223
##   2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 1       3861       4092       3852       3555       3013       3260       3747
## 2       2291       2278       2281       2291       2298       2301       2272
##   2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 1       3330       3162       2866       2523       1726       2136       2227
## 2       2194       2191       2189       2179       2145          0       4260
##   2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 1       1982       1793       1569       1376        997       1013       1052
## 2       2117       2101       2071       2053       2025       2009       2003
##   2022-02-22 2022-02-23
## 1        841        627
## 2       1989       1892
## 
## $Growth.Rate
##        geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1 SAUDI ARABIA        NaN        NaN        NaN        NaN        NaN
## 2        EGYPT        NaN        NaN        NaN        NaN        NaN
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN         NA          0        NaN        NaN        NaN
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1        NaN        NaN        NaN        NaN        NaN         NA          0
## 2        NaN        NaN        NaN        NaN         NA          0        NaN
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1        NaN         NA          0        NaN         NA  0.6666667  1.2500000
## 2        NaN         NA         12          0         NA  0.1764706  0.6666667
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1       0.20         24   1.708333  0.4146341 0.00000000         NA   3.533333
## 2       0.25          7   1.857143  2.2307692 0.03448276         40   1.150000
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1          0         NA  0.6796117  0.6857143   2.479167  0.4285714  4.0196078
## 2          0         NA  0.4833333  0.3103448   3.666667  1.1818182  0.9230769
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1  0.6487805  0.8421053  0.8214286  1.0760870   0.969697   1.604167  0.7142857
## 2  1.5000000  0.7222222  1.0512821  0.9756098   0.825000   1.424242  1.1489362
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1   1.427273   1.050955  0.9333333  0.9090909   1.592857  0.9103139  0.9359606
## 2   1.277778   1.246377  1.3953488  0.7083333   1.211765  1.4466019  0.8590604
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1  0.7210526   2.591241  1.0253521   1.049451  1.1230366  1.1002331  0.9216102
## 2  0.8593750   1.263636  0.6834532   1.526316  0.8689655  0.9920635  1.2800000
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1   1.133333   1.050710   1.471042   1.485564  0.9611307    1.03125  1.0222816
## 2   0.968750   1.083871   1.017857   1.099415  0.5957447    1.68750  0.8306878
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1   0.994769   1.014899  1.0120898   1.021331  1.0217210   1.053966  0.9821567
## 2   1.076433   1.372781  0.8663793   1.129353  0.9471366   1.153488  1.0483871
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1  1.0466035   1.019623  0.9948187  1.0133929  1.1395007   1.059923  0.9696049
## 2  0.8692308   1.190265  1.3308550  0.8324022  0.9127517   1.279412  1.1149425
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1  1.0576803   1.062833  0.9486893  1.0017637  1.1220657   1.028243  0.9720244
## 2  0.9974227   1.015504  1.2595420  0.9858586  0.8934426   0.793578  1.0028902
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1  0.9968603   1.070341   1.131437   1.231036  0.9633803  0.9477339  0.9676051
## 2  0.9740634   1.177515   1.002513   1.230576  1.0386965  1.0490196  1.3457944
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1   1.072539  0.9409142   1.043444  0.9242998  0.9823915  0.9316382  0.8639821
## 2   1.034722  1.0389262   1.011628  0.9284802  1.0343879  0.9335106  1.1239316
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1  0.9399275  0.9057851  0.9616788   1.023403   1.160074  1.0021311  0.9936204
## 2  1.1533587  1.2384615  1.1437445   1.060512   1.123628  0.9108073  0.8234453
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1  1.1615837   0.909719   1.311899   1.204554  0.9756488  1.1064039  0.9759573
## 2  0.9366319   1.067655   1.170139   1.110534  0.9799599  0.9304703  1.0146520
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1   1.130474  1.0043045   1.050362  0.8584545  1.2575758   1.064730  0.9467495
## 2   1.050542  0.9910653   1.093620  1.0634115  0.9648181   1.045117  0.9266706
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1  1.1528006  0.9670665  0.9041413  0.9162985  0.8573966   1.004143  0.9251400
## 2  0.8698149  0.8936170  1.4564860  0.8720406  0.9534583   1.068475  0.8451777
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1  0.9949028   1.079731   1.167853  0.9972067   1.015788  0.9884683  1.1126046
## 2  1.0660661   1.104930   1.035692  0.7187692   1.083048  1.2379447  0.9942529
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1  0.7754730   0.994415  1.2394325  0.9844980  0.8672481  1.1751397  0.8062753
## 2  0.9653179   0.988024  0.9508418  0.9376771  0.9199396  0.7955665  1.0908153
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1  0.8950472  1.0484190  0.9924599  0.9477683  0.9281897   1.026268  0.9438990
## 2  0.9697256  0.9268293  1.0326316  0.9408767  0.9880823   1.020833  0.9978518
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1  0.9921991   1.034818  0.9453690  0.9816303  0.9762183  0.9700479    1.01935
## 2  0.9827772   1.016429  0.7575431  0.9928876  0.8638968  1.0398010    1.07815
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1  0.9414378   0.960103  1.0625559  0.9255677  0.8941390  1.0127033  0.9518314
## 2  0.9866864   1.001499  0.9865269  0.7754173  0.9373777  0.8768267  1.1071429
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1  0.9272536  0.9260944  1.0349908  0.9329775  0.8626828  0.9270450  1.0834658
## 2  0.8795699  0.9804401  0.8004988  0.7414330  0.7016807  0.9401198  0.7133758
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1   1.003668   1.024854   1.117689  0.9374601  0.9720899  0.8802521  1.2100239
## 2   1.098214   1.065041   1.076336  1.1843972  1.0658683  0.9775281  0.9655172
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1  1.0315582  0.9445507  0.9331984   1.021692  0.8683652  1.1181744   1.026968
## 2  0.7678571  1.1240310  0.7724138   1.035714  1.1982759  0.8273381   1.417391
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1  0.9673527  0.9442406  0.9425019  0.9760923  0.9366554   1.059513  0.9480851
## 2  0.9877301  0.6894410  1.1081081  0.7235772  1.1573034   1.339806  1.0217391
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1  0.9587074  0.9541199  1.0490677  0.9232928  0.9219858  1.0450549  0.9442692
## 2  1.4609929  1.1504854  0.9409283  0.9506726  1.0849057  0.9217391  0.8301887
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1   0.908686  1.0208333  0.9867947  0.9622871  0.9557522   1.015873   1.016927
## 2   0.937500  0.8787879  1.0827586  0.8280255  1.1615385   1.178808   1.050562
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1  0.9923175  0.9135484  0.9703390  0.9359534  0.9346812   1.009983  1.1070840
## 2  0.9358289  0.8800000  0.9805195  0.9801325  1.0337838   1.098039  0.9702381
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1  0.9241071  0.9549114  0.9713322  0.9565972  0.8765880   1.018634  1.1219512
## 2  0.9815951  0.8812500  0.9290780  0.9770992  0.8984375   1.095652  0.8968254
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1   1.016304  0.8877005  0.9477912  0.9766949  0.8741866   1.129032   1.184615
## 2   1.070796  1.1404959  0.8115942  0.9910714  0.9369369   1.105769   1.078261
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1  0.7755102   1.177033  0.9776423  0.8711019  0.9307876  0.9717949   1.258575
## 2  0.9596774   1.000000  1.2521008  0.7315436  0.9908257  0.9074074   1.234694
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1  0.9811321  0.8995726  0.9667458   0.995086  0.7975309   1.077399   1.362069
## 2  1.0991736  0.9097744  0.8760331   1.179245  1.0320000   1.023256   1.053030
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1  1.0569620  0.9421158  0.9173729  0.8290993  0.9693593  1.0948276   1.010499
## 2  0.9208633  0.9218750  1.0677966  1.0952381  0.9202899  0.9685039   1.284553
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1   1.051948  0.9901235  0.9551122  1.0313316  0.8177215   1.105263   1.117647
## 2   1.126582  0.9943820  0.9604520  0.9823529  0.8562874   1.167832   1.017964
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1   1.042607   1.045673  0.9149425   1.010050  0.9303483   1.018717   1.241470
## 2   0.900000   1.169935  0.9329609   1.071856  1.0111732   1.044199   1.042328
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1  0.9006342  1.0563380  0.9688889  0.9334862  0.8918919  1.0798898   1.201531
## 2  1.0507614  0.9710145  1.1144279  0.9285714  1.1490385  0.9246862   1.049774
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1  0.8365180  0.7893401   1.418006  0.7913832  0.8739255  0.9868852   1.202658
## 2  0.9784483  0.9427313   1.046729  1.0178571  0.9649123  1.1000000   1.136364
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1   0.801105   1.100000  0.8965517  0.7727273  1.0135747   1.031250   1.090909
## 2   1.196364   1.039514  1.0614035  0.9862259  0.9804469   1.008547   1.019774
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1   1.293651  0.9877301  0.9378882  0.7284768  0.9863636   1.069124   1.133621
## 2   1.011080  1.0082192  0.9701087  0.9831933  1.0199430   1.033520   1.059459
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1  0.9467681  0.9236948  1.0173913  0.8119658  0.9842105   1.117647   0.923445
## 2  1.0739796  1.0261283  0.9884259  1.0093677  0.9698376   0.992823   1.045783
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1  0.8238342  0.8867925   1.191489  0.9880952  0.8373494  0.8992806   1.136000
## 2  0.9700461  1.0570071   1.042697  1.0301724  1.0167364  1.0514403   1.023483
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1   1.267606   1.005556   0.961326   0.908046   1.025316   1.037037   1.077381
## 2   1.040153   1.007353   1.056569   1.055268   1.086743   1.081325   1.097493
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1  0.9779006   1.067797  0.9417989  0.9157303  0.9447853  0.7727273  1.2521008
## 2  1.1560914   1.120746  1.1096964  1.0494263  1.0311186  1.1084829  0.9808683
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1  0.7583893   1.238938  0.9785714  0.7372263  0.8118812  1.1463415  1.1063830
## 2  1.0585146   1.004961  0.9936530  0.9985806  0.9303483  0.9755539  0.8762725
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1  1.1346154  0.9152542  0.8981481   1.134021   1.063636  1.1965812   1.050000
## 2  0.8999106  1.2105263  0.8211649   0.988012   1.004044  0.9677744   1.009365
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1   1.190476  0.9657143  1.0236686  0.8092486   1.257143  0.9659091   1.329412
## 2   1.026804  1.0261044  0.8600783  1.0091013   1.003382  0.9865169   1.023918
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1  1.0530973  0.8907563  1.0047170  0.9248826  0.9441624  1.1451613   1.046948
## 2  0.8776418  0.9531052  0.9946809  0.9090909  0.9911765  0.9925816   0.961136
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1  0.9686099  1.1712963   1.055336  1.0112360  0.9666667  0.9770115  1.2156863
## 2  0.9828927  0.8243671   1.130518  0.9286927  0.9744059  1.0150094  0.9630314
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1  0.9870968  0.9901961   1.079208  1.1804281  0.8212435   1.123028   0.991573
## 2  0.9827255  1.0390625   1.015038  0.9425926  1.0491159   1.061798   1.010582
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
## 1   1.045326  0.9864499  0.9697802  0.9546742  0.9554896  0.9751553   1.025478
## 2   1.064572  0.9885246  1.0099502  0.9852217  1.0183333  1.0032733   1.032626
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1  1.0372671  0.9790419   1.030581  0.9643917  0.9692308   1.038095   1.024465
## 2  0.9763033  1.0614887   0.929878  0.9836066  1.0133333   1.024671   1.016051
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1   1.053731  1.0084986  0.9719101  0.9768786  0.9526627  0.9844720  0.9526814
## 2   1.017378  0.9145963  1.0203735  0.9783694  1.0119048  0.9848739  0.9914676
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1  1.0960265   1.132931  1.0240000  0.9947917  0.9345550  0.9831933   1.111111
## 2  0.9931153   1.017331  0.9863714  1.0155440  0.9880952  1.0172117   1.052453
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1  0.9897436  1.0103627  0.9230769    0.97500   0.991453  0.9913793   1.026087
## 2  1.0369775  0.9906977  1.0000000    1.00313   1.004680  0.9798137   1.014263
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1   1.110169  0.9694656   1.026247  0.9769821   0.960733  1.1008174   1.014851
## 2   1.007812  0.9953488   1.004673  0.9984496   1.004658  0.9938176   1.007776
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1  1.1365854   1.034335   1.058091  0.9843137   1.057769   1.018832   1.027726
## 2  0.9891975   1.031201   1.013616  1.0194030   1.004392   1.004373   1.005806
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1   1.052158   1.008547  1.2338983  0.9395604  0.9839181   1.032689   1.139568
## 2   1.008658   1.018598  0.9873596  1.0099573  0.9985915   1.081805   1.014342
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1  0.9886364   1.151980   1.002217  0.9712389  0.9100228   1.053817   1.129454
## 2  1.0064267   1.007663   1.006337  1.0088161  1.0137328   1.007389   1.006112
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1  0.9768665    1.06028  0.9786802  0.9834025  0.9662447   1.058952   1.103093
## 2  1.0097205    1.00722  1.0047790  1.0047562  1.0059172   1.002353   1.003521
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1  0.9607477   1.026265   1.040758  0.9763206  0.8889925   1.005247   1.090814
## 2  1.0070175   1.012776   1.013761  1.0316742  1.0449561   1.039874   1.012109
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1   1.016268  0.9661017   1.029240  0.9924242   0.894084   1.017076   1.048269
## 2   1.007976  0.9920870   1.017946  1.0107738   1.018411   1.025690   1.011132
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1   1.017017   1.072835   0.953211  0.9595765  0.9448345   1.046709   1.013185
## 2   1.011009   1.007260   1.013514  1.0062222  1.0053004   1.010545   1.026087
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1   1.021021   1.094118  0.8306452  0.9029126  0.9856631  1.0739394  1.1817156
## 2   1.005932   1.005055  1.0033529  1.0050125  0.9983375  0.9891757  0.9840067
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1  1.1575931  1.0973597  0.8541353   1.005282  0.9343257   1.084349  1.2005186
## 2  0.9923011  0.9939655  0.9956635   1.002613  0.9947871   1.003493  0.9921671
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1   0.950324  0.8962121   1.027050  0.9102881  0.8200723  1.3726571  1.0048193
## 2   1.009649  0.9834926   1.000883  0.9876434  0.8999106  0.9771599  0.9715447
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1  1.0143885  0.9936958  0.9524187  0.9525396  0.8601399  1.1798780   1.086133
## 2  0.9947699  0.9800210  0.9238197  0.9535424  0.9756395  0.9762797   0.988491
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1  1.0103093  0.0000000         NA  0.4376270  0.9442897  1.0904621  1.1442741
## 2  0.9896507  0.9869281  0.9708609  0.9699864  0.9718706  0.8871201  0.9934747
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1  0.9763593  1.0564972  0.9442322  0.9328479  0.9358196  1.1232623   1.220297
## 2  0.9950739  0.9752475  0.9966159  0.9609508  0.9399293  0.9567669   0.978389
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1  0.8471941  1.0015962  1.0454183  0.9916159  0.9362029   1.082102  1.1889226
## 2  0.9357430  0.9077253  0.9739953  0.9927184  0.9511002   0.966581  0.6941489
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1  0.9483089  1.0323015  0.8722295  0.8579970  1.0217770  1.0630861  1.0240577
## 2  0.9616858  0.9641434  0.8181818  0.9141414  0.9889503  0.9776536  0.9371429
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1  0.9451840  1.0414250  0.9013524  1.0388350  0.9447749  1.1187050  1.0409968
## 2  0.9817073  0.9627329  0.8193548  0.9527559  0.9669421  0.9401709  0.9818182
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1  0.9467181  0.9502447  1.1141631  0.8459168  0.9608379   1.225592  0.9845321
## 2  0.8240741  0.9101124  0.9506173  0.8961039  0.0000000         NA  0.4047619
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1  0.8970935  1.0175131  1.0731497  1.0072173  0.9506369  1.0485762  1.1014377
## 2  0.9607843  0.8979592  0.9318182  0.9268293  1.0263158  0.8974359  0.8857143
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1  0.9673677   0.000000        NaN         NA  0.2992822   0.000000        NaN
## 2  1.2258065   1.105263   1.071429   1.044444  1.0425532   1.040816   1.039216
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1        NaN        NaN         NA   0.000000        NaN        NaN        NaN
## 2   1.075472  0.8947368   1.117647   1.070175   1.065574   1.276923   1.036145
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1        NaN        NaN         NA   0.000000        NaN         NA   0.000000
## 2    1.05814   1.043956   1.021053   1.020619   1.020202   1.059406   1.046729
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1        NaN         NA   0.000000         NA   0.000000         NA   0.000000
## 2   1.098214   1.065041   1.251908   1.054878   1.063584   1.027174   1.026455
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1        NaN        NaN         NA    0.00000        NaN        NaN        NaN
## 2   1.046392    1.08867   1.058824    1.07265   1.015936   1.031373   1.060837
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1        NaN        NaN        NaN        NaN         NA   0.000000         NA
## 2   1.043011   1.041237   1.049505   1.040881   1.036254   1.072886   1.027174
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1   0.000000         NA  0.4594595   0.000000        NaN        NaN        NaN
## 2   1.055556   1.035088  1.0484262   1.057737   1.050218    1.02079    1.02444
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1        NaN         NA   0.147929   0.000000        NaN         NA  0.3432836
## 2   1.055666   1.071563   1.033392   1.083333   1.025118   1.039816  1.0132548
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1  0.7826087   0.000000        NaN         NA  0.2993197   1.340909  0.8474576
## 2  1.0058140   1.043353  0.7867036   1.174296  1.0194903   1.032353  1.0227920
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1   0.000000         NA   0.000000        NaN        NaN        NaN         NA
## 2   1.027855   1.004065   1.005398   1.021477   1.009198   1.003906   1.009079
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1  0.2017937   1.044444   1.021277  0.7291667   1.685714  0.9830508  0.9482759
## 2  1.0128535   1.013959   1.015019  1.0246609   1.007220  1.0107527  1.0130024
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1   1.036364  0.6315789   1.333333   0.937500  0.9111111  0.9268293  1.2894737
## 2   1.004667  1.0046458   1.004624   1.005754  0.9965675  1.0137773  0.9830125
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1  0.9591837  0.9787234   1.108696  0.8431373   1.093023   1.085106  1.2745098
## 2  1.0103687  1.0068415   1.002265  0.9954802   1.005675   1.003386  0.9797525
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1  0.8461538  0.9272727   1.098039  0.7321429  1.1219512   1.065217  0.9183673
## 2  1.0413318  1.0176406   1.004334  1.0226537  0.9841772   1.019293  0.9684543
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1   1.088889  0.9183673  0.9555556  0.9767442  0.9523810   1.075000  1.1395349
## 2   1.032573  0.9579390  1.0120746  0.9859002  0.9933993   1.028793  0.9913886
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1  0.7755102   1.131579  1.0465116  0.9777778  0.6818182   1.266667  0.9736842
## 2  1.0141151   0.983940  0.9891186  1.0220022  0.0000000         NA  0.5002658
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1   1.135135  0.9047619  0.9210526  0.8857143  1.1612903  1.0833333   0.974359
## 2   1.020191  0.9489583  0.9791438  1.0112108  0.9800443  0.9841629   0.983908
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1  0.8947368  0.8235294  0.8571429   1.208333  0.8275862   1.041667   1.280000
## 2  1.0525701  0.9977802  1.0155729   1.019715  1.0214823   0.957939   1.041712
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1  1.0625000  0.7058824   1.583333  0.7631579   1.206897  1.2285714  0.9767442
## 2  0.9884089  0.9797441   1.015234  0.9560557   1.011211  0.9656319  1.0206659
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1   1.095238  0.9782609   1.066667   1.104167  0.9622642   1.254902   1.015625
## 2   1.022497  0.8833883   1.023661   1.069343  0.9817975   0.000000         NA
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1  1.3538462  0.9659091  0.9411765  1.4500000  0.8965517  1.4038462  1.5205479
## 2  0.5422579  1.0250284  1.0099889  0.9912088  1.0188470  0.9825898  0.9390919
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1   1.135135   1.138889  1.1567944  0.9789157  1.1969231   1.347044  1.1488550
## 2   1.036557   1.004551  0.9184598  1.0678175  0.9503464   1.034022  0.9506463
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1   1.235880   1.010753  1.0890957  1.0329670   1.210402  1.7050781   1.480527
## 2   1.055624   1.019906  0.9724455  0.9244392   1.022989  0.9026217   1.063624
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1   1.177950   1.040394   1.128472  0.8581818   1.127771   1.380925  0.9736291
## 2   1.044213   1.046077   0.977381  1.0109622   1.098795   1.042763  0.9800210
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1   1.152623   1.025550   1.023459   0.938344   1.037114   1.005112   1.066848
## 2   1.017167   1.066456   1.067260   1.020389   1.087193   1.029240   1.057630
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1   1.009365  0.9431511  0.8735468  0.9434889   0.984158   1.066814   0.938611
## 2   1.058327  1.0174039  1.0926586  1.0234834   1.021670   1.029944   1.095700
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1  0.9966968   1.046840  0.9442803  0.8746089  0.9376438   1.147724  0.9168844
## 2  1.0558320   1.039267  1.0110831  1.0054808  1.0951437   1.005882  1.0305893
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1  1.0598291   0.941349  0.9228972  0.8475387   1.081978  1.1493865   0.888711
## 2  0.9943256   1.001317  1.0043840  1.0030554   1.001305  0.9873968   0.965669
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1  0.9495495  0.9063884  0.8803210  0.6841062   1.237543   1.042603  0.8899865
## 2  0.9986326  0.9990872  0.9954317  0.9843965   0.000000         NA  0.4969484
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1  0.9046418  0.8750697  0.8769917  0.7245640  1.0160481  1.0384995  0.7994297
## 2  0.9924421  0.9857211  0.9913085  0.9863614  0.9920988  0.9970134  0.9930105
##   2022-02-23 NA
## 1  0.7455410 NA
## 2  0.9512318 NA

# in Saudi Arabia
growth.rate(TS.data,geo.loc=c("Qatar","Kuwait"))

## Processing...  QATAR

## Processing...  KUWAIT

## $Changes
##   geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1   QATAR          0          0          0          0          0          0
## 2  KUWAIT          0          0          0          0          0          0
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          1         10
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1          0          0          0          1          2          0          4
## 2         15         17          2          0          0         11          0
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1          1          0          0          0          7          3          6
## 2          0          2          0          3          3          0          5
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1        238          0         58         17         64         38          0
## 2          3          8          0         24          8         11          7
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1         13          8         10         11         13          7         25
## 2         12          6         11         17         12          1          2
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1         11         12         13         28         44         59         88
## 2          4         13         17         10         20         11         23
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1         54        114        126        250        279        228        225
## 2         28         25         75         62         77        109         78
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1        153        166        136        216        251        252        197
## 2        112         55         83        161         80         66         55
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1        283        392        560        345        440        567        518
## 2         50        119        134         93        164         80         85
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1        608        623        761        833        929        957        677
## 2        168        151        215        278        183        213        152
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1        643        845        687        776        679        640        951
## 2        300        284        353        242        364        295        526
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1        830        918       1311       1130       1189       1103       1526
## 2        485        278        641        415       1065        598        991
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1       1390       1733       1153       1547       1632       1365       1637
## 2        751        947        885        942       1048        841       1073
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1       1491       1554       1830       1732       1501       1751       1742
## 2        804       1041        955        900        838        665        608
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1       1740       1967       1993       2355       1648       1523       1826
## 2        692        845       1072       1008        851        719        887
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1       1901       1581       1754       1700       1595       1368       1721
## 2        710        562        723        487        717        662        630
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1       1716       1476       1517       1828       1186       1274       1201
## 2        683        609        520        514        454        511        527
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1       1097       1267       1021       1026        881       1034       1176
## 2        575        541        604        467        505        641        742
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1       1199       1060        946        879        750        693        982
## 2        846        909        915        688        551        582        671
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1        915        894        756        530        616        546        600
## 2        745        919        813        631        638        703        601
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1        608        557        520        498        470        418        517
## 2        762        833        740        478        836        614        666
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1        450        494        421        410        340        389        393
## 2        703        791        553        683        300        559        671
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1        441        373        394        398        269        292        283
## 2        751        687        753        684        464        606        770
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1        273        307        235        216        196        215        216
## 2        754        626        428        491        463        388        475
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1        267        287        291        267        297        315        384
## 2        651        620        682        472        514        687        668
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1        292        343        251        277        271        288        293
## 2        717        701        699        512        508        622        643
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1        295        268        257        284        243        258        232
## 2        675        622        502        688        571        432        613
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1        244        246        208        211        168        203        216
## 2        698        674        633        646        412        473        702
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1        212        214        217        227        231        253        231
## 2        667        900        865        720        619        805        857
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1        267        206        235        236        217        235        239
## 2        838        740        653        736        553        708        829
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1        235        244        224        229        230        228        313
## 2        698        825        704        521        385        530        719
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1        258        250        225        200        234        227        222
## 2        616        552        590        758        345        437        587
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1        227        199        205        175        159        194        251
## 2        614        494        411        371        567        567        676
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1        238        213        206        178        207        206        214
## 2        475        698        635        492        548        777        844
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1        198        200        189        235        204        240        273
## 2        532        746        729        739        663        686        886
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1        266        252        249        254        205        262        257
## 2        813        889        812        695        708        682        775
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1        250        211        193        213        164        197        226
## 2        814        760        671        589        608        759        787
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1        227        249        192        202        190        230        230
## 2        763        795        825        742        538        735        903
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1        224        245        235        203        215        243        194
## 2        778        773        718        691        499        489        556
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1        219        208        239        174        167        186        227
## 2        452        485        486        426        322        337        402
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1        209        215        184        227        171        185        168
## 2        422        330        329        319        231        209        357
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1        255        221        166        140        125        178        117
## 2        268        314        343        247        205        230        301
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1        150        163        164        147        134        160        151
## 2        304        291        294        255        174        231        261
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1        145        140        159        142        143        158        149
## 2        261        221        339        244        204        230        298
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1        140        157        129        169        159        160        206
## 2        266        244        260        172        204        204        236
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1        193        213        208        198        197        207        208
## 2        205        286        285        205        269        372        312
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1        209        210        195        206        193        203        211
## 2        411        540        495        427        414        527        494
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1        201        209        196        204        188        227        225
## 2        539        560        530        435        378        467        578
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1        271        258        263        251        247        277        299
## 2        442        570        533        534        384        492        505
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1        338        347        341        363        351        385        375
## 2        580        588        658        514        635        586        811
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1        396        407        398        394        408        427        477
## 2        756        840        940        846        962        996       1002
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
## 1        451        448        450        453        440        888          6
## 2        987       1048       1021        851        798        823        964
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1        453        462        465        449        459        463        455
## 2       1017        979        976        862        768        899       1015
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1        459        465        469        460        467        473        463
## 2       1001       1019       1022        844        962       1179       1341
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1        471        475        469        460        474        468        471
## 2       1409       1716       1613       1318       1144       1326       1157
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1        473        468        455        483        485        481        479
## 2       1333       1505       1356       1211       1063       1332       1314
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1        489        499        497        509        503        519        534
## 2       1504       1394       1519       1347       1192       1330       1288
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1        570        587        602        614        639        690        720
## 2       1299       1390       1548       1198       1121       1251       1271
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1        780        840        874        870        876        910        927
## 2       1282       1418       1233       1235       1203       1357       1403
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1        940        949        950        964        961        973        981
## 2       1517       1379       1477       1379       1390       1635       1544
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1        984        989        978        827        823        896        885
## 2       1402       1391       1406       1388       1127       1510       1371
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1        819        800        798        718        705        703        695
## 2       1439       1459       1432       1206       1545       1286       1446
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1        690        687        676        650        646        644        640
## 2       1464       1417       1429       1279       1316       1246       1253
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1        645        593        600        533        389        397        343
## 2       1451       1236       1209       1095        992        978       1153
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1        392        299        244        260        256        302        370
## 2        985       1059        763        795        828        861       1084
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1        295        313        367        330        283        299        349
## 2       1119       1168       1345       1017        992       1240       1408
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1        306        286        202        156        189        228        230
## 2       1176       1160       1384       1134       1095       1410       1279
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1        194        198        183        192        172        171        182
## 2       1345       1443       1280       1331       1297       1479       1581
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1        158        143        185        147        117        157        146
## 2       1391       1709       1657       1437       1512       1563       1487
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1        165        127        184        184        107        130        189
## 2       1557       1646       1658       1497       1661       1935       1962
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1        154        105         87        125        102        118        143
## 2       1870       1761       1702       1661       1558       1652       1718
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1        118        146        133        103        121         93         93
## 2       1836       1824       1895       1612       1654       1977       1993
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1        158        113        144         97         86        146        142
## 2       1585       1705       1617       1555       1490       1770       1712
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1        134        131        133        116        118        124        108
## 2       1623       1385       1263       1226       1189       1107       1043
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1        128        196        114        124        126        178        146
## 2        969        987        926        987        836        988        933
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1        225        158        172        162        151        150        170
## 2        941        853        766        733        707        805        785
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1        164        181        218        199        217        210        220
## 2        851        718        596        501        555        519        595
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1        227        197        244        194        187        260        269
## 2        424        555        417        375        306        343        256
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1        218        306        221        190        205        289        217
## 2        237        276        226        200        167        189        166
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1        216        233        212        183        179        205        173
## 2        197        212        168        186        189        184        124
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1        166        177        193        157        130        189        172
## 2        101        111         95        103         72         71         83
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1        165        143        126        147        133        131        122
## 2         66         69         62         41         50         58         59
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1        159        133        117         82        101        139        143
## 2         59         58         63         43         56         53         45
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1        138        109        107        108         67         90         94
## 2         38         42         48         41         37         49         39
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1         76         85         92         99         79         77         58
## 2         33         50         35         41         72         52         49
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1         99         78         72         62         65         59         96
## 2         31         46         35         32         31         42         37
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1         83         79         57         69         62         67         83
## 2         38         32         30         39         35         39         32
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1         86        106         88         77         82        122        102
## 2         21         25         32         25         21         30         21
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1        103        105        101         92        104        127        134
## 2         16         25         22         12         25         30         23
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1        138        106        119        124        103        149        138
## 2         21         16         25         28         23         25         24
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1        172        148        123        137        124        143        146
## 2         23         20         24         22         26         26         16
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1        149        145        147        150        118        141        147
## 2         18         22         16         14         17         28         12
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1        143        155        151        155        153        158        157
## 2         21         26         21         21          0         61         35
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1        160        151        154        159        152        164        158
## 2         21         36         22         23         27         33         31
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1        163        159        163        158        166        169        167
## 2         33         20         29         35         39          0         79
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1        165        169        164        179        170        177        183
## 2         57         44         81         51         75         80         92
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1        185        187        248        279        296        343        367
## 2        143        178        170        150        240        198        329
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1        443        542        741        833        998       1177       1695
## 2        399        554        504        588        609        982       1482
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1       2273       2779       3192       3487       3689       3878       4169
## 2       2246       2413       2645       2820       2999       3683       4397
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1       4206       4187       4123       4007       4021       3998       3816
## 2       4548       4883       4881       4517       4503       5147       4825
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1       3723       3294       3204       3087       2981       2748       2551
## 2       4337       4510       4809       4148       4347       5176       5742
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1       2204       1952       1743       1538       1557       1509       1236
## 2       6454       6515       6913       5808       5592       6063       6436
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1       1245       1183        997        903        912        923        819
## 2       6592       5990       5407       4445       4232       4294       3989
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1        776        783        657        607        613        601        547
## 2       3463       3324       2896       2254       2268       2562       2166
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1        498        447        452        434        442        416        394
## 2       1917       1501       1348       1019       1195       1329       1053
##   2022-02-23
## 1        365
## 2       1012
## 
## $Growth.Rate
##   geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1   QATAR        NaN        NaN        NaN        NaN        NaN        NaN
## 2  KUWAIT        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN         NA         10        1.5
##   2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1        NaN        NaN         NA          2          0         NA       0.25
## 2   1.133333  0.1176471          0        NaN         NA          0        NaN
##   2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1          0        NaN        NaN         NA  0.4285714          2   39.66667
## 2         NA          0         NA          1  0.0000000         NA    0.60000
##   2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1   0.000000         NA  0.2931034  3.7647059    0.59375  0.0000000         NA
## 2   2.666667          0         NA  0.3333333    1.37500  0.6363636   1.714286
##   2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1  0.6153846   1.250000   1.100000  1.1818182 0.53846154   3.571429       0.44
## 2  0.5000000   1.833333   1.545455  0.7058824 0.08333333   2.000000       2.00
##   2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1   1.090909   1.083333  2.1538462   1.571429   1.340909   1.491525  0.6136364
## 2   3.250000   1.307692  0.5882353   2.000000   0.550000   2.090909  1.2173913
##   2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1  2.1111111   1.105263  1.9841270   1.116000  0.8172043  0.9868421   0.680000
## 2  0.8928571   3.000000  0.8266667   1.241935  1.4155844  0.7155963   1.435897
##   2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1  1.0849673  0.8192771   1.588235  1.1620370   1.003984  0.7817460  1.4365482
## 2  0.4910714  1.5090909   1.939759  0.4968944   0.825000  0.8333333  0.9090909
##   2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1   1.385159   1.428571  0.6160714   1.275362  1.2886364  0.9135802   1.173745
## 2   2.380000   1.126050  0.6940299   1.763441  0.4878049  1.0625000   1.976471
##   2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1  1.0246711   1.221509   1.094612  1.1152461   1.030140   0.707419  0.9497784
## 2  0.8988095   1.423841   1.293023  0.6582734   1.163934   0.713615  1.9736842
##   2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1  1.3141524  0.8130178  1.1295488   0.875000  0.9425626   1.485937  0.8727655
## 2  0.9466667  1.2429577  0.6855524   1.504132  0.8104396   1.783051  0.9220532
##   2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1  1.1060241   1.428105  0.8619375   1.052212  0.9276703   1.383500  0.9108781
## 2  0.5731959   2.305755  0.6474259   2.566265  0.5615023   1.657191  0.7578204
##   2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1   1.246763  0.6653203   1.341717   1.054945  0.8363971   1.199267  0.9108125
## 2   1.260985  0.9345301   1.064407   1.112527  0.8024809   1.275862  0.7493010
##   2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1   1.042254  1.1776062  0.9464481  0.8666282  1.1665556  0.9948601  0.9988519
## 2   1.294776  0.9173871  0.9424084  0.9311111  0.7935561  0.9142857  1.1381579
##   2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1   1.130460   1.013218  1.1816357  0.6997877  0.9241505   1.198949   1.041073
## 2   1.221098   1.268639  0.9402985  0.8442460  0.8448884   1.233658   0.800451
##   2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1  0.8316675   1.109424  0.9692132  0.9382353  0.8576803  1.2580409  0.9970947
## 2  0.7915493   1.286477  0.6735823  1.4722793  0.9232915  0.9516616  1.0841270
##   2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1  0.8601399  1.0277778  1.2050099  0.6487965   1.074199  0.9427002  0.9134055
## 2  0.8916545  0.8538588  0.9884615  0.8832685   1.125551  1.0313112  1.0910816
##   2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1  1.1549681  0.8058406  1.0048972  0.8586745   1.173666   1.137331   1.019558
## 2  0.9408696  1.1164510  0.7731788  1.0813704   1.269307   1.157566   1.140162
##   2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1  0.8840701  0.8924528  0.9291755  0.8532423   0.924000   1.417027  0.9317719
## 2  1.0744681  1.0066007  0.7519126  0.8008721   1.056261   1.152921  1.1102832
##   2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1  0.9770492  0.8456376  0.7010582   1.162264  0.8863636  1.0989011   1.013333
## 2  1.2335570  0.8846572  0.7761378   1.011094  1.1018809  0.8549075   1.267887
##   2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1  0.9161184  0.9335727  0.9576923  0.9437751  0.8893617   1.236842  0.8704062
## 2  1.0931759  0.8883553  0.6459459  1.7489540  0.7344498   1.084691  1.0555556
##   2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1   1.097778  0.8522267  0.9738717  0.8292683   1.144118   1.010283   1.122137
## 2   1.125178  0.6991150  1.2350814  0.4392387   1.863333   1.200358   1.119225
##   2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1  0.8458050    1.05630  1.0101523  0.6758794   1.085502  0.9691781  0.9646643
## 2  0.9147803    1.09607  0.9083665  0.6783626   1.306034  1.2706271  0.9792208
##   2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1  1.1245421  0.7654723  0.9191489  0.9074074   1.096939   1.004651   1.236111
## 2  0.8302387  0.6837061  1.1471963  0.9429735   0.838013   1.224227   1.370526
##   2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1   1.074906   1.013937  0.9175258   1.112360   1.060606  1.2190476  0.7604167
## 2   0.952381   1.100000  0.6920821   1.088983   1.336576  0.9723435  1.0733533
##   2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1  1.1746575  0.7317784   1.103586  0.9783394   1.062731   1.017361   1.006826
## 2  0.9776848  0.9971469   0.732475  0.9921875   1.224409   1.033762   1.049767
##   2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1  0.9084746  0.9589552   1.105058  0.8556338  1.0617284  0.8992248   1.051724
## 2  0.9214815  0.8070740   1.370518  0.8299419  0.7565674  1.4189815   1.138662
##   2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02
## 1   1.008197  0.8455285   1.014423  0.7962085   1.208333   1.064039  0.9814815
## 2   0.965616  0.9391691   1.020537  0.6377709   1.148058   1.484144  0.9501425
##   2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09
## 1   1.009434  1.0140187  1.0460829  1.0176211   1.095238  0.9130435  1.1558442
## 2   1.349325  0.9611111  0.8323699  0.8597222   1.300485  1.0645963  0.9778296
##   2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16
## 1  0.7715356  1.1407767   1.004255  0.9194915   1.082949   1.017021  0.9832636
## 2  0.8830549  0.8824324   1.127106  0.7513587   1.280289   1.170904  0.8419783
##   2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23
## 1   1.038298  0.9180328  1.0223214  1.0043668  0.9913043   1.372807  0.8242812
## 2   1.181948  0.8533333  0.7400568  0.7389635  1.3766234   1.356604  0.8567455
##   2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30
## 1  0.9689922   0.900000  0.8888889  1.1700000  0.9700855  0.9779736   1.022523
## 2  0.8961039   1.068841  1.2847458  0.4551451  1.2666667  1.3432494   1.045997
##   2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07
## 1  0.8766520  1.0301508  0.8536585  0.9085714   1.220126   1.293814  0.9482072
## 2  0.8045603  0.8319838  0.9026764  1.5283019   1.000000   1.192240  0.7026627
##   2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14
## 1   0.894958  0.9671362  0.8640777   1.162921  0.9951691   1.038835  0.9252336
## 2   1.469474  0.9097421  0.7748031   1.113821  1.4178832   1.086229  0.6303318
##   2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21
## 1   1.010101  0.9450000   1.243386  0.8680851   1.176471   1.137500  0.9743590
## 2   1.402256  0.9772118   1.013717  0.8971583   1.034691   1.291545  0.9176072
##   2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28
## 1  0.9473684  0.9880952  1.0200803  0.8070866  1.2780488   0.980916  0.9727626
## 2  1.0934809  0.9133858  0.8559113  1.0187050  0.9632768   1.136364  1.0503226
##   2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04
## 1  0.8440000  0.9146919  1.1036269  0.7699531   1.201220   1.147208  1.0044248
## 2  0.9336609  0.8828947  0.8777943  1.0322581   1.248355   1.036891  0.9695044
##   2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11
## 1   1.096916  0.7710843  1.0520833  0.9405941   1.210526   1.000000  0.9739130
## 2   1.041940  1.0377358  0.8993939  0.7250674   1.366171   1.228571  0.8615725
##   2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18
## 1  1.0937500  0.9591837  0.8638298  1.0591133  1.1302326  0.7983539  1.1288660
## 2  0.9935733  0.9288486  0.9623955  0.7221418  0.9799599  1.1370143  0.8129496
##   2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24 2020-11-25
## 1  0.9497717   1.149038  0.7280335  0.9597701   1.113772   1.220430  0.9207048
## 2  1.0730088   1.002062  0.8765432  0.7558685   1.046584   1.192878  1.0497512
##   2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01 2020-12-02
## 1  1.0287081  0.8558140  1.2336957  0.7533040  1.0818713  0.9081081  1.5178571
## 2  0.7819905  0.9969697  0.9696049  0.7241379  0.9047619  1.7081340  0.7507003
##   2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08 2020-12-09
## 1  0.8666667  0.7511312  0.8433735  0.8928571   1.424000  0.6573034   1.282051
## 2  1.1716418  1.0923567  0.7201166  0.8299595   1.121951  1.3086957   1.009967
##   2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15 2020-12-16
## 1  1.0866667   1.006135  0.8963415  0.9115646   1.194030    0.94375  0.9602649
## 2  0.9572368   1.010309  0.8673469  0.6823529   1.327586    1.12987  1.0000000
##   2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22 2020-12-23
## 1  0.9655172   1.135714  0.8930818  1.0070423   1.104895   0.943038  0.9395973
## 2  0.8467433   1.533937  0.7197640  0.8360656   1.127451   1.295652  0.8926174
##   2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29 2020-12-30
## 1  1.1214286  0.8216561  1.3100775  0.9408284   1.006289   1.287500  0.9368932
## 2  0.9172932  1.0655738  0.6615385  1.1860465   1.000000   1.156863  0.8686441
##   2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06
## 1   1.103627  0.9765258  0.9519231  0.9949495   1.050761  1.0048309   1.004808
## 2   1.395122  0.9965035  0.7192982  1.3121951   1.382900  0.8387097   1.317308
##   2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12 2021-01-13
## 1   1.004785  0.9285714  1.0564103  0.9368932   1.051813  1.0394089  0.9526066
## 2   1.313869  0.9166667  0.8626263  0.9695550   1.272947  0.9373814  1.0910931
##   2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19 2021-01-20
## 1   1.039801  0.9377990  1.0408163  0.9215686   1.207447  0.9911894  1.2044444
## 2   1.038961  0.9464286  0.8207547  0.8689655   1.235450  1.2376874  0.7647059
##   2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26 2021-01-27
## 1  0.9520295  1.0193798  0.9543726  0.9840637   1.121457   1.079422   1.130435
## 2  1.2895928  0.9350877  1.0018762  0.7191011   1.281250   1.026423   1.148515
##   2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02 2021-02-03
## 1   1.026627  0.9827089   1.064516  0.9669421  1.0968661   0.974026  1.0560000
## 2   1.013793  1.1190476   0.781155  1.2354086  0.9228346   1.383959  0.9321825
##   2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09 2021-02-10
## 1   1.027778   0.977887  0.9899497   1.035533   1.046569   1.117096  0.9454927
## 2   1.111111   1.119048  0.9000000   1.137116   1.035343   1.006024  0.9850299
##   2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15  2021-02-16 2021-02-17
## 1  0.9933481  1.0044643  1.0066667  0.9713024   2.018182 0.006756757  75.500000
## 2  1.0618034  0.9742366  0.8334966  0.9377203   1.031328 1.171324423   1.054979
##   2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23 2021-02-24
## 1  1.0198675  1.0064935  0.9655914  1.0222717   1.008715  0.9827214  1.0087912
## 2  0.9626352  0.9969356  0.8831967  0.8909513   1.170573  1.1290323  0.9862069
##   2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02 2021-03-03
## 1   1.013072   1.008602  0.9808102   1.015217   1.012848  0.9788584   1.017279
## 2   1.017982   1.002944  0.8258317   1.139810   1.225572  1.1374046   1.050708
##   2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09 2021-03-10
## 1   1.008493  0.9873684  0.9808102  1.0304348  0.9873418   1.006410   1.004246
## 2   1.217885  0.9399767  0.8171110  0.8679818  1.1590909   0.872549   1.152118
##   2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16 2021-03-17
## 1  0.9894292  0.9722222  1.0615385   1.004141  0.9917526  0.9958420   1.020877
## 2  1.1290323  0.9009967  0.8930678   0.877787  1.2530574  0.9864865   1.144597
##   2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23 2021-03-24
## 1  1.0204499   0.995992  1.0241449  0.9882122   1.031809  1.0289017   1.067416
## 2  0.9268617   1.089670  0.8867676  0.8849295   1.115772  0.9684211   1.008540
##   2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30 2021-03-31
## 1   1.029825   1.025554  1.0199336  1.0407166   1.079812   1.043478   1.083333
## 2   1.070054   1.113669  0.7739018  0.9357262   1.115968   1.015987   1.008655
##   2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06 2021-04-07
## 1   1.076923  1.0404762  0.9954233  1.0068966   1.038813   1.018681   1.014024
## 2   1.106084  0.8695346  1.0016221  0.9740891   1.128013   1.033898   1.081254
##   2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13 2021-04-14
## 1   1.009574   1.001054  1.0147368   0.996888   1.012487  1.0082220  1.0030581
## 2   0.909031   1.071066  0.9336493   1.007977   1.176259  0.9443425  0.9080311
##   2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20 2021-04-21
## 1  1.0050813  0.9888777  0.8456033  0.9951632    1.08870  0.9877232  0.9254237
## 2  0.9921541  1.0107836  0.9871977  0.8119597    1.33984  0.9079470  1.0495988
##   2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27 2021-04-28
## 1   0.976801  0.9975000  0.8997494  0.9818942  0.9971631  0.9886202  0.9928058
## 2   1.013899  0.9814942  0.8421788  1.2810945  0.8323625  1.1244168  1.0124481
##   2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04 2021-05-05
## 1  0.9956522  0.9839884  0.9615385  0.9938462  0.9969040  0.9937888   1.007812
## 2  0.9678962  1.0084686  0.8950315  1.0289289  0.9468085  1.0056180   1.158021
##   2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11 2021-05-12
## 1  0.9193798  1.0118044  0.8883333  0.7298311  1.0205656  0.8639798  1.1428571
## 2  0.8518263  0.9781553  0.9057072  0.9059361  0.9858871  1.1789366  0.8542931
##   2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18 2021-05-19
## 1  0.7627551  0.8160535   1.065574  0.9846154   1.179688   1.225166  0.7972973
## 2  1.0751269  0.7204910   1.041940  1.0415094   1.039855   1.259001  1.0322878
##   2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25 2021-05-26
## 1   1.061017   1.172524  0.8991826  0.8575758   1.056537   1.167224  0.8767908
## 2   1.043789   1.151541  0.7561338  0.9754179   1.250000   1.135484  0.8352273
##   2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01 2021-06-02
## 1  0.9346405  0.7062937  0.7722772  1.2115385   1.206349  1.0087719  0.8434783
## 2  0.9863946  1.1931034  0.8193642  0.9656085   1.287671  0.9070922  1.0516028
##   2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08 2021-06-09
## 1   1.020619  0.9242424   1.049180  0.8958333   0.994186   1.064327  0.8681319
## 2   1.072862  0.8870409   1.039844  0.9744553   1.140324   1.068966  0.8798229
##   2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15 2021-06-16
## 1  0.9050633  1.2937063  0.7945946  0.7959184    1.34188  0.9299363   1.130137
## 2  1.2286125  0.9695728  0.8672299  1.0521921    1.03373  0.9513756   1.047075
##   2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22 2021-06-23
## 1   0.769697   1.448819  1.0000000  0.5815217   1.214953   1.453846  0.8148148
## 2   1.057161   1.007290  0.9028951  1.1095524   1.164961   1.013953  0.9531091
##   2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29 2021-06-30
## 1  0.6818182  0.8285714  1.4367816  0.8160000   1.156863   1.211864  0.8251748
## 2  0.9417112  0.9664963  0.9759107  0.9379892   1.060334   1.039952  1.0686845
##   2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06 2021-07-07
## 1  1.2372881  0.9109589  0.7744361   1.174757   0.768595   1.000000  1.6989247
## 2  0.9934641  1.0389254  0.8506596   1.026055   1.195284   1.008093  0.7952835
##   2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13 2021-07-14
## 1  0.7151899  1.2743363  0.6736111  0.8865979   1.697674  0.9726027   0.943662
## 2  1.0757098  0.9483871  0.9616574  0.9581994   1.187919  0.9672316   0.948014
##   2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20 2021-07-21
## 1  0.9776119  1.0152672  0.8721805  1.0172414  1.0508475  0.8709677  1.1851852
## 2  0.8533580  0.9119134  0.9707047  0.9698206  0.9310345  0.9421861  0.9290508
##   2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27 2021-07-28
## 1   1.531250  0.5816327   1.087719  1.0161290   1.412698  0.8202247   1.541096
## 2   1.018576  0.9381966   1.065875  0.8470111   1.181818  0.9443320   1.008574
##   2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03 2021-08-04
## 1  0.7022222   1.088608  0.9418605  0.9320988  0.9933775  1.1333333  0.9647059
## 2  0.9064825   0.898007  0.9569191  0.9645293  1.1386139  0.9751553  1.0840764
##   2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
## 1  1.1036585  1.2044199   0.912844   1.090452  0.9677419   1.047619   1.031818
## 2  0.8437133  0.8300836   0.840604   1.107784  0.9351351   1.146435   0.712605
##   2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18
## 1  0.8678414  1.2385787  0.7950820  0.9639175   1.390374  1.0346154  0.8104089
## 2  1.3089623  0.7513514  0.8992806  0.8160000   1.120915  0.7463557  0.9257812
##   2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25
## 1   1.403670  0.7222222  0.8597285   1.078947   1.409756  0.7508651  0.9953917
## 2   1.164557  0.8188406  0.8849558   0.835000   1.131737  0.8783069  1.1867470
##   2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01
## 1   1.078704  0.9098712  0.8632075  0.9781421   1.145251  0.8439024  0.9595376
## 2   1.076142  0.7924528  1.1071429  1.0161290   0.973545  0.6739130  0.8145161
##   2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08
## 1   1.066265  1.0903955  0.8134715  0.8280255  1.4538462  0.9100529  0.9593023
## 2   1.099010  0.8558559  1.0842105  0.6990291  0.9861111  1.1690141  0.7951807
##   2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15
## 1  0.8666667  0.8811189  1.1666667  0.9047619  0.9849624  0.9312977   1.303279
## 2  1.0454545  0.8985507  0.6612903  1.2195122  1.1600000  1.0172414   1.000000
##   2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22
## 1  0.8364780  0.8796992  0.7008547   1.231707  1.3762376  1.0287770  0.9650350
## 2  0.9830508  1.0862069  0.6825397   1.302326  0.9464286  0.8490566  0.8444444
##   2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28 2021-09-29
## 1  0.7898551  0.9816514  1.0093458  0.6203704   1.343284  1.0444444  0.8085106
## 2  1.1052632  1.1428571  0.8541667  0.9024390   1.324324  0.7959184  0.8461538
##   2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05 2021-10-06
## 1   1.118421   1.082353   1.076087  0.7979798  0.9746835  0.7532468  1.7068966
## 2   1.515152   0.700000   1.171429  1.7560976  0.7222222  0.9423077  0.6326531
##   2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12 2021-10-13
## 1  0.7878788  0.9230769  0.8611111   1.048387  0.9076923  1.6271186  0.8645833
## 2  1.4838710  0.7608696  0.9142857   0.968750  1.3548387  0.8809524  1.0270270
##   2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19 2021-10-20
## 1  0.9518072   0.721519   1.210526  0.8985507   1.080645  1.2388060   1.036145
## 2  0.8421053   0.937500   1.300000  0.8974359   1.114286  0.8205128   0.656250
##   2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26 2021-10-27
## 1   1.232558  0.8301887    0.87500   1.064935   1.487805  0.8360656  1.0098039
## 2   1.190476  1.2800000    0.78125   0.840000   1.428571  0.7000000  0.7619048
##   2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02 2021-11-03
## 1   1.019417  0.9619048  0.9108911   1.130435   1.221154  1.0551181  1.0298507
## 2   1.562500  0.8800000  0.5454545   2.083333   1.200000  0.7666667  0.9130435
##   2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09 2021-11-10
## 1  0.7681159   1.122642   1.042017  0.8306452   1.446602  0.9261745  1.2463768
## 2  0.7619048   1.562500   1.120000  0.8214286   1.086957  0.9600000  0.9583333
##   2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16 2021-11-17
## 1  0.8604651  0.8310811  1.1138211  0.9051095   1.153226  1.0209790   1.020548
## 2  0.8695652  1.2000000  0.9166667  1.1818182   1.000000  0.6153846   1.125000
##   2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23 2021-11-24
## 1  0.9731544  1.0137931   1.020408  0.7866667   1.194915  1.0425532  0.9727891
## 2  1.2222222  0.7272727   0.875000  1.2142857   1.647059  0.4285714  1.7500000
##   2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30 2021-12-01
## 1   1.083916  0.9741935    1.02649  0.9870968    1.03268  0.9936709   1.019108
## 2   1.238095  0.8076923    1.00000  0.0000000         NA  0.5737705   0.600000
##   2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07 2021-12-08
## 1   0.943750  1.0198675   1.032468  0.9559748   1.078947  0.9634146   1.031646
## 2   1.714286  0.6111111   1.045455  1.1739130   1.222222  0.9393939   1.064516
##   2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14 2021-12-15
## 1  0.9754601   1.025157  0.9693252   1.050633   1.018072  0.9881657   0.988024
## 2  0.6060606   1.450000  1.2068966   1.114286   0.000000         NA   0.721519
##   2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21 2021-12-22
## 1  1.0242424  0.9704142  1.0914634  0.9497207   1.041176   1.033898   1.010929
## 2  0.7719298  1.8409091  0.6296296  1.4705882   1.066667   1.150000   1.554348
##   2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28 2021-12-29
## 1   1.010811  1.3262032  1.1250000   1.060932   1.158784   1.069971   1.207084
## 2   1.244755  0.9550562  0.8823529   1.600000   0.825000   1.661616   1.212766
##   2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05
## 1   1.223476  1.3671587   1.124157   1.198079   1.179359   1.440102   1.341003
## 2   1.388471  0.9097473   1.166667   1.035714   1.612479   1.509165   1.515520
##   2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11 2022-01-12
## 1   1.222613   1.148615   1.092419   1.057929   1.051233   1.075039   1.008875
## 2   1.074354   1.096146   1.066163   1.063475   1.228076   1.193864   1.034342
##   2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18 2022-01-19
## 1  0.9954826  0.9847146  0.9718651  1.0034939   0.994280  0.9544772  0.9756289
## 2  1.0736588  0.9995904  0.9254251  0.9969006   1.143016  0.9374393  0.8988601
##   2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25 2022-01-26
## 1  0.8847703  0.9726776  0.9634831  0.9656625  0.9218383  0.9283115  0.8639749
## 2  1.0398893  1.0662971  0.8625494  1.0479749  1.1907062  1.1093509  1.1239986
##   2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01 2022-02-02
## 1  0.8856624  0.8929303  0.8823867  1.0123537  0.9691715  0.8190855   1.007282
## 2  1.0094515  1.0610898  0.8401562  0.9628099  1.0842275  1.0615207   1.024239
##   2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08 2022-02-09
## 1  0.9502008  0.8427726  0.9057172   1.009967   1.012061  0.8873239  0.9474969
## 2  0.9086772  0.9026711  0.8220825   0.952081   1.014650  0.9289707  0.8681374
##   2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15 2022-02-16
## 1  1.0090206  0.8390805  0.9238965   1.009885  0.9804241  0.9101498  0.9104205
## 2  0.9598614  0.8712395  0.7783149   1.006211  1.1296296  0.8454333  0.8850416
##   2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22 2022-02-23
## 1  0.8975904   1.011186  0.9601770   1.018433  0.9411765  0.9471154  0.9263959
## 2  0.7829943   0.898068  0.7559347   1.172718  1.1121339  0.7923251  0.9610636
##   NA
## 1 NA
## 2 NA

# obtain Time Series data
TSconfirmed <- covid19.data("ts-confirmed")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:31:13 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

# explore different combinations of regions/cities/countries
# when combining different locations, heatmaps will also be generated comparing the trends among these locations
growth.rate(TSconfirmed,geo.loc=c("Libya","Egypt","Algeria","Jordan","Saudi Arabia"))

## Processing...  LIBYA

## Processing...  EGYPT

## Processing...  ALGERIA

## Processing...  JORDAN

## Warning in log1p(changes): NaNs produced

## Processing...  SAUDI ARABIA

## $Changes
##        geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27
## 1        LIBYA          0          0          0          0          0
## 2        EGYPT          0          0          0          0          0
## 3      ALGERIA          0          0          0          0          0
## 4       JORDAN          0          0          0          0          0
## 5 SAUDI ARABIA          0          0          0          0          0
##   2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17
## 1          0          0          0          0          0          0          0
## 2          0          0          0          1          0          0          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          1          0
## 3          1          0          0          0          0          0          2
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          1
##   2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
## 1          0          0          0          0          0          0          0
## 2          0          0          1         12          0         34          6
## 3          2          7          0          5          0          2          1
## 4          1          0          0          0          0          0          0
## 5          0          0          4          0          0          6          4
##   2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16
## 1          0          0          0          0          0          0          0
## 2          4          1          7         13         29          1         40
## 3          0          0          4          2         11         11          6
## 4          0          0          0          0          0          7          9
## 5          5          1         24         41         17          0         15
##   2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23
## 1          0          0          0          0          0          0          0
## 2         46          0         60         29          9         33         39
## 3          6         14         13          3         49         62         29
## 4         17         18         17         16          0         27         15
## 5         53          0        103         70         48        119         51
##   2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30
## 1          1          0          0          0          2          5          0
## 2         36         54         39         41         40         33         47
## 3         34         38         65         42         45         57         73
## 4         27         18         40         23         11         13          9
## 5        205        133        112         92         99         96        154
##   2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
## 1          2          0          1          0          7          0          1
## 2         54         69         86        120         85        103        149
## 3        132        131        139        185         80         69        103
## 4          6          4         21         11         13         22          4
## 5        110        157        165        154        140        223        203
##   2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13
## 1          1          1          3          0          0          1          1
## 2        128        110        139         95        145        126        125
## 3         45        104         94         95         64         89         69
## 4          4          5         14          0          9          8          2
## 5        190        137        355        364        382        429        472
##   2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20
## 1          9         13          1          0          0          2          0
## 2        160        155        168        171        188        112        189
## 3         87         90        108        150        116         95         89
## 4          6          4          1          5          6          4          8
## 5        435        493        518        762       1132       1088       1122
##   2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27
## 1          0          8          1          1          0          0          0
## 2        157        169        232        201        227        215        248
## 3         93         99         97        120        129        126        135
## 4          3          7          2          4          3          3          2
## 5       1147       1141       1158       1172       1197       1223       1289
##   2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04
## 1          0          0          0          2          0          0          0
## 2        260        226        269        358        298        272        348
## 3        132        199        158        148        141        179        174
## 4          0          2          2          6          1          1          4
## 5       1266       1325       1351       1344       1362       1552       1645
##   2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11
## 1          0          1          0          0          0          0          0
## 2        388        387        393        495        488        436        346
## 3        190        159        185        187        189        165        168
## 4          6          2         21         14         14         18         22
## 5       1595       1687       1793       1701       1704       1912       1966
##   2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18
## 1          0          0          0          0          1          0          0
## 2        347        338        398        399        491        510        535
## 3        176        186        189        187        192        198        182
## 4         14          6          4         10         11          6         16
## 5       1911       1905       2039       2307       2840       2736       2593
##   2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25
## 1          3          1          2          1          3          0          0
## 2        720        745        774        783        727        752        702
## 3        176        165        186        190        195        193        197
## 4         20         23         12         16          4          4          3
## 5       2509       2691       2532       2642       2442       2399       2235
##   2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01
## 1          2         22          6         13         12         26         12
## 2        789        910       1127       1289       1367       1536       1399
## 3        194        160        140        137        133        127        119
## 4          7          2          8          2          4          5          7
## 5       1931       1815       1644       1581       1618       1877       1881
##   2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08
## 1         14         14         13         30         17          0         76
## 2       1152       1079       1152       1348       1497       1467       1365
## 3        113        107         98        104        115        104        111
## 4          9          2          8         19         11         13         23
## 5       1869       2171       1975       2591       3121       3045       3369
##   2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15
## 1         27         19         15         16          9         36         13
## 2       1385       1455       1442       1577       1677       1618       1691
## 3        117        102        105        109        112        109        112
## 4         14         18         27         25         38          8         18
## 5       3288       3717       3733       3921       3366       4233       4507
##   2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22
## 1         17         16         10         10         24         27         24
## 2       1567       1363       1218       1774       1547       1475       1576
## 3        116        121        117        119        127        140        149
## 4          2          6         14          7          7         18          9
## 5       4267       4919       4757       4301       3941       3379       3393
##   2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29
## 1         44         31         28         15         14         35         40
## 2       1332       1420       1569       1625       1168       1265       1566
## 3        156        172        197        240        283        305        298
## 4          5         24         15         18          7         10          7
## 5       3139       3123       3372       3938       3927       3989       3943
##   2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06
## 1         22         50         17         27         71         57         71
## 2       1557       1503       1485       1412       1324       1218        969
## 3        336        365        385        413        430        441        463
## 4          4          1          3         11          3         14          3
## 5       4387       3402       3383       4193       4128       3580       4207
##   2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13
## 1         65         86         74          0         47         44         79
## 2       1057       1025        950        981        923        912        931
## 3        475        469        460        434        470        483        494
## 4          2          0          0          4          3          3          4
## 5       3392       3036       3183       3159       2994       2779       2852
##   2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20
## 1         51         26         63         52         87         75        114
## 2        929        913        928        703        698        603        627
## 3        527        554        585        593        601        535        607
## 4         15          3          5          3          5          4          5
## 5       2692       2671       2764       2613       2565       2504       2429
##   2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27
## 1        108         88        138        110        123        122        158
## 2        676        667        668        659        511        479        420
## 3        587        594        612        675        605        593        616
## 4       -110          7         11         15          8         14          8
## 5       2476       2331       2238       2378       2201       1968       1993
##   2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03
## 1        190        205        216        183         70        146        226
## 2        465        409        401        321        238        167        157
## 3        642        614        602        563        556        515        507
## 4          6          5          4          2         15          5          5
## 5       1897       1759       1629       1686       1573       1357       1258
##   2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10
## 1        161        251        404        200        153        219        478
## 2        112        123        131        141        167        178        174
## 3        532        551        571        529        538        467        552
## 4          6          7          1          5          9          6         16
## 5       1363       1368       1402       1567       1469       1428       1257
##   2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17
## 1        373        309        439        277        411        434        407
## 2        168        129        145        112        116        139        115
## 3        492        495        488        477        469        450        442
## 4         15         20         17          9         10         39         20
## 5       1521       1569       1482       1383       1413       1227       1372
##   2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24
## 1        489        395        244        414        316          0        572
## 2        163        161        111        123         89        103        138
## 3        419        403        411        409        401        392        398
## 4         40         44         16         34         44         33         30
## 5       1409       1363       1287       1213       1184       1109       1175
##   2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31
## 1        272        553        440        355        329        465        543
## 2        141        206        237        223        212        230        212
## 3        370        391        397        387        378        365        348
## 4         77         40         45         68         24         73         68
## 5       1114       1068       1019       1069        987        910        951
##   2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07
## 1        658        532        617        672        649        655       1085
## 2        176        165        145        157        130        151        178
## 3        339        325        311        304        298        293        289
## 4         63         64         72         68         52         58         67
## 5        898        816        833        822        791        756        768
##   2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14
## 1        749        879        477        969        440        433        734
## 2        187        175        154        151        148        153        168
## 3        285        278        272        264        255        247        242
## 4        103         78         80        206        117        252        214
## 5        781        775        708        687        643        601        607
##   2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21
## 1        629        792        886        616        796        715        847
## 2        163        160        141        131        128        115        126
## 3        238        232        228        219        210        203        197
## 4        149        175        279        213        196        239        266
## 5        672        621        593        576        551        483        492
##   2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28
## 1        650        651        535        658        538        536        849
## 2        113        121        138        112        111        104        115
## 3        191        186        179        175        160        153        146
## 4        634        363        549        620        850        431        734
## 5        552        561        498        472        461        403        455
##   2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05
## 1        801        511        683        509        370        722        628
## 2        124        119        119        149        109        108         98
## 3        155        162        160        157        148        141        134
## 4        823       1776       1276        549       1099        891       1824
## 5        539        418        492        481        419        390        379
##   2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12
## 1       1031       1045        779       1076        318       1026       1109
## 2        121        133        121        106        125        129        132
## 3        129        121        138        146        136        132        253
## 4       1537       1199       1317       1246       1235        928       1147
## 5        477        468        421        407        405        323        348
##   2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19
## 1       1164        836        855       1169          0        945       1159
## 2        139        128        118        126        138        127        123
## 3         74        185        193        221        205        199        214
## 4       2054       2423       2459       1539       1505       1520       1364
## 5        474        501        472        433        359        348        381
##   2020-10-20 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26
## 1        957        719        995        764        990       1639       1210
## 2        158        178        177        170        167        143        167
## 3        213        252        276        273        250        263        276
## 4       2035       2648       2821       2489       1820       2337       1968
## 5        385        405        401        383        395        323        357
##   2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02
## 1        752        899        782        972        467        950        862
## 2        170        153        179        167        179        181        189
## 3        287        320        306        319        291        330        302
## 4       3800       3087       3443       3921       3301       3259       5877
## 5        399        416        435        398        402        374        381
##   2020-11-03 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09
## 1        781        899        853       1004        595       1078        923
## 2        197        207        201        224        208        239        221
## 3        405        548        642        631        581        670        642
## 4       4833       4658       4630       5384       3554       4519       5665
## 5        473        426        450        436        407        363        392
##   2020-11-10 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16
## 1        970        875        919        824          0        974        722
## 2        232        227        214        224        228        220        242
## 3        753        811        851        867        844        860        910
## 4       5996       5419       5685       4469       4750       2373       5861
## 5        471        394        311        441        349        305        301
##   2020-11-17 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23
## 1        612        529        541        802          0       1015        650
## 2        275        329        342        363        358        351        354
## 3       1002       1038       1023       1103       1019       1088       1005
## 4       6454       7933       5469       4940       3826       5268       4981
## 5        362        290        319        286        221        224        231
##   2020-11-24 2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30
## 1        707        617        610        866          0       1157        379
## 2        361        365        368        357        351        358        370
## 3       1133       1025       1085       1058       1044       1009        978
## 4       4586       5025       5000       4580       3108       3598       5123
## 5        252        326        322        302        220        217        232
##   2020-12-01 2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07
## 1        608        670        762        680          0       1051        517
## 2        392        421        432        427        431        418        415
## 3        953        932        843        803        772        750        573
## 4       4187       3591       4029       3116       3160       2576       3980
## 5        263        249        230        234        190        187        209
##   2020-12-08 2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14
## 1        889        536        661        697          0        899        578
## 2        434        421        445        464        478        486        511
## 3        591        598        565        542        517        464        495
## 4       3062       3088       2902       2338       1816       2339       2863
## 5        193        159        141        168        166        139        125
##   2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21
## 1        660        560        706        489          0        788        640
## 2        523        544        548        579        611        664        718
## 3        468        442        426        438        410        422        456
## 4       2547       2561       2221       1708       1283       2152       2499
## 5        142        180        181        174        158        162        168
##   2020-12-22 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28
## 1        506        640        846        461          0        728        532
## 2        788        911       1021       1133       1189       1226       1359
## 3        410        480        458        434        416        392        382
## 4       2444       2091       1707       1616       1050       1590       1802
## 5        181        177        189        178        163        154        119
##   2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04
## 1        437        585        342        467          0        670        561
## 2       1333       1411       1418       1409       1407       1309       1277
## 3        357        323        299        287        262        249        237
## 4       1645       1674       1427       1271        903       1540       1623
## 5        149        113        140        137        101         82         94
##   2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11
## 1        481        424        635        487          0        743        633
## 2       1119       1007       1219       1001        989        993        961
## 3        228        247        262        275        256        231        225
## 4       1472       1553       1215       1092        796       1250       1461
## 5        104        118        108         97        110        117        140
##   2021-01-12 2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18
## 1        652        640        764        583          0       1071        781
## 2        970        996       1022        879        887        890        878
## 3        272        219        267        254        230        222        259
## 4       1176       1122       1075        808        706        957       1030
## 5        147        175        169        173        140        176        170
##   2021-01-19 2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25
## 1        596        659        622        794          0       1148        741
## 2        899        789        752        748        680        674        669
## 3        249        265        246        272        245        227        258
## 4        883        978        776        730        608        934        845
## 5        226        238        212        213        197        186        213
##   2021-01-26 2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01
## 1        870        765        715        871          0        981        771
## 2        643        632        521        589        547        533        541
## 3        243        262        251        277        235        217        239
## 4        943        870       1058        864        641       1181       1207
## 5        223        216        253        267        270        261        255
##   2021-02-02 2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08
## 1       1032        809        770        881          0       1132        856
## 2        521        512        532        540        509        534        567
## 3        263        275        265        248          0        459        225
## 4       1132       1280       1294       1222        865       1299       1685
## 5        310        306        303        327        386        317        356
##   2021-02-09 2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15
## 1        679        467        333        520          0        473        351
## 2        573        610        603        609        600        611        613
## 3        246        223        267        254        210        198        183
## 4       1483       1855       1807       1579       1240       2447       2312
## 5        353        369        364        353        337        322        314
##   2021-02-16 2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22
## 1        331        312        392        585          0        472        415
## 2        633        618        656        610        600        608        623
## 3        175        178        171        182        164        153        177
## 4       2657       2887       1638        867       2200       3917       4550
## 5        322        334        327        337        325        315        327
##   2021-02-23 2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01
## 1        489        561        571        625          0        880        789
## 2        633        644        589        601        588        595        586
## 3        185        182        161        183        155        132        163
## 4       4139       4024       3827       3644       2584       4594       6068
## 5        335        353        356        346        338        322        317
##   2021-03-02 2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08
## 1        840        618       1002        895          0       1158       1018
## 2        581        577        587        579        588        581        591
## 3        175        163        168        187        156        130        148
## 4       5124       5335       5733       4584       3481       6302       7413
## 5        302        331        375        384        382        357        351
##   2021-03-09 2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15
## 1       1030        910       1073        972          0       1350       1087
## 2        622        645        639        639        641        644        631
## 3        161        138        170        157        135        122        145
## 4       7072       6649       8300       7705       4144       8053       9417
## 5        390        386        390        360        351        348        345
##   2021-03-16 2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22
## 1       1041       1054       1032       1134          0       1264        905
## 2        640        645        642        645        644        647        643
## 3        130        148        154        128         96         91         98
## 4       8910       9535       9192       7354       5205       8789       9269
## 5        354        393        381        391        382        367        404
##   2021-03-23 2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29
## 1        901        909        912        884          0        733        696
## 2        648        641        661        670        683        686        689
## 3         94         89        105        114         93         86        110
## 4       9003       9130       8433       6444       4399       7183       7940
## 5        410        466        482        510        502        531        541
##   2021-03-30 2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05
## 1        706        706       1023       1108          0       1206       1148
## 2        693        699        712        703        710        709        767
## 3        115        131        112        125         95         98        117
## 4       7751       6570       6482       4774       4042       6032       6537
## 5        556        585        590        728        684        673        695
##   2021-04-06 2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12
## 1        876        969        869        732          0        937        851
## 2        778        783        789        794        801        812        818
## 3        140        125        112        135        127        138        129
## 4       6005       5232       4775       3794       3145       3340       3565
## 5        792        783        902        904        878        799        842
##   2021-04-13 2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19
## 1        828        541        513        573          0        749        584
## 2        823        831        837        841        845        850        852
## 3        154        176        167        181        163        156        163
## 4       2790       4085       2963       2732       1596       2507       3509
## 5        951        929        985        964        948        916        970
##   2021-04-20 2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26
## 1        625        594        533        536          0        534        467
## 2        855        861        872        884        912        953        991
## 3        187        182        189        199        174        186        190
## 4       2699       3209       2097       1677       1259       1731       2386
## 5       1070       1028       1055       1098       1072        953        958
##   2021-04-27 2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03
## 1        501        447        371        436          0        363        464
## 2       1003       1011       1003       1021       1032       1051       1078
## 3        232        236        286        242        203        211        195
## 4       1815       1910       1552       1556        704        824       1272
## 5       1045       1062       1026       1056       1048        937        953
##   2021-05-04 2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10
## 1        337        255        266        504          0        273        256
## 2       1090       1102       1110       1125       1132       1138       1150
## 3        282        273        201        219        208        204        184
## 4       1530       1220       1007        702        601          0       1765
## 5        999       1016       1090       1039        997        942        986
##   2021-05-11 2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17
## 1        466        253        234          0          0        231        304
## 2       1180       1187       1193       1197       1203       1201       1188
## 3        195        199        207        170        135        117        174
## 4        855          0        901          0          0       1400       1104
## 5        999       1020       1116        927        837        825        886
##   2021-05-18 2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24
## 1        298        338        299        250          0        412        281
## 2       1169       1160       1153       1148       1151       1145       1149
## 3        208        203        260        278        217        209        247
## 4       1174          0          0          0          0          0       5004
## 5       1047       1212       1330       1136       1142       1067       1157
##   2021-05-25 2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31
## 1        340        219        321        343          0        366        595
## 2       1140       1151       1132       1133       1119       1007        984
## 3        254        285        280        272        258        269        188
## 4          0          0       2603        620          0          0       1875
## 5       1389       1320       1183       1215       1106        907       1245
##   2021-06-01 2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07
## 1        296        251        244        386          0        328        404
## 2        956        951        932        861        821        801        782
## 3        305        422        336        385        320        277        325
## 4        750        604        633        494        304        528        655
## 5       1251       1269       1261       1201       1144        984       1161
##   2021-06-08 2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14
## 1        243        229        229        376          0        297        225
## 2        773        765        755        733        711        691        613
## 3        364        387        321        372        343        318        354
## 4        599        598        479        343        310        500        546
## 5       1261       1274          0       2461       1077       1017       1109
##   2021-06-15 2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21
## 1        271        333        258        280          0        322        290
## 2        609        606        591        589        566        532        509
## 3        373        343        382        379        367        235        473
## 4        500        467        522        301        311        502        520
## 5       1269       1239       1309       1236       1153       1079       1212
##   2021-06-22 2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28
## 1        215        223        184        469          0        341        316
## 2        498        466        423        412        409        389        376
## 3        385        370        354        369        341        352        375
## 4        504        599        582        361        273        465        605
## 5       1479       1253       1255       1312       1301       1218       1318
##   2021-06-29 2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05
## 1        452        236        431        418          0        719        782
## 2        261        251        242        198        181        179        175
## 3        389        397        449        475        457        464        495
## 4        497        518        533        458        222        575        584
## 5       1567       1486       1534       1338       1148       1173       1247
##   2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12
## 1       1070       1248       1384       1710          0       2854       2679
## 2        164        161        155        127        121        117        110
## 3        481        585        620        831        813        768        878
## 4        505        646        553        468        406        569        767
## 5       1277       1207       1257       1133       1177       1112       1244
##   2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19
## 1       2640       2604       2555       2866          0       4061       3425
## 2        108         89         81         77         69          0        126
## 3        941        914       1109       1197       1107       1099       1177
## 4        601        734        661        475        389        675          0
## 5       1295       1226       1165       1298       1098       1055       1293
##   2021-07-20 2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26
## 1       1781          0        732       2171          0       3845       3512
## 2         51         49         44         41         38         39         35
## 3       1298       1221       1208       1350       1305       1287       1505
## 4       1195        286        400        331        485       1061       1131
## 5       1273       1142       1162       1247       1256       1194       1252
##   2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02
## 1       3348       3161       2730       2914          0       4322       2892
## 2         31         38         42         45         47         49         51
## 3       1544       1927       1537       1521       1203       1172       1358
## 4       1213       1055       1008        760        562       1041          0
## 5       1379       1334          0          0       3622       1084          0
##   2021-08-03 2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09
## 1       2139       2484       1997       1879          0       3019       2001
## 2         53         57         51         57         61         65         83
## 3       1307       1495       1289       1203       1140       1020        992
## 4       1904        897        697        531        396        986        929
## 5          0          0          0       5121          0          0          0
##   2021-08-10 2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16
## 1       2134       2472       2286       2360          0       2831       2688
## 2         86         91         95         97         99        101        107
## 3        979        844        851        860        753        603        710
## 4        926        511       1012       1010        658       1238       1183
## 5          0          0          0       5439          0          0       1755
##   2021-08-17 2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23
## 1       2276       2325       1949       2364          0       1722       1625
## 2        112        123        131        164        173        184        189
## 3        695        721        694        578        515        412        506
## 4       1066        888        782        721          0       1386        976
## 5          0          0       1115          0        957          0        793
##   2021-08-24 2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30
## 1       1894       1682       1722       1613          0       2003       1678
## 2        194        203        221        234        251        255        263
## 3        537        545        503        512        485        491        412
## 4       1016        812        904          0       1168        811       1098
## 5          0          0          0       1324          0          0          0
##   2021-08-31 2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06
## 1       1501       1665       1479       1388          0       1914       1379
## 2        279        291        303        318        331        343        368
## 3        506        447        388        393        351        345        309
## 4        867        965        893        841        415       1048       1061
## 5          0          0          0          0          0       1925          0
##   2021-09-07 2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13
## 1       1272       1499       1802       1117          0       1443       1291
## 2        378        399        413        433        458        481        491
## 3        332        317        313        285        262        246        233
## 4       1002        975          0       1686          0       1372          0
## 5        262          0        222        102          0          0          0
##   2021-09-14 2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20
## 1       1149       1433       1053        968          0       1121       1081
## 2        503        531        569        588        637        653        679
## 3        227        242        219        235        201        175        166
## 4       2059       1116        904        790        428        920          0
## 5          0          0        507         75          0          0        201
##   2021-09-21 2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27
## 1       1038        985       1006        936          0        989        910
## 2        688        692        722        568        667        680        702
## 3        182        174        161        166        125        148        155
## 4       1945       1028        913        871        438        987       1015
## 5         69         54          0          0        147         44         59
##   2021-09-28 2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04
## 1        686        693        815       1007          0        748        719
## 2        718        738        741        745        761        768        771
## 3        168        153        161        158        140        132        126
## 4       1042       1052       1027        778        548       1037       1222
## 5         50          0         99          0          0          0          0
##   2021-10-05 2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11
## 1        682        692        915        604          0        725        637
## 2        778        788        799        811        831        837        846
## 3        131        125        105        112        102        107         98
## 4       1068       1316       1056        888        567       1072       1307
## 5        223         45         47         48         35         59         58
##   2021-10-12 2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18
## 1        551        724        559        563          0        780        638
## 2        857        861        865        869        874        871        883
## 3         95        110        105        101         93         87         78
## 4       1191       1286       1268       1021        573       1372       1715
## 5         55         57         36         48         45         41         38
##   2021-10-19 2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25
## 1        596        532        436        689          0        745        589
## 2        868        877        883        885        881        886        889
## 3         89         76         70         84         67         72         81
## 4       1597       1232          0       2967        758       1652       1602
## 5         49         47         46         51         43         47         51
##   2021-10-26 2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01
## 1        651        624        596        569          0        683        626
## 2        871        907        923        927        948        933        951
## 3         87         79         91        110         88         94        114
## 4       1746       1692       1892       1454       1022       1723       2120
## 5         65         55         51         56         41         46         49
##   2021-11-02 2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08
## 1        499        556        648        599          0        648        795
## 2        921        951        911        922        909        903        929
## 3         83        105        124        117         84         77         98
## 4       1790       2042       2012       2080       1122       2097       2562
## 5         45         49         45         43         42         40         43
##   2021-11-09 2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15
## 1        609        597        568        593          0        599        562
## 2        921        934        919        909        929          0       1881
## 3        131        124        115        140        109         97        134
## 4       2577       2503       2596       2393       1236          0       5893
## 5         49         38         43         45         44         30         38
##   2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22
## 1        593        408        551        429          0        593        581
## 2        941        960        911        892        902        884        870
## 3        141        135        152        163        144        113        159
## 4          0       3118       7056          0       5532       3579       4324
## 5         37         42         38         35         31         36         39
##   2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29
## 1        595        468        732        600          0        784        638
## 2        856        901        899        913        931        951        911
## 3        172        180        161        193        163        172        192
## 4       4549       4534       4283       4080       2674       4012       5661
## 5         38         34         28         24         29         24         25
##   2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06
## 1        427        574        529        541          0        709        479
## 2        949        938        919        933        892        902        871
## 3        187        192        198        191        185        172        193
## 4       4977       5047       4665          0       7746       4555       5811
## 5         32         34         24         38         29         35         43
##   2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13
## 1        401        509        495        577          0        655        711
## 2        889        909        803        822        879        863          0
## 3        197        188        177        210        218        196        210
## 4       6392       5180       5153       4936       2708       3897       5256
## 5         42         46         45         48         53         51         64
##   2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20
## 1        512        488        648        559          0        726        592
## 2       1621        879        901        910        902        919        903
## 3        230        245        212        299        286        262        243
## 4       4402       4341       3812       3705       1920       3196       3578
## 5         65         88         85         80        116        104        146
##   2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27
## 1        543        561        560        658          0        735        881
## 2        848        879        883        811        866        823        851
## 3        310        285        293        375        278        261        293
## 4       3023       2448       2239       2304       1164       1447       2024
## 5        222        252        287        332        325        389        524
##   2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03
## 1        599        665        640        551          0        916        634
## 2        809        854        871        847        783        801        723
## 3        335        382        390        395        386        341        373
## 4       2403       2220       2045       1845        872       1294       1682
## 5        602        744        752        819        846       1024       1746
##   2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10
## 1        651        698        643        592          0        579        536
## 2        769        803        840        821        830        912        951
## 3        421        462        410        491        426        415        482
## 4       2296       2288       2170       2033       1392       2349       2789
## 5       2585       3045       3168       3575       3068       3460       4778
##   2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17
## 1        487        599        618        765          0        867        736
## 2        932        948       1011       1079       1101       1197       1232
## 3        557        610        577        596        505        573        692
## 4       2930       2829       3362       3396       2387       3493       5039
## 5       4652       5362       5499       5628       5281       5477       5505
##   2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24
## 1        885       1173       1331       1700          0       2281       2333
## 2       1303       1379       1403       1533       1569       1603       1651
## 3        810       1359       1552       1855       2211       2134       2215
## 4       5175       6216       6289       6309       4102       6951      11478
## 5       5873       5928       5591       4884       4608       4535       4838
##   2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31
## 1       3063       2245       3157       3320          0       5694       4429
## 2       1809       1910       1985       2007       2018       2210       2223
## 3       2521       2162       2130       1870       1742       1464       1343
## 4      11813      12826       7691       4224       8170      11833      16226
## 5       4541       4526       4738       4474       3913       3669       4211
##   2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 1       4266       4371       3656       3917          0       4242       2832
## 2       2291       2278       2281       2291       2298       2301       2272
## 3       1403       1365        951        970        792        378        502
## 4      17781      17893      19123      21386      12751      15864      21626
## 5       3861       4092       3852       3555       3013       3260       3747
##   2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 1       3326       3272       3773       3345          0       3648       2800
## 2       2194       2191       2189       2179       2145          0       4260
## 3        610        585        518        532        503        526        413
## 4      22720      21977      21460      18872      14048      17550      22113
## 5       3330       3162       2866       2523       1726       2136       2227
##   2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 1       2490       2884       2457       1208          0       2292       2307
## 2       2117       2101       2071       2053       2025       2009       2003
## 3        405        424        375        316        251        118        147
## 4      18477      17322      15107      12379       7064      10085      11254
## 5       1982       1793       1569       1376        997       1013       1052
##   2022-02-22 2022-02-23
## 1       1815       1373
## 2       1989       1892
## 3        164        123
## 4       9117       8144
## 5        841        627
## 
## $Growth.Rate
##        geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1        LIBYA        NaN        NaN        NaN        NaN        NaN
## 2        EGYPT        NaN        NaN        NaN        NaN        NaN
## 3      ALGERIA        NaN        NaN        NaN        NaN        NaN
## 4       JORDAN        NaN        NaN        NaN        NaN        NaN
## 5 SAUDI ARABIA        NaN        NaN        NaN        NaN        NaN
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN         NA          0        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN         NA
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN         NA          0        NaN
## 3          0        NaN        NaN        NaN        NaN         NA          1
## 4        NaN        NaN        NaN        NaN        NaN        NaN         NA
## 5        NaN        NaN        NaN        NaN        NaN         NA          0
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN         NA         12          0         NA  0.1764706  0.6666667
## 3        3.5          0         NA          0         NA  0.5000000  0.0000000
## 4        0.0        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN         NA          0        NaN         NA  0.6666667  1.2500000
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2       0.25          7   1.857143  2.2307692 0.03448276 40.0000000   1.150000
## 3        NaN         NA   0.500000  5.5000000 1.00000000  0.5454545   1.000000
## 4        NaN        NaN        NaN        NaN         NA  1.2857143   1.888889
## 5       0.20         24   1.708333  0.4146341 0.00000000         NA   3.533333
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1        NaN        NaN        NaN        NaN        NaN        NaN         NA
## 2   0.000000         NA  0.4833333  0.3103448   3.666667  1.1818182  0.9230769
## 3   2.333333  0.9285714  0.2307692 16.3333333   1.265306  0.4677419  1.1724138
## 4   1.058824  0.9444444  0.9411765  0.0000000         NA  0.5555556  1.8000000
## 5   0.000000         NA  0.6796117  0.6857143   2.479167  0.4285714  4.0196078
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1  0.0000000        NaN        NaN         NA   2.500000  0.0000000         NA
## 2  1.5000000  0.7222222  1.0512821  0.9756098   0.825000  1.4242424  1.1489362
## 3  1.1176471  1.7105263  0.6461538  1.0714286   1.266667  1.2807018  1.8082192
## 4  0.6666667  2.2222222  0.5750000  0.4782609   1.181818  0.6923077  0.6666667
## 5  0.6487805  0.8421053  0.8214286  1.0760870   0.969697  1.6041667  0.7142857
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1  0.0000000         NA  0.0000000         NA   0.000000         NA  1.0000000
## 2  1.2777778   1.246377  1.3953488  0.7083333   1.211765  1.4466019  0.8590604
## 3  0.9924242   1.061069  1.3309353  0.4324324   0.862500  1.4927536  0.4368932
## 4  0.6666667   5.250000  0.5238095  1.1818182   1.692308  0.1818182  1.0000000
## 5  1.4272727   1.050955  0.9333333  0.9090909   1.592857  0.9103139  0.9359606
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1  1.0000000  3.0000000  0.0000000        NaN         NA  1.0000000  9.0000000
## 2  0.8593750  1.2636364  0.6834532  1.5263158  0.8689655  0.9920635  1.2800000
## 3  2.3111111  0.9038462  1.0106383  0.6736842  1.3906250  0.7752809  1.2608696
## 4  1.2500000  2.8000000  0.0000000         NA  0.8888889  0.2500000  3.0000000
## 5  0.7210526  2.5912409  1.0253521  1.0494505  1.1230366  1.1002331  0.9216102
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1  1.4444444 0.07692308   0.000000        NaN         NA  0.0000000        NaN
## 2  0.9687500 1.08387097   1.017857  1.0994152  0.5957447  1.6875000  0.8306878
## 3  1.0344828 1.20000000   1.388889  0.7733333  0.8189655  0.9368421  1.0449438
## 4  0.6666667 0.25000000   5.000000  1.2000000  0.6666667  2.0000000  0.3750000
## 5  1.1333333 1.05070994   1.471042  1.4855643  0.9611307  1.0312500  1.0222816
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1         NA  0.1250000  1.0000000   0.000000        NaN        NaN        NaN
## 2   1.076433  1.3727811  0.8663793   1.129353  0.9471366  1.1534884  1.0483871
## 3   1.064516  0.9797980  1.2371134   1.075000  0.9767442  1.0714286  0.9777778
## 4   2.333333  0.2857143  2.0000000   0.750000  1.0000000  0.6666667  0.0000000
## 5   0.994769  1.0148992  1.0120898   1.021331  1.0217210  1.0539657  0.9821567
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
## 2  0.8692308  1.1902655  1.3308550  0.8324022  0.9127517   1.279412  1.1149425
## 3  1.5075758  0.7939698  0.9367089  0.9527027  1.2695035   0.972067  1.0919540
## 4         NA  1.0000000  3.0000000  0.1666667  1.0000000   4.000000  1.5000000
## 5  1.0466035  1.0196226  0.9948187  1.0133929  1.1395007   1.059923  0.9696049
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1         NA   0.000000        NaN        NaN        NaN        NaN        NaN
## 2  0.9974227   1.015504  1.2595420  0.9858586  0.8934426   0.793578  1.0028902
## 3  0.8368421   1.163522  1.0108108  1.0106952  0.8730159   1.018182  1.0476190
## 4  0.3333333  10.500000  0.6666667  1.0000000  1.2857143   1.222222  0.6363636
## 5  1.0576803   1.062833  0.9486893  1.0017637  1.1220657   1.028243  0.9720244
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1        NaN        NaN        NaN         NA  0.0000000        NaN         NA
## 2  0.9740634  1.1775148   1.002513   1.230576  1.0386965  1.0490196  1.3457944
## 3  1.0568182  1.0161290   0.989418   1.026738  1.0312500  0.9191919  0.9670330
## 4  0.4285714  0.6666667   2.500000   1.100000  0.5454545  2.6666667  1.2500000
## 5  0.9968603  1.0703412   1.131437   1.231036  0.9633803  0.9477339  0.9676051
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1  0.3333333  2.0000000   0.500000  3.0000000  0.0000000        NaN         NA
## 2  1.0347222  1.0389262   1.011628  0.9284802  1.0343879  0.9335106  1.1239316
## 3  0.9375000  1.1272727   1.021505  1.0263158  0.9897436  1.0207254  0.9847716
## 4  1.1500000  0.5217391   1.333333  0.2500000  1.0000000  0.7500000  2.3333333
## 5  1.0725389  0.9409142   1.043444  0.9242998  0.9823915  0.9316382  0.8639821
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1 11.0000000  0.2727273  2.1666667  0.9230769  2.1666667  0.4615385  1.1666667
## 2  1.1533587  1.2384615  1.1437445  1.0605120  1.1236284  0.9108073  0.8234453
## 3  0.8247423  0.8750000  0.9785714  0.9708029  0.9548872  0.9370079  0.9495798
## 4  0.2857143  4.0000000  0.2500000  2.0000000  1.2500000  1.4000000  1.2857143
## 5  0.9399275  0.9057851  0.9616788  1.0234029  1.1600742  1.0021311  0.9936204
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1  1.0000000  0.9285714   2.307692  0.5666667  0.0000000         NA  0.3552632
## 2  0.9366319  1.0676552   1.170139  1.1105341  0.9799599  0.9304703  1.0146520
## 3  0.9469027  0.9158879   1.061224  1.1057692  0.9043478  1.0673077  1.0540541
## 4  0.2222222  4.0000000   2.375000  0.5789474  1.1818182  1.7692308  0.6086957
## 5  1.1615837  0.9097190   1.311899  1.2045542  0.9756488  1.1064039  0.9759573
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1  0.7037037  0.7894737  1.0666667  0.5625000  4.0000000  0.3611111  1.3076923
## 2  1.0505415  0.9910653  1.0936200  1.0634115  0.9648181  1.0451174  0.9266706
## 3  0.8717949  1.0294118  1.0380952  1.0275229  0.9732143  1.0275229  1.0357143
## 4  1.2857143  1.5000000  0.9259259  1.5200000  0.2105263  2.2500000  0.1111111
## 5  1.1304745  1.0043045  1.0503616  0.8584545  1.2575758  1.0647295  0.9467495
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1  0.9411765  0.6250000  1.0000000  2.4000000  1.1250000  0.8888889  1.8333333
## 2  0.8698149  0.8936170  1.4564860  0.8720406  0.9534583  1.0684746  0.8451777
## 3  1.0431034  0.9669421  1.0170940  1.0672269  1.1023622  1.0642857  1.0469799
## 4  3.0000000  2.3333333  0.5000000  1.0000000  2.5714286  0.5000000  0.5555556
## 5  1.1528006  0.9670665  0.9041413  0.9162985  0.8573966  1.0041432  0.9251400
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1  0.7045455  0.9032258  0.5357143  0.9333333   2.500000  1.1428571  0.5500000
## 2  1.0660661  1.1049296  1.0356915  0.7187692   1.083048  1.2379447  0.9942529
## 3  1.1025641  1.1453488  1.2182741  1.1791667   1.077739  0.9770492  1.1275168
## 4  4.8000000  0.6250000  1.2000000  0.3888889   1.428571  0.7000000  0.5714286
## 5  0.9949028  1.0797310  1.1678529  0.9972067   1.015788  0.9884683  1.1126046
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1  2.2727273   0.340000  1.5882353  2.6296296  0.8028169  1.2456140  0.9154930
## 2  0.9653179   0.988024  0.9508418  0.9376771  0.9199396  0.7955665  1.0908153
## 3  1.0863095   1.054795  1.0727273  1.0411622  1.0255814  1.0498866  1.0259179
## 4  0.2500000   3.000000  3.6666667  0.2727273  4.6666667  0.2142857  0.6666667
## 5  0.7754730   0.994415  1.2394325  0.9844980  0.8672481  1.1751397  0.8062753
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1  1.3230769  0.8604651  0.0000000         NA  0.9361702   1.795455  0.6455696
## 2  0.9697256  0.9268293  1.0326316  0.9408767  0.9880823   1.020833  0.9978518
## 3  0.9873684  0.9808102  0.9434783  1.0829493  1.0276596   1.022774  1.0668016
## 4  0.0000000        NaN         NA  0.7500000  1.0000000   1.333333  3.7500000
## 5  0.8950472  1.0484190  0.9924599  0.9477683  0.9281897   1.026268  0.9438990
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20  2020-07-21
## 1  0.5098039   2.423077  0.8253968  1.6730769  0.8620690  1.5200000   0.9473684
## 2  0.9827772   1.016429  0.7575431  0.9928876  0.8638968  1.0398010   1.0781499
## 3  1.0512334   1.055957  1.0136752  1.0134907  0.8901830  1.1345794   0.9670511
## 4  0.2000000   1.666667  0.6000000  1.6666667  0.8000000  1.2500000 -22.0000000
## 5  0.9921991   1.034818  0.9453690  0.9816303  0.9762183  0.9700479   1.0193495
##    2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1  0.81481481   1.568182  0.7971014  1.1181818  0.9918699  1.2950820  1.2025316
## 2  0.98668639   1.001499  0.9865269  0.7754173  0.9373777  0.8768267  1.1071429
## 3  1.01192504   1.030303  1.1029412  0.8962963  0.9801653  1.0387858  1.0422078
## 4 -0.06363636   1.571429  1.3636364  0.5333333  1.7500000  0.5714286  0.7500000
## 5  0.94143780   0.960103  1.0625559  0.9255677  0.8941390  1.0127033  0.9518314
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1  1.0789474  1.0536585  0.8472222  0.3825137  2.0857143  1.5479452  0.7123894
## 2  0.8795699  0.9804401  0.8004988  0.7414330  0.7016807  0.9401198  0.7133758
## 3  0.9563863  0.9804560  0.9352159  0.9875666  0.9262590  0.9844660  1.0493097
## 4  0.8333333  0.8000000  0.5000000  7.5000000  0.3333333  1.0000000  1.2000000
## 5  0.9272536  0.9260944  1.0349908  0.9329775  0.8626828  0.9270450  1.0834658
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1   1.559006  1.6095618  0.4950495  0.7650000  1.4313725  2.1826484  0.7803347
## 2   1.098214  1.0650407  1.0763359  1.1843972  1.0658683  0.9775281  0.9655172
## 3   1.035714  1.0362976  0.9264448  1.0170132  0.8680297  1.1820128  0.8913043
## 4   1.166667  0.1428571  5.0000000  1.8000000  0.6666667  2.6666667  0.9375000
## 5   1.003668  1.0248538  1.1176890  0.9374601  0.9720899  0.8802521  1.2100239
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1  0.8284182  1.4207120  0.6309795  1.4837545  1.0559611  0.9377880  1.2014742
## 2  0.7678571  1.1240310  0.7724138  1.0357143  1.1982759  0.8273381  1.4173913
## 3  1.0060976  0.9858586  0.9774590  0.9832285  0.9594883  0.9822222  0.9479638
## 4  1.3333333  0.8500000  0.5294118  1.1111111  3.9000000  0.5128205  2.0000000
## 5  1.0315582  0.9445507  0.9331984  1.0216920  0.8683652  1.1181744  1.0269679
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1  0.8077710  0.6177215  1.6967213  0.7632850  0.0000000         NA  0.4755245
## 2  0.9877301  0.6894410  1.1081081  0.7235772  1.1573034  1.3398058  1.0217391
## 3  0.9618138  1.0198511  0.9951338  0.9804401  0.9775561  1.0153061  0.9296482
## 4  1.1000000  0.3636364  2.1250000  1.2941176  0.7500000  0.9090909  2.5666667
## 5  0.9673527  0.9442406  0.9425019  0.9760923  0.9366554  1.0595131  0.9480851
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1  2.0330882  0.7956600  0.8068182  0.9267606  1.4133739  1.1677419  1.2117864
## 2  1.4609929  1.1504854  0.9409283  0.9506726  1.0849057  0.9217391  0.8301887
## 3  1.0567568  1.0153453  0.9748111  0.9767442  0.9656085  0.9534247  0.9741379
## 4  0.5194805  1.1250000  1.5111111  0.3529412  3.0416667  0.9315068  0.9264706
## 5  0.9587074  0.9541199  1.0490677  0.9232928  0.9219858  1.0450549  0.9442692
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1  0.8085106  1.1597744  1.0891410  0.9657738  1.0092450  1.6564885  0.6903226
## 2  0.9375000  0.8787879  1.0827586  0.8280255  1.1615385  1.1788079  1.0505618
## 3  0.9587021  0.9569231  0.9774920  0.9802632  0.9832215  0.9863481  0.9861592
## 4  1.0158730  1.1250000  0.9444444  0.7647059  1.1153846  1.1551724  1.5373134
## 5  0.9086860  1.0208333  0.9867947  0.9622871  0.9557522  1.0158730  1.0169271
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1  1.1735648  0.5426621  2.0314465  0.4540764  0.9840909  1.6951501  0.8569482
## 2  0.9358289  0.8800000  0.9805195  0.9801325  1.0337838  1.0980392  0.9702381
## 3  0.9754386  0.9784173  0.9705882  0.9659091  0.9686275  0.9797571  0.9834711
## 4  0.7572816  1.0256410  2.5750000  0.5679612  2.1538462  0.8492063  0.6962617
## 5  0.9923175  0.9135484  0.9703390  0.9359534  0.9346812  1.0099834  1.1070840
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1  1.2591415  1.1186869  0.6952596  1.2922078  0.8982412  1.1846154  0.7674144
## 2  0.9815951  0.8812500  0.9290780  0.9770992  0.8984375  1.0956522  0.8968254
## 3  0.9747899  0.9827586  0.9605263  0.9589041  0.9666667  0.9704433  0.9695431
## 4  1.1744966  1.5942857  0.7634409  0.9201878  1.2193878  1.1129707  2.3834586
## 5  0.9241071  0.9549114  0.9713322  0.9565972  0.8765880  1.0186335  1.1219512
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1  1.0015385  0.8218126  1.2299065  0.8176292  0.9962825  1.5839552  0.9434629
## 2  1.0707965  1.1404959  0.8115942  0.9910714  0.9369369  1.1057692  1.0782609
## 3  0.9738220  0.9623656  0.9776536  0.9142857  0.9562500  0.9542484  1.0616438
## 4  0.5725552  1.5123967  1.1293260  1.3709677  0.5070588  1.7030162  1.1212534
## 5  1.0163043  0.8877005  0.9477912  0.9766949  0.8741866  1.1290323  1.1846154
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1  0.6379526  1.3365949  0.7452416  0.7269155  1.9513514  0.8698061  1.6417197
## 2  0.9596774  1.0000000  1.2521008  0.7315436  0.9908257  0.9074074  1.2346939
## 3  1.0451613  0.9876543  0.9812500  0.9426752  0.9527027  0.9503546  0.9626866
## 4  2.1579587  0.7184685  0.4302508  2.0018215  0.8107370  2.0471380  0.8426535
## 5  0.7755102  1.1770335  0.9776423  0.8711019  0.9307876  0.9717949  1.2585752
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1  1.0135790  0.7454545  1.3812580  0.2955390  3.2264151   1.080897  1.0495942
## 2  1.0991736  0.9097744  0.8760331  1.1792453  1.0320000   1.023256  1.0530303
## 3  0.9379845  1.1404959  1.0579710  0.9315068  0.9705882   1.916667  0.2924901
## 4  0.7800911  1.0984153  0.9460896  0.9911717  0.7514170   1.235991  1.7907585
## 5  0.9811321  0.8995726  0.9667458  0.9950860  0.7975309   1.077399  1.3620690
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1  0.7182131  1.0227273  1.3672515  0.0000000         NA  1.2264550  0.8257118
## 2  0.9208633  0.9218750  1.0677966  1.0952381  0.9202899  0.9685039  1.2845528
## 3  2.5000000  1.0432432  1.1450777  0.9276018  0.9707317  1.0753769  0.9953271
## 4  1.1796495  1.0148576  0.6258642  0.9779077  1.0099668  0.8973684  1.4919355
## 5  1.0569620  0.9421158  0.9173729  0.8290993  0.9693593  1.0948276  1.0104987
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1  0.7513062  1.3838665  0.7678392  1.2958115  1.6555556  0.7382550  0.6214876
## 2  1.1265823  0.9943820  0.9604520  0.9823529  0.8562874  1.1678322  1.0179641
## 3  1.1830986  1.0952381  0.9891304  0.9157509  1.0520000  1.0494297  1.0398551
## 4  1.3012285  1.0653323  0.8823112  0.7312174  1.2840659  0.8421053  1.9308943
## 5  1.0519481  0.9901235  0.9551122  1.0313316  0.8177215  1.1052632  1.1176471
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1  1.1954787  0.8698554  1.2429668  0.4804527  2.0342612  0.9073684  0.9060325
## 2  0.9000000  1.1699346  0.9329609  1.0718563  1.0111732  1.0441989  1.0423280
## 3  1.1149826  0.9562500  1.0424837  0.9122257  1.1340206  0.9151515  1.3410596
## 4  0.8123684  1.1153223  1.1388324  0.8418771  0.9872766  1.8033139  0.8223583
## 5  1.0426065  1.0456731  0.9149425  1.0100503  0.9303483  1.0187166  1.2414698
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1  1.1510883  0.9488320  1.1770223  0.5926295  1.8117647  0.8562152   1.050921
## 2  1.0507614  0.9710145  1.1144279  0.9285714  1.1490385  0.9246862   1.049774
## 3  1.3530864  1.1715328  0.9828660  0.9207607  1.1531842  0.9582090   1.172897
## 4  0.9637906  0.9939888  1.1628510  0.6601040  1.2715250  1.2535959   1.058429
## 5  0.9006342  1.0563380  0.9688889  0.9334862  0.8918919  1.0798898   1.201531
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1  0.9020619  1.0502857  0.8966268  0.0000000         NA  0.7412731  0.8476454
## 2  0.9784483  0.9427313  1.0467290  1.0178571  0.9649123  1.1000000  1.1363636
## 3  1.0770252  1.0493218  1.0188014  0.9734717  1.0189573  1.0581395  1.1010989
## 4  0.9037692  1.0490865  0.7861038  1.0628776  0.4995789  2.4698694  1.1011773
## 5  0.8365180  0.7893401  1.4180064  0.7913832  0.8739255  0.9868852  1.2026578
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1  0.8643791  1.0226843  1.4824399  0.0000000         NA  0.6403941  1.0876923
## 2  1.1963636  1.0395137  1.0614035  0.9862259  0.9804469  1.0085470  1.0197740
## 3  1.0359281  0.9855491  1.0782014  0.9238441  1.0677134  0.9237132  1.1273632
## 4  1.2291602  0.6893987  0.9032730  0.7744939  1.3768949  0.9455201  0.9206987
## 5  0.8011050  1.1000000  0.8965517  0.7727273  1.0135747  1.0312500  1.0909091
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1  0.8727016  0.9886548  1.4196721  0.0000000         NA  0.3275713  1.6042216
## 2  1.0110803  1.0082192  0.9701087  0.9831933  1.0199430  1.0335196  1.0594595
## 3  0.9046778  1.0585366  0.9751152  0.9867675  0.9664751  0.9692765  0.9744376
## 4  1.0957261  0.9950249  0.9160000  0.6786026  1.1576577  1.4238466  0.8172946
## 5  1.2936508  0.9877301  0.9378882  0.7284768  0.9863636  1.0691244  1.1336207
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1  1.1019737  1.1373134  0.8923885  0.0000000         NA  0.4919125  1.7195358
## 2  1.0739796  1.0261283  0.9884259  1.0093677  0.9698376  0.9928230  1.0457831
## 3  0.9779643  0.9045064  0.9525504  0.9613948  0.9715026  0.7640000  1.0314136
## 4  0.8576546  1.1219716  0.7733929  1.0141207  0.8151899  1.5450311  0.7693467
## 5  0.9467681  0.9236948  1.0173913  0.8119658  0.9842105  1.1176471  0.9234450
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1  0.6029246  1.2332090  1.0544629  0.0000000         NA  0.6429366  1.1418685
## 2  0.9700461  1.0570071  1.0426966  1.0301724  1.0167364  1.0514403  1.0234834
## 3  1.0118443  0.9448161  0.9592920  0.9538745  0.8974855  1.0668103  0.9454545
## 4  1.0084912  0.9397668  0.8056513  0.7767322  1.2879956  1.2240274  0.8896263
## 5  0.8238342  0.8867925  1.1914894  0.9880952  0.8373494  0.8992806  1.1360000
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1  0.8484848  1.2607143  0.6926346  0.0000000         NA  0.8121827  0.7906250
## 2  1.0401530  1.0073529  1.0565693  1.0552677   1.086743  1.0813253  1.0974930
## 3  0.9444444  0.9638009  1.0281690  0.9360731   1.029268  1.0805687  0.8991228
## 4  1.0054967  0.8672394  0.7690230  0.7511710   1.677319  1.1612454  0.9779912
## 5  1.2676056  1.0055556  0.9613260  0.9080460   1.025316  1.0370370  1.0773810
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1  1.2648221  1.3218750  0.5449173  0.0000000         NA  0.7307692  0.8214286
## 2  1.1560914  1.1207464  1.1096964  1.0494263  1.0311186  1.1084829  0.9808683
## 3  1.1707317  0.9541667  0.9475983  0.9585253  0.9423077  0.9744898  0.9345550
## 4  0.8555646  0.8163558  0.9466901  0.6497525  1.5142857  1.1333333  0.9128746
## 5  0.9779006  1.0677966  0.9417989  0.9157303  0.9447853  0.7727273  1.2521008
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1  1.3386728  0.5846154  1.3654971  0.0000000         NA  0.8373134  0.8573975
## 2  1.0585146  1.0049610  0.9936530  0.9985806  0.9303483  0.9755539  0.8762725
## 3  0.9047619  0.9256966  0.9598662  0.9128920  0.9503817  0.9518072  0.9620253
## 4  1.0176292  0.8524492  0.8906797  0.7104642  1.7054264  1.0538961  0.9069624
## 5  0.7583893  1.2389381  0.9785714  0.7372263  0.8118812  1.1463415  1.1063830
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1  0.8814969  1.4976415  0.7669291  0.0000000         NA  0.8519515  1.0300158
## 2  0.8999106  1.2105263  0.8211649  0.9880120  1.0040445  0.9677744  1.0093652
## 3  1.0833333  1.0607287  1.0496183  0.9309091  0.9023438  0.9740260  1.2088889
## 4  1.0550272  0.7823567  0.8987654  0.7289377  1.5703518  1.1688000  0.8049281
## 5  1.1346154  0.9152542  0.8981481  1.1340206  1.0636364  1.1965812  1.0500000
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1  0.9815951  1.1937500  0.7630890  0.0000000         NA  0.7292250  0.7631242
## 2  1.0268041  1.0261044  0.8600783  1.0091013  1.0033822  0.9865169  1.0239180
## 3  0.8051471  1.2191781  0.9513109  0.9055118  0.9652174  1.1666667  0.9613900
## 4  0.9540816  0.9581105  0.7516279  0.8737624  1.3555241  1.0762800  0.8572816
## 5  1.1904762  0.9657143  1.0236686  0.8092486  1.2571429  0.9659091  1.3294118
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1  1.1057047  0.9438543  1.2765273  0.0000000         NA  0.6454704  1.1740891
## 2  0.8776418  0.9531052  0.9946809  0.9090909  0.9911765  0.9925816  0.9611360
## 3  1.0642570  0.9283019  1.1056911  0.9007353  0.9265306  1.1365639  0.9418605
## 4  1.1075878  0.7934560  0.9407216  0.8328767  1.5361842  0.9047109  1.1159763
## 5  1.0530973  0.8907563  1.0047170  0.9248826  0.9441624  1.1451613  1.0469484
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1  0.8793103  0.9346405  1.2181818  0.0000000         NA  0.7859327  1.3385214
## 2  0.9828927  0.8243671  1.1305182  0.9286927  0.9744059  1.0150094  0.9630314
## 3  1.0781893  0.9580153  1.1035857  0.8483755  0.9234043  1.1013825  1.1004184
## 4  0.9225875  1.2160920  0.8166352  0.7418981  1.8424337  1.0220152  0.9378625
## 5  0.9686099  1.1712963  1.0553360  1.0112360  0.9666667  0.9770115  1.2156863
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1  0.7839147  0.9517923  1.1441558  0.0000000         NA  0.7561837  0.7932243
## 2  0.9827255  1.0390625  1.0150376  0.9425926  1.0491159  1.0617978  1.0105820
## 3  1.0456274  0.9636364  0.9358491  0.0000000         NA  0.4901961  1.0933333
## 4  1.1307420  1.0109375  0.9443586  0.7078560  1.5017341  1.2971517  0.8801187
## 5  0.9870968  0.9901961  1.0792079  1.1804281  0.8212435  1.1230284  0.9915730
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
## 1  0.6877761  0.7130621  1.5615616  0.0000000         NA  0.7420719  0.9430199
## 2  1.0645724  0.9885246  1.0099502  0.9852217  1.0183333  1.0032733  1.0326264
## 3  0.9065041  1.1973094  0.9513109  0.8267717  0.9428571  0.9242424  0.9562842
## 4  1.2508429  0.9741240  0.8738240  0.7853072  1.9733871  0.9448304  1.1492215
## 5  1.0453258  0.9864499  0.9697802  0.9546742  0.9554896  0.9751553  1.0254777
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1  0.9425982  1.2564103   1.492347  0.0000000         NA  0.8792373  1.1783133
## 2  0.9763033  1.0614887   0.929878  0.9836066  1.0133333  1.0246711  1.0160514
## 3  1.0171429  0.9606742   1.064327  0.9010989  0.9329268  1.1568627  1.0451977
## 4  1.0865638  0.5673710   0.529304  2.5374856  1.7804545  1.1616033  0.9096703
## 5  1.0372671  0.9790419   1.030581  0.9643917  0.9692308  1.0380952  1.0244648
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1  1.1472393  1.0178253  1.0945709  0.0000000         NA  0.8965909  1.0646388
## 2  1.0173776  0.9145963  1.0203735  0.9783694  1.0119048  0.9848739  0.9914676
## 3  0.9837838  0.8846154  1.1366460  0.8469945  0.8516129  1.2348485  1.0736196
## 4  0.9722155  0.9510437  0.9521819  0.7091109  1.7778638  1.3208533  0.8444298
## 5  1.0537313  1.0084986  0.9719101  0.9768786  0.9526627  0.9844720  0.9526814
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1  0.7357143   1.621359  0.8932136  0.0000000         NA  0.8791019  1.0117878
## 2  0.9931153   1.017331  0.9863714  1.0155440  0.9880952  1.0172117  1.0524535
## 3  0.9314286   1.030675  1.1130952  0.8342246  0.8333333  1.1384615  1.0878378
## 4  1.0411788   1.074602  0.7995814  0.7593805  1.8103993  1.1762932  0.9539997
## 5  1.0960265   1.132931  1.0240000  0.9947917  0.9345550  0.9831933  1.1111111
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1  0.8834951  1.1791209  0.9058714  0.0000000         NA  0.8051852  0.9576817
## 2  1.0369775  0.9906977  1.0000000  1.0031299  1.0046802  0.9798137  1.0142631
## 3  0.8571429  1.2318841  0.9235294  0.8598726  0.9037037  1.1885246  0.8965517
## 4  0.9401867  1.2483080  0.9283133  0.5378326  1.9432915  1.1693779  0.9461612
## 5  0.9897436  1.0103627  0.9230769  0.9750000  0.9914530  0.9913793  1.0260870
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1   1.012488  0.9791271  1.0988372  0.0000000         NA  0.7159810  0.9955801
## 2   1.007812  0.9953488  1.0046729  0.9984496  1.0046584  0.9938176  1.0077760
## 3   1.138462  1.0405405  0.8311688  0.7500000  0.9479167  1.0769231  0.9591837
## 4   1.070146  0.9640273  0.8000435  0.7077781  1.6885687  1.0546137  0.9713022
## 5   1.110169  0.9694656  1.0262467  0.9769821  0.9607330  1.1008174  1.0148515
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1  1.0088790  1.0033003  0.9692982  0.0000000         NA  0.9495225  1.0143678
## 2  0.9891975  1.0312012  1.0136157  1.0194030  1.0043924  1.0043732  1.0058055
## 3  0.9468085  1.1797753  1.0857143  0.8157895  0.9247312  1.2790698  1.0454545
## 4  1.0141064  0.9236583  0.7641409  0.6826505  1.6328711  1.1053877  0.9761965
## 5  1.1365854  1.0343348  1.0580913  0.9843137  1.0577689  1.0188324  1.0277264
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1  1.0000000  1.4490085  1.0830890  0.0000000         NA  0.9519071  0.7630662
## 2  1.0086580  1.0185980  0.9873596  1.0099573  0.9985915  1.0818054  1.0143416
## 3  1.1391304  0.8549618  1.1160714  0.7600000  1.0315789  1.1938776  1.1965812
## 4  0.8476326  0.9866058  0.7365011  0.8466695  1.4923305  1.0837202  0.9186171
## 5  1.0521583  1.0085470  1.2338983  0.9395604  0.9839181  1.0326895  1.1395683
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1  1.1061644  0.8968008  0.8423475  0.0000000         NA  0.9082177  0.9729730
## 2  1.0064267  1.0076628  1.0063371  1.0088161  1.0137328  1.0073892  1.0061125
## 3  0.8928571  0.8960000  1.2053571  0.9407407  1.0866142  0.9347826  1.1937984
## 4  0.8712739  0.9126529  0.7945550  0.8289404  1.0620032  1.0673653  0.7826087
## 5  0.9886364  1.1519796  1.0022173  0.9712389  0.9100228  1.0538173  1.1294537
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1  0.6533816  0.9482440  1.1169591  0.0000000         NA  0.7797063   1.070205
## 2  1.0097205  1.0072202  1.0047790  1.0047562  1.0059172  1.0023529   1.003521
## 3  1.1428571  0.9488636  1.0838323  0.9005525  0.9570552  1.0448718   1.147239
## 4  1.4641577  0.7253366  0.9220385  0.5841874  1.5708020  1.3996809   0.769165
## 5  0.9768665  1.0602799  0.9786802  0.9834025  0.9662447  1.0589520   1.103093
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1  0.9504000  0.8973064  1.0056285  0.0000000         NA  0.8745318  1.0728051
## 2  1.0070175  1.0127758  1.0137615  1.0316742  1.0449561  1.0398741  1.0121090
## 3  0.9732620  1.0384615  1.0529101  0.8743719  1.0689655  1.0215054  1.2210526
## 4  1.1889589  0.6534746  0.7997139  0.7507454  1.3749007  1.3783940  0.7606873
## 5  0.9607477  1.0262646  1.0407583  0.9763206  0.8889925  1.0052466  1.0908142
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1  0.8922156  0.8299776  1.1752022  0.0000000         NA  1.2782369  0.7262931
## 2  1.0079761  0.9920870  1.0179462  1.0107738   1.018411  1.0256898  1.0111317
## 3  1.0172414  1.2118644  0.8461538  0.8388430   1.039409  0.9241706  1.4461538
## 4  1.0523416  0.8125654  1.0025773  0.4524422   1.170455  1.5436893  1.2028302
## 5  1.0162679  0.9661017  1.0292398  0.9924242   0.894084  1.0170758  1.0482686
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1  0.7566766  1.0431373  1.8947368  0.0000000         NA  0.9377289  1.8203125
## 2  1.0110092  1.0072595  1.0135135  1.0062222  1.0053004  1.0105448  1.0260870
## 3  0.9680851  0.7362637  1.0895522  0.9497717  0.9807692  0.9019608  1.0597826
## 4  0.7973856  0.8254098  0.6971202  0.8561254  0.0000000         NA  0.4844193
## 5  1.0170170  1.0728346  0.9532110  0.9595765  0.9448345  1.0467091  1.0131846
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1  0.5429185  0.9249012  0.0000000        NaN         NA  1.3160173  0.9802632
## 2  1.0059322  1.0050548  1.0033529  1.0050125  0.9983375  0.9891757  0.9840067
## 3  1.0205128  1.0402010  0.8212560  0.7941176  0.8666667  1.4871795  1.1954023
## 4  0.0000000         NA  0.0000000        NaN         NA  0.7885714  1.0634058
## 5  1.0210210  1.0941176  0.8306452  0.9029126  0.9856631  1.0739394  1.1817156
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1  1.1342282  0.8846154  0.8361204  0.0000000         NA  0.6820388  1.2099644
## 2  0.9923011  0.9939655  0.9956635  1.0026132  0.9947871  1.0034934  0.9921671
## 3  0.9759615  1.2807882  1.0692308  0.7805755  0.9631336  1.1818182  1.0283401
## 4  0.0000000        NaN        NaN        NaN        NaN         NA  0.0000000
## 5  1.1575931  1.0973597  0.8541353  1.0052817  0.9343257  1.0843486  1.2005186
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1  0.6441176  1.4657534  1.0685358  0.0000000         NA  1.6256831  0.4974790
## 2  1.0096491  0.9834926  1.0008834  0.9876434  0.8999106  0.9771599  0.9715447
## 3  1.1220472  0.9824561  0.9714286  0.9485294  1.0426357  0.6988848  1.6223404
## 4        NaN         NA  0.2381867  0.0000000        NaN         NA  0.4000000
## 5  0.9503240  0.8962121  1.0270499  0.9102881  0.8200723  1.3726571  1.0048193
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1  0.8479730  0.9721116  1.5819672  0.0000000         NA  1.2317073  0.6014851
## 2  0.9947699  0.9800210  0.9238197  0.9535424  0.9756395  0.9762797  0.9884910
## 3  1.3836066  0.7962085  1.1458333  0.8311688  0.8656250  1.1732852  1.1200000
## 4  0.8053333  1.0480132  0.7804107  0.6153846  1.7368421  1.2405303  0.9145038
## 5  1.0143885  0.9936958  0.9524187  0.9525396  0.8601399  1.1798780  1.0861326
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1  0.9423868  1.0000000  1.6419214  0.0000000         NA  0.7575758  1.2044444
## 2  0.9896507  0.9869281  0.9708609  0.9699864  0.9718706  0.8871201  0.9934747
## 3  1.0631868  0.8294574  1.1588785  0.9220430  0.9271137  1.1132075  1.0536723
## 4  0.9983306  0.8010033  0.7160752  0.9037901  1.6129032  1.0920000  0.9157509
## 5  1.0103093  0.0000000         NA  0.4376270  0.9442897  1.0904621  1.1442741
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1  1.2287823  0.7747748  1.0852713  0.0000000         NA  0.9006211  0.7413793
## 2  0.9950739  0.9752475  0.9966159  0.9609508  0.9399293  0.9567669  0.9783890
## 3  0.9195710  1.1137026  0.9921466  0.9683377  0.6403270  2.0127660  0.8139535
## 4  0.9340000  1.1177730  0.5766284  1.0332226  1.6141479  1.0358566  0.9692308
## 5  0.9763593  1.0564972  0.9442322  0.9328479  0.9358196  1.1232623  1.2202970
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1  1.0372093  0.8251121  2.5489130  0.0000000         NA  0.9266862  1.4303797
## 2  0.9357430  0.9077253  0.9739953  0.9927184  0.9511002  0.9665810  0.6941489
## 3  0.9610390  0.9567568  1.0423729  0.9241192  1.0322581  1.0653409  1.0373333
## 4  1.1884921  0.9716194  0.6202749  0.7562327  1.7032967  1.3010753  0.8214876
## 5  0.8471941  1.0015962  1.0454183  0.9916159  0.9362029  1.0821018  1.1889226
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1  0.5221239  1.8262712  0.9698376  0.0000000         NA  1.0876217  1.3682864
## 2  0.9616858  0.9641434  0.8181818  0.9141414  0.9889503  0.9776536  0.9371429
## 3  1.0205656  1.1309824  1.0579065  0.9621053  1.0153173  1.0668103  0.9717172
## 4  1.0422535  1.0289575  0.8592871  0.4847162  2.5900901  1.0156522  0.8647260
## 5  0.9483089  1.0323015  0.8722295  0.8579970  1.0217770  1.0630861  1.0240577
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1  1.1663551  1.1089744  1.2355491  0.0000000         NA  0.9386826  0.9854423
## 2  0.9817073  0.9627329  0.8193548  0.9527559  0.9669421  0.9401709  0.9818182
## 3  1.2162162  1.0598291  1.3403226  0.9783394  0.9446494  1.1432292  1.0717540
## 4  1.2792079  0.8560372  0.8462929  0.8675214  1.4014778  1.3479789  0.7835724
## 5  0.9451840  1.0414250  0.9013524  1.0388350  0.9447749  1.1187050  1.0409968
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1  0.9863636  0.9811828  1.1217221  0.0000000         NA  0.8433883  0.5200000
## 2  0.8240741  0.9101124  0.9506173  0.8961039  0.0000000         NA  0.4047619
## 3  0.9713071  1.2133479  1.0793508  0.9248120  0.9927733  1.0709736  1.1028037
## 4  1.2212978  0.9005450  0.7186082  0.8189474  1.7352185  0.0000000         NA
## 5  0.9467181  0.9502447  1.1141631  0.8459168  0.9608379  1.2255924  0.9845321
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1  0.0000000         NA  2.9658470  0.0000000         NA  0.9133940  0.9533030
## 2  0.9607843  0.8979592  0.9318182  0.9268293  1.0263158  0.8974359  0.8857143
## 3  0.9406780  0.9893530  1.1175497  0.9666667  0.9862069  1.1693862  1.0259136
## 4  0.2393305  1.3986014  0.8275000  1.4652568  2.1876289  1.0659755  1.0725022
## 5  0.8970935  1.0175131  1.0731497  1.0072173  0.9506369  1.0485762  1.1014377
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1  0.9441458  0.8636507  1.0673993  0.0000000         NA  0.6691347  0.7396266
## 2  1.2258065  1.1052632  1.0714286  1.0444444  1.0425532  1.0408163  1.0392157
## 3  1.2480570  0.7976129  0.9895901  0.7909270  0.9742311  1.1587031  0.9624448
## 4  0.8697444  0.9554502  0.7539683  0.7394737  1.8523132  0.0000000         NA
## 5  0.9673677  0.0000000        NaN         NA  0.2992822  0.0000000        NaN
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1  1.1612903  0.8039452  0.9409114  0.0000000         NA  0.6628023  1.0664668
## 2  1.0754717  0.8947368  1.1176471  1.0701754  1.0655738  1.2769231  1.0361446
## 3  1.1438409  0.8622074  0.9332816  0.9476309  0.8947368  0.9725490  0.9868952
## 4  0.4711134  0.7770346  0.7618364  0.7457627  2.4898990  0.9421907  0.9967707
## 5        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1  1.1583880  0.9247573  1.0323710  0.0000000         NA  0.9494878  0.8467262
## 2  1.0581395  1.0439560  1.0210526  1.0206186  1.0202020  1.0594059  1.0467290
## 3  0.8621042  1.0082938  1.0105758  0.8755814  0.8007968  1.1774461  0.9788732
## 4  0.5518359  1.9804305  0.9980237  0.6514851  1.8814590  0.9555735  0.9010989
## 5        NaN        NaN         NA  0.0000000        NaN         NA  0.0000000
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1  1.0215290  0.8382796  1.2129297  0.0000000         NA  0.9436702   1.165538
## 2  1.0982143  1.0650407  1.2519084  1.0548780   1.063584  1.0271739   1.026455
## 3  1.0374101  0.9625520  0.8328530  0.8910035   0.800000  1.2281553   1.061265
## 4  0.8330206  0.8806306  0.9219949  0.0000000         NA  0.7041847   1.040984
## 5        NaN         NA  0.0000000         NA   0.000000         NA   0.000000
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1  0.8880676  1.0237812  0.9367015  0.0000000         NA  0.8377434  0.8945173
## 2  1.0463918  1.0886700  1.0588235  1.0726496  1.0159363  1.0313725  1.0608365
## 3  1.0148976  0.9229358  1.0178926  0.9472656  1.0123711  0.8391039  1.2281553
## 4  0.7992126  1.1133005  0.0000000         NA  0.6943493  1.3538841  0.7896175
## 5        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1  1.1092605  0.8882883  0.9384719  0.0000000         NA  0.7204807  0.9224075
## 2  1.0430108  1.0412371  1.0495050  1.0408805   1.036254  1.0728863  1.0271739
## 3  0.8833992  0.8680089  1.0128866  0.8931298   0.982906  0.8956522  1.0744337
## 4  1.1130334  0.9253886  0.9417693  0.4934602   2.525301  1.0124046  0.9443921
## 5        NaN        NaN        NaN        NaN         NA  0.0000000         NA
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1  1.1784591  1.2021348  0.6198668  0.0000000         NA  0.8946639  0.8900077
## 2  1.0555556  1.0350877  1.0484262  1.0577367  1.0502183  1.0207900  1.0244399
## 3  0.9548193  0.9873817  0.9105431  0.9192982  0.9389313  0.9471545  0.9742489
## 4  0.9730539  0.0000000         NA  0.0000000         NA  0.0000000         NA
## 5  0.0000000         NA  0.4594595  0.0000000        NaN        NaN        NaN
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1  1.2471715  0.7348221  0.9192783  0.0000000         NA  0.9643176  0.9602220
## 2  1.0556660  1.0715631  1.0333919  1.0833333  1.0251177  1.0398162  1.0132548
## 3  1.0660793  0.9049587  1.0730594  0.8553191  0.8706468  0.9485714  1.0963855
## 4  0.5420107  0.8100358  0.8738938  0.5417722  2.1495327  0.0000000         NA
## 5        NaN         NA  0.1479290  0.0000000        NaN         NA  0.3432836
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1  0.9489403  1.0213198  0.9304175  0.0000000         NA  0.9201213  0.7538462
## 2  1.0058140  1.0433526  0.7867036  1.1742958  1.0194903  1.0323529  1.0227920
## 3  0.9560440  0.9252874  1.0310559  0.7530120  1.1840000  1.0472973  1.0838710
## 4  0.5285347  0.8881323  0.9539978  0.5028703  2.2534247  1.0283688  1.0266010
## 5  0.7826087  0.0000000        NaN         NA  0.2993197  1.3409091  0.8474576
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1  1.0102041  1.1760462  1.2355828  0.0000000         NA  0.9612299  0.9485396
## 2  1.0278552  1.0040650  1.0053981  1.0214765  1.0091984  1.0039062  1.0090791
## 3  0.9107143  1.0522876  0.9813665  0.8860759  0.9428571  0.9545455  1.0396825
## 4  1.0095969  0.9762357  0.7575463  0.7043702  1.8923358  1.1783992  0.8739771
## 5  0.0000000         NA  0.0000000        NaN        NaN        NaN         NA
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1  1.0146628  1.3222543  0.6601093  0.0000000         NA  0.8786207  0.8649922
## 2  1.0128535  1.0139594  1.0150188  1.0246609   1.007220  1.0107527  1.0130024
## 3  0.9541985  0.8400000  1.0666667  0.9107143   1.049020  0.9158879  0.9693878
## 4  1.2322097  0.8024316  0.8409091  0.6385135   1.890653  1.2192164  0.9112471
## 5  0.2017937  1.0444444  1.0212766  0.7291667   1.685714  0.9830508  0.9482759
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1   1.313975  0.7720994  1.0071556  0.0000000         NA  0.8179487  0.9341693
## 2   1.004667  1.0046458  1.0046243  1.0057537  0.9965675  1.0137773  0.9830125
## 3   1.157895  0.9545455  0.9619048  0.9207921  0.9354839  0.8965517  1.1410256
## 4   1.079765  0.9860031  0.8052050  0.5612145  2.3944154  1.2500000  0.9311953
## 5   1.036364  0.6315789  1.3333333  0.9375000  0.9111111  0.9268293  1.2894737
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1  0.8926174  0.8195489   1.580275  0.0000000         NA  0.7906040  1.1052632
## 2  1.0103687  1.0068415   1.002265  0.9954802   1.005675  1.0033860  0.9797525
## 3  0.8539326  0.9210526   1.200000  0.7976190   1.074627  1.1250000  1.0740741
## 4  0.7714465  0.0000000         NA  0.2554769   2.179420  0.9697337  1.0898876
## 5  0.9591837  0.9787234   1.108696  0.8431373   1.093023  1.0851064  1.2745098
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1  0.9585253  0.9551282  0.9546980  0.0000000         NA  0.9165447  0.7971246
## 2  1.0413318  1.0176406  1.0043337  1.0226537  0.9841772  1.0192926  0.9684543
## 3  0.9080460  1.1518987  1.2087912  0.8000000  1.0681818  1.2127660  0.7280702
## 4  0.9690722  1.1182033  0.7684989  0.7028886  1.6859100  1.2304121  0.8443396
## 5  0.8461538  0.9272727  1.0980392  0.7321429  1.1219512  1.0652174  0.9183673
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1   1.114228  1.1654676  0.9243827  0.0000000         NA   1.226852  0.7660377
## 2   1.032573  0.9579390  1.0120746  0.9859002  0.9933993   1.028793  0.9913886
## 3   1.265060  1.1809524  0.9435484  0.7179487  0.9166667   1.272727  1.3367347
## 4   1.140782  0.9853085  1.0337972  0.5394231  1.8689840   1.221745  1.0058548
## 5   1.088889  0.9183673  0.9555556  0.9767442  0.9523810   1.075000  1.1395349
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1  0.9802956  0.9514238  1.0440141  0.0000000         NA  0.9382304  1.0551601
## 2  1.0141151  0.9839400  0.9891186  1.0220022  0.0000000         NA  0.5002658
## 3  0.9465649  0.9274194  1.2173913  0.7785714  0.8899083  1.3814433  1.0522388
## 4  0.9712844  1.0371554  0.9218028  0.5165065  0.0000000         NA  0.0000000
## 5  0.7755102  1.1315789  1.0465116  0.9777778  0.6818182  1.2666667  0.9736842
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1  0.6880270  1.3504902  0.7785844  0.0000000         NA  0.9797639   1.024096
## 2  1.0201913  0.9489583  0.9791438  1.0112108  0.9800443  0.9841629   0.983908
## 3  0.9574468  1.1259259  1.0723684  0.8834356  0.7847222  1.4070796   1.081761
## 4         NA  2.2629891  0.0000000         NA  0.6469631  1.2081587   1.052035
## 5  1.1351351  0.9047619  0.9210526  0.8857143  1.1612903  1.0833333   0.974359
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1  0.7865546  1.5641026  0.8196721  0.0000000         NA  0.8137755  0.6692790
## 2  1.0525701  0.9977802  1.0155729  1.0197152  1.0214823  0.9579390  1.0417124
## 3  1.0465116  0.8944444  1.1987578  0.8445596  1.0552147  1.1162791  0.9739583
## 4  0.9967026  0.9446405  0.9526033  0.6553922  1.5003740  1.4110169  0.8791733
## 5  0.8947368  0.8235294  0.8571429  1.2083333  0.8275862  1.0416667  1.2800000
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1  1.3442623  0.9216028  1.0226843  0.0000000         NA  0.6755994  0.8371608
## 2  0.9884089  0.9797441  1.0152339  0.9560557  1.0112108  0.9656319  1.0206659
## 3  1.0267380  1.0312500  0.9646465  0.9685864  0.9297297  1.1220930  1.0207254
## 4  1.0140647  0.9243115  0.0000000         NA  0.5880454  1.2757409  1.0999828
## 5  1.0625000  0.7058824  1.5833333  0.7631579  1.2068966  1.2285714  0.9767442
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1  1.2693267  0.9724951  1.1656566  0.0000000         NA   1.085496  0.7201125
## 2  1.0224972  0.8833883  1.0236613  1.0693431  0.9817975   0.000000         NA
## 3  0.9543147  0.9414894  1.1864407  1.0380952  0.8990826   1.071429  1.0952381
## 4  0.8103880  0.9947876  0.9578886  0.5486224  1.4390694   1.348730  0.8375190
## 5  1.0952381  0.9782609  1.0666667  1.1041667  0.9622642   1.254902  1.0156250
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1  0.9531250  1.3278689  0.8626543  0.0000000         NA  0.8154270  0.9172297
## 2  0.5422579  1.0250284  1.0099889  0.9912088  1.0188470  0.9825898  0.9390919
## 3  1.0652174  0.8653061  1.4103774  0.9565217  0.9160839  0.9274809  1.2757202
## 4  0.9861427  0.8781387  0.9719307  0.5182186  1.6645833  1.1195244  0.8448854
## 5  1.3538462  0.9659091  0.9411765  1.4500000  0.8965517  1.4038462  1.5205479
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1  1.0331492  0.9982175  1.1750000  0.0000000         NA   1.198639  0.6799092
## 2  1.0365566  1.0045506  0.9184598  1.0678175  0.9503464   1.034022  0.9506463
## 3  0.9193548  1.0280702  1.2798635  0.7413333  0.9388489   1.122605  1.1433447
## 4  0.8097916  0.9146242  1.0290308  0.5052083  1.2431271   1.398756  1.1872530
## 5  1.1351351  1.1388889  1.1567944  0.9789157  1.1969231   1.347044  1.1488550
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1  1.1101836  0.9624060  0.8609375  0.0000000         NA  0.6921397   1.026814
## 2  1.0556242  1.0199063  0.9724455  0.9244392  1.0229885  0.9026217   1.063624
## 3  1.1402985  1.0209424  1.0128205  0.9772152  0.8834197  1.0938416   1.128686
## 4  0.9238452  0.9211712  0.9022005  0.4726287  1.4839450  1.2998454   1.365042
## 5  1.2358804  1.0107527  1.0890957  1.0329670  1.2104019  1.7050781   1.480527
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1  1.0721966  0.9212034  0.9206843  0.0000000         NA   0.925734  0.9085821
## 2  1.0442133  1.0460772  0.9773810  1.0109622  1.0987952   1.042763  0.9800210
## 3  1.0973872  0.8874459  1.1975610  0.8676171  0.9741784   1.161446  1.1556017
## 4  0.9965157  0.9484266  0.9368664  0.6847024  1.6875000   1.187314  1.0505558
## 5  1.1779497  1.0403941  1.1284722  0.8581818  1.1277705   1.380925  0.9736291
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1   1.229979  1.0317195   1.237864  0.0000000         NA  0.8489043   1.202446
## 2   1.017167  1.0664557   1.067260  1.0203892   1.087193  1.0292398   1.057630
## 3   1.095153  0.9459016   1.032929  0.8473154   1.134653  1.2076789   1.170520
## 4   0.965529  1.1884058   1.010113  0.7028857   1.463343  1.4425995   1.026989
## 5   1.152623  1.0255502   1.023459  0.9383440   1.037114  1.0051123   1.066848
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1   1.325424  1.1346974  1.2772352  0.0000000         NA   1.022797   1.312902
## 2   1.058327  1.0174039  1.0926586  1.0234834  1.0216699   1.029944   1.095700
## 3   1.677778  1.1420162  1.1952320  1.1919137  0.9651741   1.037957   1.138149
## 4   1.201159  1.0117439  1.0031802  0.6501823  1.6945392   1.651273   1.029186
## 5   1.009365  0.9431511  0.8735468  0.9434889  0.9841580   1.066814   0.938611
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1  0.7329416  1.4062361  1.0516313  0.0000000         NA  0.7778363  0.9631971
## 2  1.0558320  1.0392670  1.0110831  1.0054808  1.0951437  1.0058824  1.0305893
## 3  0.8575962  0.9851989  0.8779343  0.9315508  0.8404133  0.9173497  1.0446761
## 4  1.0857530  0.5996414  0.5492134  1.9341856  1.4483476  1.3712499  1.0958338
## 5  0.9966968  1.0468405  0.9442803  0.8746089  0.9376438  1.1477242  0.9168844
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1  1.0246132  0.8364219  1.0713895  0.0000000         NA  0.6676096   1.174435
## 2  0.9943256  1.0013169  1.0043840  1.0030554  1.0013055  0.9873968   0.965669
## 3  0.9729152  0.6967033  1.0199790  0.8164948  0.4772727  1.3280423   1.215139
## 4  1.0062989  1.0687420  1.1183392  0.5962312  1.2441377  1.3632123   1.050587
## 5  1.0598291  0.9413490  0.9228972  0.8475387  1.0819781  1.1493865   0.888711
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1  0.9837643  1.1531174  0.8865624  0.0000000         NA  0.7675439  0.8892857
## 2  0.9986326  0.9990872  0.9954317  0.9843965   0.000000         NA  0.4969484
## 3  0.9590164  0.8854701  1.0270270  0.9454887   1.045726  0.7851711  0.9806295
## 4  0.9672975  0.9764754  0.8794035  0.7443832   1.249288  1.2600000  0.8355718
## 5  0.9495495  0.9063884  0.8803210  0.6841062   1.237543  1.0426030  0.8899865
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1  1.1582329  0.8519417  0.4916565  0.0000000         NA  1.0065445  0.7867360
## 2  0.9924421  0.9857211  0.9913085  0.9863614  0.9920988  0.9970134  0.9930105
## 3  1.0469136  0.8844340  0.8426667  0.7943038  0.4701195  1.2457627  1.1156463
## 4  0.9374899  0.8721279  0.8194215  0.5706438  1.4276614  1.1159147  0.8101120
## 5  0.9046418  0.8750697  0.8769917  0.7245640  1.0160481  1.0384995  0.7994297
##   2022-02-23 NA
## 1  0.7564738 NA
## 2  0.9512318 NA
## 3  0.7500000 NA
## 4  0.8932763 NA
## 5  0.7455410 NA

growth.rate(TSconfirmed,geo.loc=c("Egypt","Libra","SJordan","United Arab Emirates","Qatar","Kuwait","Yemen","Lebnon"))

## Warning in checkGeoLoc(data0, geo.loc): Unrecognized region: Libra will skip it!

## Warning in checkGeoLoc(data0, geo.loc): Unrecognized region: SJordan will skip
## it!

## Warning in checkGeoLoc(data0, geo.loc): Unrecognized region: Lebnon will skip
## it!

## Processing...  EGYPT

## Processing...  UNITED ARAB EMIRATES

## Processing...  QATAR

## Processing...  KUWAIT

## Processing...  YEMEN

## $Changes
##                geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27
## 1                EGYPT          0          0          0          0          0
## 2 UNITED ARAB EMIRATES          0          0          0          0          0
## 3                QATAR          0          0          0          0          0
## 4               KUWAIT          0          0          0          0          0
## 5                YEMEN          0          0          0          0          0
##   2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03
## 1          0          0          0          0          0          0          0
## 2          0          4          0          0          0          1          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          2          0          1
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17
## 1          0          0          0          1          0          0          0
## 2          0          0          0          0          0          1          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          0
## 5          0          0          0          0          0          0          0
##   2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          4          0          0
## 3          0          0          0          0          0          0          0
## 4          0          0          0          0          0          0          1
## 5          0          0          0          0          0          0          0
##   2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02
## 1          0          0          0          0          0          1          0
## 2          0          0          0          6          2          0          0
## 3          0          0          0          0          1          2          0
## 4         10         15         17          2          0          0         11
## 5          0          0          0          0          0          0          0
##   2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
## 1          0          0          1         12          0         34          6
## 2          6          0          2          0         16          0          0
## 3          4          1          0          0          0          7          3
## 4          0          0          2          0          3          3          0
## 5          0          0          0          0          0          0          0
##   2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16
## 1          4          1          7         13         29          1         40
## 2         29          0         11          0          0         13          0
## 3          6        238          0         58         17         64         38
## 4          5          3          8          0         24          8         11
## 5          0          0          0          0          0          0          0
##   2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23
## 1         46          0         60         29          9         33         39
## 2          0         15         27          0         13          0         45
## 3          0         13          8         10         11         13          7
## 4          7         12          6         11         17         12          1
## 5          0          0          0          0          0          0          0
##   2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30
## 1         36         54         39         41         40         33         47
## 2         50         85          0         72         63        102         41
## 3         25         11         12         13         28         44         59
## 4          2          4         13         17         10         20         11
## 5          0          0          0          0          0          0          0
##   2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
## 1         54         69         86        120         85        103        149
## 2         53        150        210        240        241        294        277
## 3         88         54        114        126        250        279        228
## 4         23         28         25         75         62         77        109
## 5          0          0          0          0          0          0          0
##   2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13
## 1        128        110        139         95        145        126        125
## 2        283        300        331        370        376        387        398
## 3        225        153        166        136        216        251        252
## 4         78        112         55         83        161         80         66
## 5          0          0          0          1          0          0          0
##   2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20
## 1        160        155        168        171        188        112        189
## 2        412        432        460        477          0        479        484
## 3        197        283        392        560        345        440        567
## 4         55         50        119        134         93        164         80
## 5          0          0          0          0          0          0          0
##   2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27
## 1        157        169        232        201        227        215        248
## 2        490        483        518        525        532        536        490
## 3        518        608        623        761        833        929        957
## 4         85        168        151        215        278        183        213
## 5          0          0          0          0          0          0          0
##   2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04
## 1        260        226        269        358        298        272        348
## 2        541        549        552        557        561        564        567
## 3        677        643        845        687        776        679        640
## 4        152        300        284        353        242        364        295
## 5          0          5          0          1          3          0          2
##   2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11
## 1        388        387        393        495        488        436        346
## 2        462        546        502        553        624        781        680
## 3        951        830        918       1311       1130       1189       1103
## 4        526        485        278        641        415       1065        598
## 5          9          4          0          9          0         17          5
##   2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18
## 1        347        338        398        399        491        510        535
## 2        783        725        698        747        796        731        832
## 3       1526       1390       1733       1153       1547       1632       1365
## 4        991        751        947        885        942       1048        841
## 5          9          5         15         21         16          6          2
##   2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25
## 1        720        745        774        783        727        752        702
## 2        873        941        894        994        812        781        822
## 3       1637       1491       1554       1830       1732       1501       1751
## 4       1073        804       1041        955        900        838        665
## 5         37         13         13         12          7         10         11
##   2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01
## 1        789        910       1127       1289       1367       1536       1399
## 2        779        883        563        638        726        661        635
## 3       1742       1740       1967       1993       2355       1648       1523
## 4        608        692        845       1072       1008        851        719
## 5         16          6         23          5         27         13         31
##   2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08
## 1       1152       1079       1152       1348       1497       1467       1365
## 2        596        571        659        624        626        540        568
## 3       1826       1901       1581       1754       1700       1595       1368
## 4        887        710        562        723        487        717        662
## 5         45         20         34         16         13          2         12
##   2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15
## 1       1385       1455       1442       1577       1677       1618       1691
## 2        528        603        479        513        491        304        342
## 3       1721       1716       1476       1517       1828       1186       1274
## 4        630        683        609        520        514        454        511
## 5         28         36         31         41         73         23        116
##   2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22
## 1       1567       1363       1218       1774       1547       1475       1576
## 2        346        382        388        393        388        392        378
## 3       1201       1097       1267       1021       1026        881       1034
## 4        527        575        541        604        467        505        641
## 5         41         17          7         10          3         19         26
##   2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29
## 1       1332       1420       1569       1625       1168       1265       1566
## 2        380        450        430        410        387        437        449
## 3       1176       1199       1060        946        879        750        693
## 4        742        846        909        915        688        551        582
## 5         25         23         61         13         14         15         10
##   2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06
## 1       1557       1503       1485       1412       1324       1218        969
## 2        421        402        400        672        716        683        528
## 3        982        915        894        756        530        616        546
## 4        671        745        919        813        631        638        703
## 5         30         32         31         19          8         17         25
##   2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13
## 1       1057       1025        950        981        923        912        931
## 2        532        445        532        473        403        401        344
## 3        600        608        557        520        498        470        418
## 4        601        762        833        740        478        836        614
## 5          7         21         38         24          9         76         33
##   2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20
## 1        929        913        928        703        698        603        627
## 2        375        275        281        293        289        211        271
## 3        517        450        494        421        410        340        389
## 4        666        703        791        553        683        300        559
## 5         18         10         26         24          5         25         13
##   2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27
## 1        676        667        668        659        511        479        420
## 2        305        236        254        261        313        351        264
## 3        393        441        373        394        398        269        292
## 4        671        751        687        753        684        464        606
## 5         10         11         14         20          0          7         10
##   2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03
## 1        465        409        401        321        238        167        157
## 2        369        375        302        283        254        239        164
## 3        283        273        307        235        216        196        215
## 4        770        754        626        428        491        463        388
## 5         12          8         15          2          2          4          0
##   2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10
## 1        112        123        131        141        167        178        174
## 2        189        254        239        216        239        225        179
## 3        216        267        287        291        267        297        315
## 4        475        651        620        682        472        514        687
## 5         26          3          5         28          1          7         28
##   2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17
## 1        168        129        145        112        116        139        115
## 2        262        246        277        330        283        210        229
## 3        384        292        343        251        277        271        288
## 4        668        717        701        699        512        508        622
## 5         -1         10          6         11          0         11          0
##   2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24
## 1        163        161        111        123         89        103        138
## 2        365        435        461        391        424        390        275
## 3        293        295        268        257        284        243        258
## 4        643        675        622        502        688        571        432
## 5         20          3          7          7          1          4          5
##   2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31
## 1        141        206        237        223        212        230        212
## 2        339        399        491        390        427        362        541
## 3        232        244        246        208        211        168        203
## 4        613        698        674        633        646        412        473
## 5          8          6          3         10          3          7          5
##   2020-09-01 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07
## 1        176        165        145        157        130        151        178
## 2        574        735        614        612        705        513        470
## 3        216        212        214        217        227        231        253
## 4        702        667        900        865        720        619        805
## 5          4         14          3          4          0          4          2
##   2020-09-08 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14
## 1        187        175        154        151        148        153        168
## 2        644        883        930        931       1007        640        777
## 3        231        267        206        235        236        217        235
## 4        857        838        740        653        736        553        708
## 5          5          5          4          4          2          2          2
##   2020-09-15 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21
## 1        163        160        141        131        128        115        126
## 2        674        842        786        865        809        674        679
## 3        239        235        244        224        229        230        228
## 4        829        698        825        704        521        385        530
## 5          3          3          3          2          2          0          2
##   2020-09-22 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28
## 1        113        121        138        112        111        104        115
## 2        852       1083       1002       1008       1078        851        626
## 3        313        258        250        225        200        234        227
## 4        719        616        552        590        758        345        437
## 5          0          1          0          0          1          0          1
##   2020-09-29 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05
## 1        124        119        119        149        109        108         98
## 2        995       1100       1158       1181       1231       1041        932
## 3        222        227        199        205        175        159        194
## 4        587        614        494        411        371        567        567
## 5          0          3          5          1          1          0          0
##   2020-10-06 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12
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##   2020-10-13 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19
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##   2020-10-27 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02
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##   2020-12-15 2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21
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##   2020-12-29 2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04
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##   2021-01-05 2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11
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##   2021-11-16 2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22
## 1        941        960        911        892        902        884        870
## 2         68         74         66         77         79         63         67
## 3        146        149        145        147        150        118        141
## 4         16         18         22         16         14         17         28
## 5          0         11          3          4          1          0          6
##   2021-11-23 2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29
## 1        856        901        899        913        931        951        911
## 2         70         73         77         70         68         60         58
## 3        147        143        155        151        155        153        158
## 4         12         21         26         21         21          0         61
## 5          6          5          5          2          2          6          8
##   2021-11-30 2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06
## 1        949        938        919        933        892        902        871
## 2         65         68          0         54         51        114         48
## 3        157        160        151        154        159        152        164
## 4         35         21         36         22         23         27         33
## 5          9          2          0         14          1          4          9
##   2021-12-07 2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13
## 1        889        909        803        822        879        863          0
## 2         62         69         60         74         78         83         92
## 3        158        163        159        163        158        166        169
## 4         31         33         20         29         35         39          0
## 5          9          4          0          9          7          6          3
##   2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20
## 1       1621        879        901        910        902        919        903
## 2        110        148        200        234          0        551        301
## 3        167        165        169        164        179        170        177
## 4         79         57         44         81         51         75         80
## 5          3          6          5          6          5          4          1
##   2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27
## 1        848        879        883        811        866        823        851
## 2        452        665       1002       1352       1621       1803       1732
## 3        183        185        187        248        279        296        343
## 4         92        143        178        170        150        240        198
## 5          1          0          2          4          2          4          3
##   2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03
## 1        809        854        871        847        783        801        723
## 2       1846       2234       2366       2426       2556       2600       2515
## 3        367        443        542        741        833        998       1177
## 4        329        399        554        504        588        609        982
## 5          5          2          1          0          1          3          8
##   2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10
## 1        769        803        840        821        830        912        951
## 2       2581       2708       2687       2627       2655       2759       2562
## 3       1695       2273       2779       3192       3487       3689       3878
## 4       1482       2246       2413       2645       2820       2999       3683
## 5          5          3          6          7         19         19          0
##   2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17
## 1        932        948       1011       1079       1101       1197       1232
## 2       2511       2616       2683       3068       3116       3067       2989
## 3       4169       4206       4187       4123       4007       4021       3998
## 4       4397       4548       4883       4881       4517       4503       5147
## 5         11         12         13          0          0         19        100
##   2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24
## 1       1303       1379       1403       1533       1569       1603       1651
## 2       2792       2902       3014       2921       3020       2813       2629
## 3       3816       3723       3294       3204       3087       2981       2748
## 4       4825       4337       4510       4809       4148       4347       5176
## 5         56         41         58         78          0          0          0
##   2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31
## 1       1809       1910       1985       2007       2018       2210       2223
## 2       2504       2369       2638       2545       2355       2291       2028
## 3       2551       2204       1952       1743       1538       1557       1509
## 4       5742       6454       6515       6913       5808       5592       6063
## 5          0        236         67         54          0          0         77
##   2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 1       2291       2278       2281       2291       2298       2301       2272
## 2       2084       2163       2232       2114       1991       2015       1704
## 3       1236       1245       1183        997        903        912        923
## 4       6436       6592       5990       5407       4445       4232       4294
## 5         42         52         32          0          4         17        100
##   2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 1       2194       2191       2189       2179       2145          0       4260
## 2       1615       1538       1588       1474       1395       1266       1191
## 3        819        776        783        657        607        613        601
## 4       3989       3463       3324       2896       2254       2268       2562
## 5        287         26         25         55          5          7          8
##   2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 1       2117       2101       2071       2053       2025       2009       2003
## 2        930        957        895        882        790        725        651
## 3        547        498        447        452        434        442        416
## 4       2166       1917       1501       1348       1019       1195       1329
## 5         20          8         11         13          0          5          5
##   2022-02-22 2022-02-23
## 1       1989       1892
## 2        626        740
## 3        394        365
## 4       1053       1012
## 5          5          5
## 
## $Growth.Rate
##                geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1                EGYPT        NaN        NaN        NaN        NaN        NaN
## 2 UNITED ARAB EMIRATES        NaN        NaN        NaN        NaN        NaN
## 3                QATAR        NaN        NaN        NaN        NaN        NaN
## 4               KUWAIT        NaN        NaN        NaN        NaN        NaN
## 5                YEMEN        NaN        NaN        NaN        NaN        NaN
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2         NA          0        NaN        NaN         NA          0        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN         NA          0         NA          0
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1        NaN        NaN         NA          0        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN         NA          0        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN         NA          0        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4        NaN        NaN        NaN        NaN        NaN         NA         10
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1        NaN        NaN        NaN        NaN         NA          0        NaN
## 2        NaN        NaN         NA  0.3333333          0        NaN         NA
## 3        NaN        NaN        NaN         NA          2          0         NA
## 4        1.5   1.133333  0.1176471  0.0000000        NaN         NA          0
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1        NaN         NA         12          0         NA  0.1764706  0.6666667
## 2       0.00         NA          0         NA          0        NaN         NA
## 3       0.25          0        NaN        NaN         NA  0.4285714  2.0000000
## 4        NaN         NA          0         NA          1  0.0000000         NA
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1    0.25000   7.000000   1.857143  2.2307692 0.03448276   40.00000  1.1500000
## 2    0.00000         NA   0.000000        NaN         NA    0.00000        NaN
## 3   39.66667   0.000000         NA  0.2931034 3.76470588    0.59375  0.0000000
## 4    0.60000   2.666667   0.000000         NA 0.33333333    1.37500  0.6363636
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1   0.000000         NA  0.4833333  0.3103448  3.6666667 1.18181818  0.9230769
## 2         NA  1.8000000  0.0000000         NA  0.0000000         NA  1.1111111
## 3         NA  0.6153846  1.2500000  1.1000000  1.1818182 0.53846154  3.5714286
## 4   1.714286  0.5000000  1.8333333  1.5454545  0.7058824 0.08333333  2.0000000
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1       1.50  0.7222222   1.051282  0.9756098   0.825000  1.4242424   1.148936
## 2       1.70  0.0000000         NA  0.8750000   1.619048  0.4019608   1.292683
## 3       0.44  1.0909091   1.083333  2.1538462   1.571429  1.3409091   1.491525
## 4       2.00  3.2500000   1.307692  0.5882353   2.000000  0.5500000   2.090909
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1  1.2777778  1.2463768   1.395349  0.7083333   1.211765  1.4466019  0.8590604
## 2  2.8301887  1.4000000   1.142857  1.0041667   1.219917  0.9421769  1.0216606
## 3  0.6136364  2.1111111   1.105263  1.9841270   1.116000  0.8172043  0.9868421
## 4  1.2173913  0.8928571   3.000000  0.8266667   1.241935  1.4155844  0.7155963
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1   0.859375  1.2636364  0.6834532   1.526316  0.8689655  0.9920635  1.2800000
## 2   1.060071  1.1033333  1.1178248   1.016216  1.0292553  1.0284238  1.0351759
## 3   0.680000  1.0849673  0.8192771   1.588235  1.1620370  1.0039841  0.7817460
## 4   1.435897  0.4910714  1.5090909   1.939759  0.4968944  0.8250000  0.8333333
## 5        NaN        NaN         NA   0.000000        NaN        NaN        NaN
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1  0.9687500   1.083871   1.017857  1.0994152  0.5957447  1.6875000  0.8306878
## 2  1.0485437   1.064815   1.036957  0.0000000         NA  1.0104384  1.0123967
## 3  1.4365482   1.385159   1.428571  0.6160714  1.2753623  1.2886364  0.9135802
## 4  0.9090909   2.380000   1.126050  0.6940299  1.7634409  0.4878049  1.0625000
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1  1.0764331  1.3727811  0.8663793   1.129353  0.9471366  1.1534884   1.048387
## 2  0.9857143  1.0724638  1.0135135   1.013333  1.0075188  0.9141791   1.104082
## 3  1.1737452  1.0246711  1.2215088   1.094612  1.1152461  1.0301399   0.707419
## 4  1.9764706  0.8988095  1.4238411   1.293023  0.6582734  1.1639344   0.713615
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1  0.8692308  1.1902655  1.3308550  0.8324022  0.9127517  1.2794118  1.1149425
## 2  1.0147874  1.0054645  1.0090580  1.0071813  1.0053476  1.0053191  0.8148148
## 3  0.9497784  1.3141524  0.8130178  1.1295488  0.8750000  0.9425626  1.4859375
## 4  1.9736842  0.9466667  1.2429577  0.6855524  1.5041322  0.8104396  1.7830508
## 5         NA  0.0000000         NA  3.0000000  0.0000000         NA  4.5000000
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1  0.9974227  1.0155039   1.259542  0.9858586  0.8934426  0.7935780   1.002890
## 2  1.1818182  0.9194139   1.101594  1.1283906  1.2516026  0.8706786   1.151471
## 3  0.8727655  1.1060241   1.428105  0.8619375  1.0522124  0.9276703   1.383500
## 4  0.9220532  0.5731959   2.305755  0.6474259  2.5662651  0.5615023   1.657191
## 5  0.4444444  0.0000000         NA  0.0000000         NA  0.2941176   1.800000
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1  0.9740634  1.1775148  1.0025126  1.2305764  1.0386965  1.0490196   1.345794
## 2  0.9259259  0.9627586  1.0702006  1.0655957  0.9183417  1.1381669   1.049279
## 3  0.9108781  1.2467626  0.6653203  1.3417173  1.0549451  0.8363971   1.199267
## 4  0.7578204  1.2609854  0.9345301  1.0644068  1.1125265  0.8024809   1.275862
## 5  0.5555556  3.0000000  1.4000000  0.7619048  0.3750000  0.3333333  18.500000
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1  1.0347222  1.0389262  1.0116279  0.9284802  1.0343879  0.9335106  1.1239316
## 2  1.0778923  0.9500531  1.1118568  0.8169014  0.9618227  1.0524968  0.9476886
## 3  0.9108125  1.0422535  1.1776062  0.9464481  0.8666282  1.1665556  0.9948601
## 4  0.7493010  1.2947761  0.9173871  0.9424084  0.9311111  0.7935561  0.9142857
## 5  0.3513514  1.0000000  0.9230769  0.5833333  1.4285714  1.1000000  1.4545455
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1  1.1533587  1.2384615  1.1437445  1.0605120  1.1236284  0.9108073  0.8234453
## 2  1.1335045  0.6375991  1.1332149  1.1379310  0.9104683  0.9606657  0.9385827
## 3  0.9988519  1.1304598  1.0132181  1.1816357  0.6997877  0.9241505  1.1989494
## 4  1.1381579  1.2210983  1.2686391  0.9402985  0.8442460  0.8448884  1.2336579
## 5  0.3750000  3.8333333  0.2173913  5.4000000  0.4814815  2.3846154  1.4516129
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1  0.9366319  1.0676552  1.1701389  1.1105341  0.9799599  0.9304703  1.0146520
## 2  0.9580537  1.1541156  0.9468892  1.0032051  0.8626198  1.0518519  0.9295775
## 3  1.0410734  0.8316675  1.1094244  0.9692132  0.9382353  0.8576803  1.2580409
## 4  0.8004510  0.7915493  1.2864769  0.6735823  1.4722793  0.9232915  0.9516616
## 5  0.4444444  1.7000000  0.4705882  0.8125000  0.1538462  6.0000000  2.3333333
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1  1.0505415  0.9910653  1.0936200  1.0634115  0.9648181   1.045117  0.9266706
## 2  1.1420455  0.7943615  1.0709812  0.9571150  0.6191446   1.125000  1.0116959
## 3  0.9970947  0.8601399  1.0277778  1.2050099  0.6487965   1.074199  0.9427002
## 4  1.0841270  0.8916545  0.8538588  0.9884615  0.8832685   1.125551  1.0313112
## 5  1.2857143  0.8611111  1.3225806  1.7804878  0.3150685   5.043478  0.3534483
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1  0.8698149  0.8936170  1.4564860  0.8720406  0.9534583  1.0684746  0.8451777
## 2  1.1040462  1.0157068  1.0128866  0.9872774  1.0103093  0.9642857  1.0052910
## 3  0.9134055  1.1549681  0.8058406  1.0048972  0.8586745  1.1736663  1.1373308
## 4  1.0910816  0.9408696  1.1164510  0.7731788  1.0813704  1.2693069  1.1575663
## 5  0.4146341  0.4117647  1.4285714  0.3000000  6.3333333  1.3684211  0.9615385
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1   1.066066  1.1049296  1.0356915  0.7187692  1.0830479  1.2379447  0.9942529
## 2   1.184211  0.9555556  0.9534884  0.9439024  1.1291990  1.0274600  0.9376392
## 3   1.019558  0.8840701  0.8924528  0.9291755  0.8532423  0.9240000  1.4170274
## 4   1.140162  1.0744681  1.0066007  0.7519126  0.8008721  1.0562613  1.1529210
## 5   0.920000  2.6521739  0.2131148  1.0769231  1.0714286  0.6666667  3.0000000
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1  0.9653179  0.9880240  0.9508418  0.9376771  0.9199396  0.7955665  1.0908153
## 2  0.9548694  0.9950249  1.6800000  1.0654762  0.9539106  0.7730600  1.0075758
## 3  0.9317719  0.9770492  0.8456376  0.7010582  1.1622642  0.8863636  1.0989011
## 4  1.1102832  1.2335570  0.8846572  0.7761378  1.0110935  1.1018809  0.8549075
## 5  1.0666667  0.9687500  0.6129032  0.4210526  2.1250000  1.4705882  0.2800000
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1  0.9697256  0.9268293  1.0326316  0.9408767  0.9880823  1.0208333  0.9978518
## 2  0.8364662  1.1955056  0.8890977  0.8520085  0.9950372  0.8578554  1.0901163
## 3  1.0133333  0.9161184  0.9335727  0.9576923  0.9437751  0.8893617  1.2368421
## 4  1.2678869  1.0931759  0.8883553  0.6459459  1.7489540  0.7344498  1.0846906
## 5  3.0000000  1.8095238  0.6315789  0.3750000  8.4444444  0.4342105  0.5454545
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1  0.9827772   1.016429  0.7575431  0.9928876  0.8638968   1.039801  1.0781499
## 2  0.7333333   1.021818  1.0427046  0.9863481  0.7301038   1.284360  1.1254613
## 3  0.8704062   1.097778  0.8522267  0.9738717  0.8292683   1.144118  1.0102828
## 4  1.0555556   1.125178  0.6991150  1.2350814  0.4392387   1.863333  1.2003578
## 5  0.5555556   2.600000  0.9230769  0.2083333  5.0000000   0.520000  0.7692308
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1  0.9866864  1.0014993  0.9865269  0.7754173  0.9373777  0.8768267  1.1071429
## 2  0.7737705  1.0762712  1.0275591  1.1992337  1.1214058  0.7521368  1.3977273
## 3  1.1221374  0.8458050  1.0563003  1.0101523  0.6758794  1.0855019  0.9691781
## 4  1.1192250  0.9147803  1.0960699  0.9083665  0.6783626  1.3060345  1.2706271
## 5  1.1000000  1.2727273  1.4285714  0.0000000         NA  1.4285714  1.2000000
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1  0.8795699  0.9804401  0.8004988  0.7414330  0.7016807  0.9401198  0.7133758
## 2  1.0162602  0.8053333  0.9370861  0.8975265  0.9409449  0.6861925  1.1524390
## 3  0.9646643  1.1245421  0.7654723  0.9191489  0.9074074  1.0969388  1.0046512
## 4  0.9792208  0.8302387  0.6837061  1.1471963  0.9429735  0.8380130  1.2242268
## 5  0.6666667  1.8750000  0.1333333  1.0000000  2.0000000  0.0000000         NA
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10  2020-08-11
## 1  1.0982143  1.0650407  1.0763359 1.18439716  1.0658683  0.9775281  0.96551724
## 2  1.3439153  0.9409449  0.9037657 1.10648148  0.9414226  0.7955556  1.46368715
## 3  1.2361111  1.0749064  1.0139373 0.91752577  1.1123596  1.0606061  1.21904762
## 4  1.3705263  0.9523810  1.1000000 0.69208211  1.0889831  1.3365759  0.97234352
## 5  0.1153846  1.6666667  5.6000000 0.03571429  7.0000000  4.0000000 -0.03571429
##    2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1   0.7678571  1.1240310  0.7724138  1.0357143  1.1982759  0.8273381   1.417391
## 2   0.9389313  1.1260163  1.1913357  0.8575758  0.7420495  1.0904762   1.593886
## 3   0.7604167  1.1746575  0.7317784  1.1035857  0.9783394  1.0627306   1.017361
## 4   1.0733533  0.9776848  0.9971469  0.7324750  0.9921875  1.2244094   1.033762
## 5 -10.0000000  0.6000000  1.8333333  0.0000000         NA  0.0000000         NA
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1  0.9877301  0.6894410  1.1081081  0.7235772  1.1573034  1.3398058  1.0217391
## 2  1.1917808  1.0597701  0.8481562  1.0843990  0.9198113  0.7051282  1.2327273
## 3  1.0068259  0.9084746  0.9589552  1.1050584  0.8556338  1.0617284  0.8992248
## 4  1.0497667  0.9214815  0.8070740  1.3705179  0.8299419  0.7565674  1.4189815
## 5  0.1500000  2.3333333  1.0000000  0.1428571  4.0000000  1.2500000  1.6000000
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1   1.460993   1.150485  0.9409283  0.9506726  1.0849057  0.9217391  0.8301887
## 2   1.176991   1.230576  0.7942974  1.0948718  0.8477752  1.4944751  1.0609982
## 3   1.051724   1.008197  0.8455285  1.0144231  0.7962085  1.2083333  1.0640394
## 4   1.138662   0.965616  0.9391691  1.0205371  0.6377709  1.1480583  1.4841438
## 5   0.750000   0.500000  3.3333333  0.3000000  2.3333333  0.7142857  0.8000000
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1  0.9375000  0.8787879  1.0827586  0.8280255  1.1615385  1.1788079  1.0505618
## 2  1.2804878  0.8353741  0.9967427  1.1519608  0.7276596  0.9161793  1.3702128
## 3  0.9814815  1.0094340  1.0140187  1.0460829  1.0176211  1.0952381  0.9130435
## 4  0.9501425  1.3493253  0.9611111  0.8323699  0.8597222  1.3004847  1.0645963
## 5  3.5000000  0.2142857  1.3333333  0.0000000         NA  0.5000000  2.5000000
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1  0.9358289  0.8800000  0.9805195  0.9801325  1.0337838   1.098039  0.9702381
## 2  1.3711180  1.0532276  1.0010753  1.0816327  0.6355511   1.214063  0.8674389
## 3  1.1558442  0.7715356  1.1407767  1.0042553  0.9194915   1.082949  1.0170213
## 4  0.9778296  0.8830549  0.8824324  1.1271057  0.7513587   1.280289  1.1709040
## 5  1.0000000  0.8000000  1.0000000  0.5000000  1.0000000   1.000000  1.5000000
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1  0.9815951  0.8812500  0.9290780  0.9770992  0.8984375  1.0956522  0.8968254
## 2  1.2492582  0.9334917  1.1005089  0.9352601  0.8331273  1.0074184  1.2547865
## 3  0.9832636  1.0382979  0.9180328  1.0223214  1.0043668  0.9913043  1.3728070
## 4  0.8419783  1.1819484  0.8533333  0.7400568  0.7389635  1.3766234  1.3566038
## 5  1.0000000  1.0000000  0.6666667  1.0000000  0.0000000         NA  0.0000000
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1  1.0707965  1.1404959  0.8115942  0.9910714  0.9369369  1.1057692  1.0782609
## 2  1.2711268  0.9252078  1.0059880  1.0694444  0.7894249  0.7356052  1.5894569
## 3  0.8242812  0.9689922  0.9000000  0.8888889  1.1700000  0.9700855  0.9779736
## 4  0.8567455  0.8961039  1.0688406  1.2847458  0.4551451  1.2666667  1.3432494
## 5         NA  0.0000000        NaN         NA  0.0000000         NA  0.0000000
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1  0.9596774  1.0000000  1.2521008  0.7315436  0.9908257  0.9074074   1.234694
## 2  1.1055276  1.0527273  1.0198618  1.0423370  0.8456539  0.8952930   1.138412
## 3  1.0225225  0.8766520  1.0301508  0.8536585  0.9085714  1.2201258   1.293814
## 4  1.0459966  0.8045603  0.8319838  0.9026764  1.5283019  1.0000000   1.192240
## 5         NA  1.6666667  0.2000000  1.0000000  0.0000000        NaN         NA
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1  1.0991736  0.9097744  0.8760331  1.1792453  1.0320000  1.0232558   1.053030
## 2  0.9858624  1.0411090  0.9871442  1.0502326  0.9707706  0.9708029   1.235902
## 3  0.9482072  0.8949580  0.9671362  0.8640777  1.1629213  0.9951691   1.038835
## 4  0.7026627  1.4694737  0.9097421  0.7748031  1.1138211  1.4178832   1.086229
## 5  0.3333333  0.5000000  1.0000000  0.0000000         NA  0.0000000         NA
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1  0.9208633  0.9218750  1.0677966   1.095238  0.9202899  0.9685039   1.284553
## 2  1.0882129  0.9769392  1.0100143   1.089235  0.7899870  0.7530864   1.177049
## 3  0.9252336  1.0101010  0.9450000   1.243386  0.8680851  1.1764706   1.137500
## 4  0.6303318  1.4022556  0.9772118   1.013717  0.8971583  1.0346908   1.291545
## 5  0.0000000        NaN         NA   0.000000         NA  0.0000000         NA
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1  1.1265823  0.9943820  0.9604520  0.9823529  0.8562874  1.1678322   1.017964
## 2  1.4280409  1.0260078  0.9904943  0.9539347  0.9114688  0.8175129   1.251125
## 3  0.9743590  0.9473684  0.9880952  1.0200803  0.8070866  1.2780488   0.980916
## 4  0.9176072  1.0934809  0.9133858  0.8559113  1.0187050  0.9632768   1.136364
## 5  0.0000000        NaN         NA  0.0000000        NaN        NaN        NaN
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1  0.9000000  1.1699346  0.9329609  1.0718563  1.0111732  1.0441989  1.0423280
## 2  1.0071942  0.9371429  0.8932927  0.9564846  1.1400535  0.9655712  0.8168558
## 3  0.9727626  0.8440000  0.9146919  1.1036269  0.7699531  1.2012195  1.1472081
## 4  1.0503226  0.9336609  0.8828947  0.8777943  1.0322581  1.2483553  1.0368906
## 5         NA  1.0000000  0.0000000         NA  0.0000000        NaN        NaN
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1  1.0507614  0.9710145  1.1144279  0.9285714  1.1490385  0.9246862   1.049774
## 2  1.1517857  1.1102498  1.0023274  0.8831269  0.9737073  1.0315032   0.956370
## 3  1.0044248  1.0969163  0.7710843  1.0520833  0.9405941  1.2105263   1.000000
## 4  0.9695044  1.0419397  1.0377358  0.8993939  0.7250674  1.3661710   1.228571
## 5        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1  0.9784483  0.9427313  1.0467290  1.0178571  0.9649123  1.1000000  1.1363636
## 2  1.1076642  0.9357496  1.0792254  0.9575856  1.0306644  0.9991736  1.0380480
## 3  0.9739130  1.0937500  0.9591837  0.8638298  1.0591133  1.1302326  0.7983539
## 4  0.8615725  0.9935733  0.9288486  0.9623955  0.7221418  0.9799599  1.1370143
## 5        NaN         NA  0.0000000        NaN        NaN        NaN        NaN
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1  1.1963636  1.0395137   1.061404  0.9862259  0.9804469  1.0085470  1.0197740
## 2  1.0294821  0.8924149   1.100607  0.9944838  0.9548336  0.8838174  1.2300469
## 3  1.1288660  0.9497717   1.149038  0.7280335  0.9597701  1.1137725  1.2204301
## 4  0.8129496  1.0730088   1.002062  0.8765432  0.7558685  1.0465839  1.1928783
## 5        NaN        NaN         NA  0.0000000        NaN         NA  0.3333333
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1  1.0110803  1.0082192  0.9701087  0.9831933  1.0199430  1.0335196  1.0594595
## 2  0.9900763  1.0061681  0.9831418  0.9758379  0.9992013  0.8848921  1.1644083
## 3  0.9207048  1.0287081  0.8558140  1.2336957  0.7533040  1.0818713  0.9081081
## 4  1.0497512  0.7819905  0.9969697  0.9696049  0.7241379  0.9047619  1.7081340
## 5  0.0000000         NA  0.0000000        NaN         NA  0.0000000        NaN
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1  1.0739796  1.0261283  0.9884259  1.0093677  0.9698376  0.9928230  1.0457831
## 2  0.9968968  1.0249027  0.9954442  0.9260107  0.9497529  0.9956635  1.0975610
## 3  1.5178571  0.8666667  0.7511312  0.8433735  0.8928571  1.4240000  0.6573034
## 4  0.7507003  1.1716418  1.0923567  0.7201166  0.8299595  1.1219512  1.3086957
## 5        NaN        NaN        NaN        NaN         NA  0.0000000         NA
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1  0.9700461  1.0570071   1.042697  1.0301724  1.0167364  1.0514403   1.023483
## 2  1.0420635  0.9558264   0.952988  0.9648829  1.0346620  0.9145729   1.122711
## 3  1.2820513  1.0866667   1.006135  0.8963415  0.9115646  1.1940299   0.943750
## 4  1.0099668  0.9572368   1.010309  0.8673469  0.6823529  1.3275862   1.129870
## 5  0.0000000         NA   0.500000  1.0000000  0.0000000         NA   1.000000
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1  1.0401530  1.0073529  1.0565693  1.0552677  1.0867430  1.0813253   1.097493
## 2  1.0424144  1.0336463  0.9719909  0.9766355  0.9338118  0.9197267   1.138347
## 3  0.9602649  0.9655172  1.1357143  0.8930818  1.0070423  1.1048951   0.943038
## 4  1.0000000  0.8467433  1.5339367  0.7197640  0.8360656  1.1274510   1.295652
## 5  0.0000000         NA  0.0000000        NaN        NaN        NaN        NaN
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1  1.1560914  1.1207464  1.1096964  1.0494263  1.0311186   1.108483  0.9808683
## 2  1.0163132  1.0521669  0.9382151  0.9975610  0.7693562   1.087924  1.4664070
## 3  0.9395973  1.1214286  0.8216561  1.3100775  0.9408284   1.006289  1.2875000
## 4  0.8926174  0.9172932  1.0655738  0.6615385  1.1860465   1.000000  1.1568627
## 5        NaN         NA  0.0000000        NaN         NA   1.000000  0.0000000
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1  1.0585146   1.004961  0.9936530  0.9985806  0.9303483  0.9755539  0.8762725
## 2  1.1440903   1.004063  1.0728324  1.0576509  0.8099847  0.9440252  1.3104597
## 3  0.9368932   1.103627  0.9765258  0.9519231  0.9949495  1.0507614  1.0048309
## 4  0.8686441   1.395122  0.9965035  0.7192982  1.3121951  1.3828996  0.8387097
## 5         NA   2.000000  1.0000000  0.0000000        NaN        NaN        NaN
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1  0.8999106   1.210526  0.8211649  0.9880120  1.0040445  0.9677744  1.0093652
## 2  1.0508388   1.445573  0.9872825  1.0162712  0.9593062  0.8358832  1.3490017
## 3  1.0048077   1.004785  0.9285714  1.0564103  0.9368932  1.0518135  1.0394089
## 4  1.3173077   1.313869  0.9166667  0.8626263  0.9695550  1.2729469  0.9373814
## 5         NA   2.000000  0.0000000        NaN        NaN         NA  2.0000000
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1  1.0268041   1.026104  0.8600783  1.0091013  1.0033822  0.9865169  1.0239180
## 2  1.0366944   1.005949  1.0073921  1.0073378  1.0061189  1.0052129  1.0057620
## 3  0.9526066   1.039801  0.9377990  1.0408163  0.9215686  1.2074468  0.9911894
## 4  1.0910931   1.038961  0.9464286  0.8207547  0.8689655  1.2354497  1.2376874
## 5  1.0000000   0.500000  1.0000000  1.0000000  0.0000000         NA  2.0000000
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1  0.8776418  0.9531052  0.9946809  0.9090909  0.9911765  0.9925816   0.961136
## 2  1.0042968  1.0065602  1.0065174  1.0039414  1.0036455  1.0033529   1.002785
## 3  1.2044444  0.9520295  1.0193798  0.9543726  0.9840637  1.1214575   1.079422
## 4  0.7647059  1.2895928  0.9350877  1.0018762  0.7191011  1.2812500   1.026423
## 5  0.0000000        NaN         NA  0.0000000        NaN        NaN         NA
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1  0.9828927  0.8243671  1.1305182  0.9286927  0.9744059  1.0150094  0.9630314
## 2  1.0938628  1.0068545  0.9989914  0.9204947  0.8083356  0.9260516  1.2124542
## 3  1.1304348  1.0266272  0.9827089  1.0645161  0.9669421  1.0968661  0.9740260
## 4  1.1485149  1.0137931  1.1190476  0.7811550  1.2354086  0.9228346  1.3839590
## 5  1.0000000  0.0000000        NaN        NaN         NA  1.0000000  0.0000000
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1  0.9827255  1.0390625   1.015038  0.9425926  1.0491159  1.0617978   1.010582
## 2  1.2015106  0.8169474   1.000616  1.0076899  0.9441392  0.9046233   1.182988
## 3  1.0560000  1.0277778   0.977887  0.9899497  1.0355330  1.0465686   1.117096
## 4  0.9321825  1.1111111   1.119048  0.9000000  1.1371158  1.0353430   1.006024
## 5        NaN        NaN         NA  1.5000000  0.0000000         NA   0.000000
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15  2021-02-16
## 1  1.0645724  0.9885246  1.0099502  0.9852217  1.0183333  1.0032733 1.032626427
## 2  1.0691843  0.9960441  0.9381560  0.7955851  1.2037248  0.9861067 1.036183157
## 3  0.9454927  0.9933481  1.0044643  1.0066667  0.9713024  2.0181818 0.006756757
## 4  0.9850299  1.0618034  0.9742366  0.8334966  0.9377203  1.0313283 1.171324423
## 5         NA  0.5000000  2.0000000  0.0000000         NA  0.0000000          NA
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1  0.9763033  1.0614887  0.9298780  0.9836066  1.0133333  1.0246711  1.0160514
## 2  1.0667491  0.9542294  0.9532483  1.0057325  0.7124763  0.9355556  1.4275534
## 3 75.5000000  1.0198675  1.0064935  0.9655914  1.0222717  1.0087146  0.9827214
## 4  1.0549793  0.9626352  0.9969356  0.8831967  0.8909513  1.1705729  1.1290323
## 5  1.0000000  1.0000000  1.0000000  0.0000000         NA  1.3750000  1.0000000
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1  1.0173776  0.9145963  1.0203735  0.9783694  1.0119048  0.9848739  0.9914676
## 2  1.0322795  0.9751773  1.1563636  0.9817038  0.8532324  0.8621160  1.0771971
## 3  1.0087912  1.0130719  1.0086022  0.9808102  1.0152174  1.0128480  0.9788584
## 4  0.9862069  1.0179820  1.0029441  0.8258317  1.1398104  1.2255717  1.1374046
## 5  3.0909091  1.0000000  0.3529412  0.1666667  8.0000000  1.5625000  1.2800000
##   2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1  0.9931153  1.0173310  0.9863714  1.0155440  0.9880952  1.0172117  1.0524535
## 2  0.9893422  1.0185736  1.1203501  0.9632161  0.8830686  0.9502488  0.9556988
## 3  1.0172786  1.0084926  0.9873684  0.9808102  1.0304348  0.9873418  1.0064103
## 4  1.0507084  1.2178850  0.9399767  0.8171110  0.8679818  1.1590909  0.8725490
## 5  0.6562500  0.5714286  3.0000000  0.9166667  0.8787879  2.4827586  0.5694444
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1  1.0369775  0.9906977  1.0000000  1.0031299  1.0046802  0.9798137  1.0142631
## 2  0.9287821  0.9469147  1.0781025  0.9595556  0.9226494  0.9528112  1.0632244
## 3  1.0042463  0.9894292  0.9722222  1.0615385  1.0041408  0.9917526  0.9958420
## 4  1.1521175  1.1290323  0.9009967  0.8930678  0.8777870  1.2530574  0.9864865
## 5  1.0000000  0.9756098  1.5500000  0.6774194  1.5476190  1.1076923  0.8472222
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1   1.007812  0.9953488   1.004673  0.9984496  1.0046584  0.9938176  1.0077760
## 2   1.016353  1.0243784   1.028082  0.9319444  0.8529558  1.0896913  1.1608765
## 3   1.020877  1.0204499   0.995992  1.0241449  0.9882122  1.0318091  1.0289017
## 4   1.144597  0.9268617   1.089670  0.8867676  0.8849295  1.1157718  0.9684211
## 5   1.114754  1.3088235   1.022472  0.6703297  2.2950820  0.7000000  0.9795918
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1  0.9891975  1.0312012  1.0136157  1.0194030  1.0043924  1.0043732   1.005806
## 2  1.0110497  0.9303279  1.0416055  1.0827068  0.9236111  0.8806391   1.221451
## 3  1.0674157  1.0298246  1.0255537  1.0199336  1.0407166  1.0798122   1.043478
## 4  1.0085404  1.0700539  1.1136691  0.7739018  0.9357262  1.1159679   1.015987
## 5  0.9479167  1.2417582  0.7433628  0.8214286  0.9275362  1.2812500   1.609756
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1  1.0086580   1.018598  0.9873596  1.0099573  0.9985915  1.0818054  1.0143416
## 2  0.9104412   1.110845  0.9416847  0.9559633  1.0139155  0.9522007  0.9880716
## 3  1.0833333   1.076923  1.0404762  0.9954233  1.0068966  1.0388128  1.0186813
## 4  1.0086546   1.106084  0.8695346  1.0016221  0.9740891  1.1280133  1.0338983
## 5  0.8333333   1.581818  0.5114943  0.8651685  1.3116883  0.8217822  1.1325301
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1  1.0064267   1.007663  1.0063371  1.0088161  1.0137328   1.007389  1.0061125
## 2  0.9471831   1.121614  0.8877841  1.0298667  0.9373382   1.065193  1.0487552
## 3  1.0140237   1.009574  1.0010537  1.0147368  0.9968880   1.012487  1.0082220
## 4  1.0812545   0.909031  1.0710660  0.9336493  1.0079768   1.176259  0.9443425
## 5  0.7659574   1.194444  1.1627907  0.4300000  1.8837209   1.098765  0.6853933
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1  1.0097205  1.0072202  1.0047790  1.0047562  1.0059172  1.0023529  1.0035211
## 2  0.8892186  1.0723026  0.9559129  1.0623983  0.9856997  0.9341969  1.0554631
## 3  1.0030581  1.0050813  0.9888777  0.8456033  0.9951632  1.0886999  0.9877232
## 4  0.9080311  0.9921541  1.0107836  0.9871977  0.8119597  1.3398403  0.9079470
## 5  1.2295082  1.0000000  0.7733333  0.9482759  0.7636364  1.0952381  1.3043478
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1  1.0070175   1.012776  1.0137615  1.0316742  1.0449561  1.0398741  1.0121090
## 2  1.0147136   1.077680  0.9481019  1.0542321  0.8716346  0.9702151  1.1904491
## 3  0.9254237   0.976801  0.9975000  0.8997494  0.9818942  0.9971631  0.9886202
## 4  1.0495988   1.013899  0.9814942  0.8421788  1.2810945  0.8323625  1.1244168
## 5  0.7000000   1.428571  0.6000000  1.3611111  0.6530612  1.4375000  0.8043478
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1  1.0079761  0.9920870  1.0179462  1.0107738  1.0184109  1.0256898  1.0111317
## 2  0.8166189  1.1467836  1.0066293  0.8672746  1.0788551  0.9593936  0.9588036
## 3  0.9928058  0.9956522  0.9839884  0.9615385  0.9938462  0.9969040  0.9937888
## 4  1.0124481  0.9678962  1.0084686  0.8950315  1.0289289  0.9468085  1.0056180
## 5  1.1621622  0.7209302  0.7419355  0.3478261  2.0000000  1.3750000  1.2272727
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1  1.0110092  1.0072595  1.0135135  1.0062222  1.0053004  1.0105448  1.0260870
## 2  1.1500883  0.8822927  1.0243619  0.9824462  0.9060519  0.9586514  1.0710020
## 3  1.0078125  0.9193798  1.0118044  0.8883333  0.7298311  1.0205656  0.8639798
## 4  1.1580208  0.8518263  0.9781553  0.9057072  0.9059361  0.9858871  1.1789366
## 5  0.8888889  0.5000000  1.6666667  0.8000000  1.2500000  0.1500000  2.3333333
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1  1.0059322  1.0050548  1.0033529  1.0050125  0.9983375  0.9891757  0.9840067
## 2  0.9343247  1.0026525  0.9603175  0.9097796  0.9470098  0.9824141  1.0333605
## 3  1.1428571  0.7627551  0.8160535  1.0655738  0.9846154  1.1796875  1.2251656
## 4  0.8542931  1.0751269  0.7204910  1.0419397  1.0415094  1.0398551  1.2590012
## 5  0.8571429  1.5000000  1.7777778  0.9375000  0.3333333  5.0000000  0.7200000
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1  0.9923011  0.9939655  0.9956635  1.0026132  0.9947871  1.0034934  0.9921671
## 2  1.0614173  1.0393175  1.0635261  1.0711409  0.9968672  0.9503457  1.1058201
## 3  0.7972973  1.0610169  1.1725240  0.8991826  0.8575758  1.0565371  1.1672241
## 4  1.0322878  1.0437891  1.1515411  0.7561338  0.9754179  1.2500000  1.1354839
## 5  0.3888889  2.8571429  0.9500000  0.8947368  0.5294118  0.4444444  2.0000000
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1  1.0096491  0.9834926  1.0008834  0.9876434  0.8999106  0.9771599  0.9715447
## 2  1.0508373  1.2333523  1.0318413  0.8103757  0.9988962  0.9740331  1.1162791
## 3  0.8767908  0.9346405  0.7062937  0.7722772  1.2115385  1.2063492  1.0087719
## 4  0.8352273  0.9863946  1.1931034  0.8193642  0.9656085  1.2876712  0.9070922
## 5  2.2500000  0.4444444  3.3750000  0.2962963  0.7500000  0.8333333  1.8000000
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1  0.9947699  0.9800210  0.9238197  0.9535424  0.9756395  0.9762797   0.988491
## 2  1.0945122  0.9233983  1.0367019  1.0611057  0.8564899  1.0501601   1.120427
## 3  0.8434783  1.0206186  0.9242424  1.0491803  0.8958333  0.9941860   1.064327
## 4  1.0516028  1.0728625  0.8870409  1.0398437  0.9744553  1.1403238   1.068966
## 5  0.8888889  1.0000000  0.7500000  1.1666667  1.0000000  3.5714286   0.440000
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1  0.9896507  0.9869281  0.9708609  0.9699864  0.9718706  0.8871201  0.9934747
## 2  0.9882086  0.0000000         NA  0.4748378  0.9274611  0.9329609  1.1578661
## 3  0.8681319  0.9050633  1.2937063  0.7945946  0.7959184  1.3418803  0.9299363
## 4  0.8798229  1.2286125  0.9695728  0.8672299  1.0521921  1.0337302  0.9513756
## 5  0.8181818  0.4444444  5.0000000  0.0500000  5.0000000  0.2000000  1.0000000
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1  0.9950739  0.9752475  0.9966159  0.9609508  0.9399293  0.9567669   0.978389
## 2  0.9454631  1.0775733  0.8961698  1.0859938  0.8771930  1.0616216   1.103360
## 3  1.1301370  0.7696970  1.4488189  1.0000000  0.5815217  1.2149533   1.453846
## 4  1.0470746  1.0571612  1.0072904  0.9028951  1.1095524  1.1649609   1.013953
## 5  3.0000000  0.6666667  3.0000000  0.3333333  4.0000000  0.5000000   0.750000
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1  0.9357430  0.9077253  0.9739953  0.9927184  0.9511002  0.9665810  0.6941489
## 2  0.9173973  1.0870221  1.0286904  1.0265407  0.9298861  0.9613572  1.0705882
## 3  0.8148148  0.6818182  0.8285714  1.4367816  0.8160000  1.1568627  1.2118644
## 4  0.9531091  0.9417112  0.9664963  0.9759107  0.9379892  1.0603338  1.0399516
## 5  2.0000000  1.1666667  0.0000000         NA  2.0000000  0.5000000  9.0000000
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1  0.9616858  0.9641434  0.8181818  0.9141414  0.9889503  0.9776536  0.9371429
## 2  0.7999084  0.9587865  0.9928358  0.9813590  0.9797794  0.9837398  0.9866497
## 3  0.8251748  1.2372881  0.9109589  0.7744361  1.1747573  0.7685950  1.0000000
## 4  1.0686845  0.9934641  1.0389254  0.8506596  1.0260546  1.1952842  1.0080931
## 5  0.2222222  1.5000000  0.3333333  2.0000000  1.5000000  0.0000000         NA
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1  0.9817073  0.9627329  0.8193548  0.9527559  0.9669421  0.9401709  0.9818182
## 2  0.9748711  1.0171844  0.9935023  0.9941138  0.9986842  1.0158103  0.9870298
## 3  1.6989247  0.7151899  1.2743363  0.6736111  0.8865979  1.6976744  0.9726027
## 4  0.7952835  1.0757098  0.9483871  0.9616574  0.9581994  1.1879195  0.9672316
## 5  1.5000000  0.6666667  2.0000000  0.2500000  7.0000000  0.5714286  2.2500000
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1  0.8240741  0.9101124  0.9506173  0.8961039  0.0000000         NA  0.4047619
## 2  1.0045992  1.0078483  0.9928618  1.0228758  0.9769968  0.9862655  1.0218833
## 3  0.9436620  0.9776119  1.0152672  0.8721805  1.0172414  1.0508475  0.8709677
## 4  0.9480140  0.8533580  0.9119134  0.9707047  0.9698206  0.9310345  0.9421861
## 5  0.3333333  1.0000000  1.6666667  1.0000000  0.8000000  1.5000000  0.8333333
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1  0.9607843  0.8979592  0.9318182  0.9268293  1.0263158  0.8974359  0.8857143
## 2  0.9772875  1.0272244  0.9831933  0.9907955  1.0139350  1.0137435  0.9935442
## 3  1.1851852  1.5312500  0.5816327  1.0877193  1.0161290  1.4126984  0.8202247
## 4  0.9290508  1.0185759  0.9381966  1.0658747  0.8470111  1.1818182  0.9443320
## 5  1.0000000  0.8000000  0.5000000  2.5000000  0.8000000  0.0000000         NA
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1  1.2258065  1.1052632  1.0714286  1.0444444  1.0425532  1.0408163  1.0392157
## 2  0.9922027  1.0150622  0.9806452  1.0111842  0.9882889  1.0118499  1.0071568
## 3  1.5410959  0.7022222  1.0886076  0.9418605  0.9320988  0.9933775  1.1333333
## 4  1.0085745  0.9064825  0.8980070  0.9569191  0.9645293  1.1386139  0.9751553
## 5  0.5000000  3.0000000  1.0666667  0.1875000  3.0000000  1.2222222  0.4545455
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1  1.0754717  0.8947368  1.1176471   1.070175  1.0655738  1.2769231   1.036145
## 2  0.9812661  0.9927584  1.0079576   1.016447  0.9126214  0.9368794   1.009841
## 3  0.9647059  1.1036585  1.2044199   0.912844  1.0904523  0.9677419   1.047619
## 4  1.0840764  0.8437133  0.8300836   0.840604  1.1077844  0.9351351   1.146435
## 5  2.0000000  0.8000000  3.3750000   0.000000         NA  0.6470588   0.500000
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1  1.0581395  1.0439560  1.0210526  1.0206186  1.0202020  1.0594059  1.0467290
## 2  0.9647676  0.9790210  0.9642857  0.9925926  0.9859038  0.9327166  1.0054103
## 3  1.0318182  0.8678414  1.2385787  0.7950820  0.9639175  1.3903743  1.0346154
## 4  0.7126050  1.3089623  0.7513514  0.8992806  0.8160000  1.1209150  0.7463557
## 5  1.3636364  1.3333333  1.1500000  0.7391304  1.1764706  0.7500000  2.6000000
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1  1.0982143  1.0650407  1.2519084  1.0548780  1.0635838  1.0271739  1.0264550
## 2  0.9766816  0.9889807  0.9935005  0.9962617  1.0093809  0.9851301  0.9339623
## 3  0.8104089  1.4036697  0.7222222  0.8597285  1.0789474  1.4097561  0.7508651
## 4  0.9257812  1.1645570  0.8188406  0.8849558  0.8350000  1.1317365  0.8783069
## 5  0.6410256  1.4000000  1.3428571  0.7021277  0.6666667  1.3636364  1.3666667
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1  1.0463918   1.088670  1.0588235  1.0726496  1.0159363   1.031373  1.0608365
## 2  0.9929293   1.008138  1.0030272  1.0040241  0.9889780   1.006079  1.0030211
## 3  0.9953917   1.078704  0.9098712  0.8632075  0.9781421   1.145251  0.8439024
## 4  1.1867470   1.076142  0.7924528  1.1071429  1.0161290   0.973545  0.6739130
## 5  1.0975610   1.133333  0.6862745  1.1428571  0.8250000   1.363636  0.8888889
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1  1.0430108  1.0412371  1.0495050  1.0408805  1.0362538  1.0728863  1.0271739
## 2  0.9889558  0.9898477  1.0030769  1.0061350  0.9867886  1.0061792  0.9744115
## 3  0.9595376  1.0662651  1.0903955  0.8134715  0.8280255  1.4538462  0.9100529
## 4  0.8145161  1.0990099  0.8558559  1.0842105  0.6990291  0.9861111  1.1690141
## 5  0.7750000  1.6451613  1.3137255  0.5671642  1.3684211  0.6538462  1.1470588
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1  1.0555556  1.0350877  1.0484262  1.0577367  1.0502183  1.0207900  1.0244399
## 2  0.8750000  0.9267707  0.9637306  0.9744624  0.8551724  1.0193548  0.9762658
## 3  0.9593023  0.8666667  0.8811189  1.1666667  0.9047619  0.9849624  0.9312977
## 4  0.7951807  1.0454545  0.8985507  0.6612903  1.2195122  1.1600000  1.0172414
## 5  1.2564103  0.7551020  1.0540541  1.3333333  1.0769231  0.6785714  1.3157895
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1  1.0556660  1.0715631  1.0333919  1.0833333  1.0251177  1.0398162  1.0132548
## 2  0.9854133  0.9276316  0.9237589  0.9040307  0.8301486  0.8005115  1.0287540
## 3  1.3032787  0.8364780  0.8796992  0.7008547  1.2317073  1.3762376  1.0287770
## 4  1.0000000  0.9830508  1.0862069  0.6825397  1.3023256  0.9464286  0.8490566
## 5  0.5000000  1.2000000  1.2000000  1.0277778  1.0000000  1.3783784  0.6666667
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1  1.0058140  1.0433526  0.7867036  1.1742958  1.0194903  1.0323529  1.0227920
## 2  0.9875776  1.0345912  0.9209726  1.0594059  0.9283489  0.9597315  0.9685315
## 3  0.9650350  0.7898551  0.9816514  1.0093458  0.6203704  1.3432836  1.0444444
## 4  0.8444444  1.1052632  1.1428571  0.8541667  0.9024390  1.3243243  0.7959184
## 5  1.0882353  1.1081081  0.7560976  0.9677419  1.4333333  1.2558140  0.5185185
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1  1.0278552  1.0040650   1.005398  1.0214765  1.0091984  1.0039062  1.0090791
## 2  0.9747292  0.9814815   1.041509  0.9275362  0.7187500  1.0271739  0.9312169
## 3  0.8085106  1.1184211   1.082353  1.0760870  0.7979798  0.9746835  0.7532468
## 4  0.8461538  1.5151515   0.700000  1.1714286  1.7560976  0.7222222  0.9423077
## 5  0.8214286  1.2173913   1.571429  0.6363636  0.0000000         NA  0.2666667
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1  1.0128535  1.0139594  1.0150188  1.0246609   1.007220  1.0107527  1.0130024
## 2  0.8863636  0.9230769  0.9444444  1.0735294   0.760274  1.1171171  1.0967742
## 3  1.7068966  0.7878788  0.9230769  0.8611111   1.048387  0.9076923  1.6271186
## 4  0.6326531  1.4838710  0.7608696  0.9142857   0.968750  1.3548387  0.8809524
## 5  1.8500000  0.7567568  1.2142857  0.3529412   2.000000  1.3750000  0.3030303
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1  1.0046674  1.0046458  1.0046243  1.0057537  0.9965675   1.013777  0.9830125
## 2  0.9264706  0.9206349  0.8965517  1.1057692  0.8608696   1.050505  1.1730769
## 3  0.8645833  0.9518072  0.7215190  1.2105263  0.8985507   1.080645  1.2388060
## 4  1.0270270  0.8421053  0.9375000  1.3000000  0.8974359   1.114286  0.8205128
## 5  2.7000000  1.0370370  1.0000000  0.6071429  0.8235294   2.142857  0.6333333
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1  1.0103687  1.0068415  1.0022650  0.9954802   1.005675   1.003386  0.9797525
## 2  0.9180328  0.8392857  0.9361702  0.9545455   1.119048   1.031915  0.9278351
## 3  1.0361446  1.2325581  0.8301887  0.8750000   1.064935   1.487805  0.8360656
## 4  0.6562500  1.1904762  1.2800000  0.7812500   0.840000   1.428571  0.7000000
## 5  1.1052632  1.8571429  0.3846154  0.8000000   1.333333   1.000000  1.0625000
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1  1.0413318  1.0176406  1.0043337  1.0226537  0.9841772  1.0192926  0.9684543
## 2  1.0555556  0.9263158  0.9318182  1.0731707  0.9204545  0.9629630  0.9487179
## 3  1.0098039  1.0194175  0.9619048  0.9108911  1.1304348  1.2211538  1.0551181
## 4  0.7619048  1.5625000  0.8800000  0.5454545  2.0833333  1.2000000  0.7666667
## 5  1.0000000  1.3529412  0.9565217  0.2727273  2.0000000  0.6666667  0.8750000
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1  1.0325733  0.9579390  1.0120746  0.9859002  0.9933993  1.0287929  0.9913886
## 2  1.0675676  0.9240506  1.0958904  0.9125000  0.9589041  0.9714286  1.0588235
## 3  1.0298507  0.7681159  1.1226415  1.0420168  0.8306452  1.4466019  0.9261745
## 4  0.9130435  0.7619048  1.5625000  1.1200000  0.8214286  1.0869565  0.9600000
## 5  0.5714286  5.2500000  0.5714286  1.2500000  0.8000000  1.0833333  0.6153846
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1  1.0141151  0.9839400  0.9891186  1.0220022  0.0000000         NA  0.5002658
## 2  1.0416667  1.0933333  0.8780488  1.0833333  0.8461538  0.9242424  1.1147541
## 3  1.2463768  0.8604651  0.8310811  1.1138211  0.9051095  1.1532258  1.0209790
## 4  0.9583333  0.8695652  1.2000000  0.9166667  1.1818182  1.0000000  0.6153846
## 5  1.3750000  0.4545455  1.0000000  1.2000000  0.0000000         NA  0.0000000
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1   1.020191  0.9489583  0.9791438   1.011211  0.9800443  0.9841629  0.9839080
## 2   1.088235  0.8918919  1.1666667   1.025974  0.7974684  1.0634921  1.0447761
## 3   1.020548  0.9731544  1.0137931   1.020408  0.7866667  1.1949153  1.0425532
## 4   1.125000  1.2222222  0.7272727   0.875000  1.2142857  1.6470588  0.4285714
## 5         NA  0.2727273  1.3333333   0.250000  0.0000000         NA  1.0000000
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1  1.0525701  0.9977802  1.0155729  1.0197152  1.0214823  0.9579390  1.0417124
## 2  1.0428571  1.0547945  0.9090909  0.9714286  0.8823529  0.9666667  1.1206897
## 3  0.9727891  1.0839161  0.9741935  1.0264901  0.9870968  1.0326797  0.9936709
## 4  1.7500000  1.2380952  0.8076923  1.0000000  0.0000000         NA  0.5737705
## 5  0.8333333  1.0000000  0.4000000  1.0000000  3.0000000  1.3333333  1.1250000
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1  0.9884089  0.9797441  1.0152339 0.95605573  1.0112108  0.9656319  1.0206659
## 2  1.0461538  0.0000000         NA 0.94444444  2.2352941  0.4210526  1.2916667
## 3  1.0191083  0.9437500  1.0198675 1.03246753  0.9559748  1.0789474  0.9634146
## 4  0.6000000  1.7142857  0.6111111 1.04545455  1.1739130  1.2222222  0.9393939
## 5  0.2222222  0.0000000         NA 0.07142857  4.0000000  2.2500000  1.0000000
##   2021-12-08 2021-12-09 2021-12-10 2021-12-11 2021-12-12 2021-12-13 2021-12-14
## 1  1.0224972  0.8833883   1.023661  1.0693431  0.9817975   0.000000         NA
## 2  1.1129032  0.8695652   1.233333  1.0540541  1.0641026   1.108434  1.1956522
## 3  1.0316456  0.9754601   1.025157  0.9693252  1.0506329   1.018072  0.9881657
## 4  1.0645161  0.6060606   1.450000  1.2068966  1.1142857   0.000000         NA
## 5  0.4444444  0.0000000         NA  0.7777778  0.8571429   0.500000  1.0000000
##   2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20 2021-12-21
## 1  0.5422579  1.0250284  1.0099889  0.9912088  1.0188470  0.9825898  0.9390919
## 2  1.3454545  1.3513514  1.1700000  0.0000000         NA  0.5462795  1.5016611
## 3  0.9880240  1.0242424  0.9704142  1.0914634  0.9497207  1.0411765  1.0338983
## 4  0.7215190  0.7719298  1.8409091  0.6296296  1.4705882  1.0666667  1.1500000
## 5  2.0000000  0.8333333  1.2000000  0.8333333  0.8000000  0.2500000  1.0000000
##   2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27 2021-12-28
## 1   1.036557   1.004551  0.9184598  1.0678175  0.9503464  1.0340219  0.9506463
## 2   1.471239   1.506767  1.3493014  1.1989645  1.1122764  0.9606212  1.0658199
## 3   1.010929   1.010811  1.3262032  1.1250000  1.0609319  1.1587838  1.0699708
## 4   1.554348   1.244755  0.9550562  0.8823529  1.6000000  0.8250000  1.6616162
## 5   0.000000         NA  2.0000000  0.5000000  2.0000000  0.7500000  1.6666667
##   2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03 2022-01-04
## 1   1.055624   1.019906  0.9724455  0.9244392   1.022989  0.9026217   1.063624
## 2   1.210184   1.059087  1.0253593  1.0535862   1.017214  0.9673077   1.026243
## 3   1.207084   1.223476  1.3671587  1.1241565   1.198079  1.1793587   1.440102
## 4   1.212766   1.388471  0.9097473  1.1666667   1.035714  1.6124795   1.509165
## 5   0.400000   0.500000  0.0000000         NA   3.000000  2.6666667   0.625000
##   2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10 2022-01-11
## 1   1.044213  1.0460772  0.9773810   1.010962   1.098795  1.0427632  0.9800210
## 2   1.049206  0.9922452  0.9776703   1.010659   1.039171  0.9285973  0.9800937
## 3   1.341003  1.2226133  1.1486146   1.092419   1.057929  1.0512334  1.0750387
## 4   1.515520  1.0743544  1.0961459   1.066163   1.063475  1.2280760  1.1938637
## 5   0.600000  2.0000000  1.1666667   2.714286   1.000000  0.0000000         NA
##   2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17 2022-01-18
## 1   1.017167  1.0664557  1.0672601  1.0203892  1.0871935   1.029240  1.0576299
## 2   1.041816  1.0256116  1.1434961  1.0156454  0.9842747   0.974568  0.9340917
## 3   1.008875  0.9954826  0.9847146  0.9718651  1.0034939   0.994280  0.9544772
## 4   1.034342  1.0736588  0.9995904  0.9254251  0.9969006   1.143016  0.9374393
## 5   1.090909  1.0833333  0.0000000        NaN         NA   5.263158  0.5600000
##   2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24 2022-01-25
## 1  1.0583269  1.0174039  1.0926586  1.0234834  1.0216699  1.0299439  1.0956996
## 2  1.0393983  1.0385941  0.9691440  1.0338925  0.9314570  0.9345894  0.9524534
## 3  0.9756289  0.8847703  0.9726776  0.9634831  0.9656625  0.9218383  0.9283115
## 4  0.8988601  1.0398893  1.0662971  0.8625494  1.0479749  1.1907062  1.1093509
## 5  0.7321429  1.4146341  1.3448276  0.0000000        NaN        NaN        NaN
##   2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31 2022-02-01
## 1  1.0558320  1.0392670  1.0110831  1.0054808  1.0951437  1.0058824  1.0305893
## 2  0.9460863  1.1135500  0.9647460  0.9253438  0.9728238  0.8852030  1.0276134
## 3  0.8639749  0.8856624  0.8929303  0.8823867  1.0123537  0.9691715  0.8190855
## 4  1.1239986  1.0094515  1.0610898  0.8401562  0.9628099  1.0842275  1.0615207
## 5         NA  0.2838983  0.8059701  0.0000000        NaN         NA  0.5454545
##   2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07 2022-02-08
## 1  0.9943256  1.0013169  1.0043840  1.0030554   1.001305  0.9873968  0.9656690
## 2  1.0379079  1.0319001  0.9471326  0.9418165   1.012054  0.8456576  0.9477700
## 3  1.0072816  0.9502008  0.8427726  0.9057172   1.009967  1.0120614  0.8873239
## 4  1.0242387  0.9086772  0.9026711  0.8220825   0.952081  1.0146503  0.9289707
## 5  1.2380952  0.6153846  0.0000000         NA   4.250000  5.8823529  2.8700000
##   2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14 2022-02-15
## 1 0.99863263  0.9990872  0.9954317 0.98439651  0.0000000         NA  0.4969484
## 2 0.95232198  1.0325098  0.9282116 0.94640434  0.9075269  0.9407583  0.7808564
## 3 0.94749695  1.0090206  0.8390805 0.92389650  1.0098847  0.9804241  0.9101498
## 4 0.86813738  0.9598614  0.8712395 0.77831492  1.0062112  1.1296296  0.8454333
## 5 0.09059233  0.9615385  2.2000000 0.09090909  1.4000000  1.1428571  2.5000000
##   2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21 2022-02-22
## 1  0.9924421  0.9857211  0.9913085  0.9863614  0.9920988  0.9970134  0.9930105
## 2  1.0290323  0.9352142  0.9854749  0.8956916  0.9177215  0.8979310  0.9615975
## 3  0.9104205  0.8975904  1.0111857  0.9601770  1.0184332  0.9411765  0.9471154
## 4  0.8850416  0.7829943  0.8980680  0.7559347  1.1727184  1.1121339  0.7923251
## 5  0.4000000  1.3750000  1.1818182  0.0000000         NA  1.0000000  1.0000000
##   2022-02-23 NA
## 1  0.9512318 NA
## 2  1.1821086 NA
## 3  0.9263959 NA
## 4  0.9610636 NA
## 5  1.0000000 NA

growth.rate(TSconfirmed,geo.loc=c("Saudi Arabia","Italy","Spain","US","Canada","Libya","Qatar"))

## Processing...  SAUDI ARABIA

## Processing...  ITALY

## Warning in log1p(changes): NaNs produced

## Processing...  SPAIN

## Warning in log1p(changes): NaNs produced

## Processing...  US

## Processing...  CANADA

## Processing...  LIBYA

## Processing...  QATAR

## $Changes
##        geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27
## 1 SAUDI ARABIA          0          0          0          0          0
## 2        ITALY          0          0          0          0          0
## 3        SPAIN          0          0          0          0          0
## 4           US          0          1          0          3          0
## 5       CANADA          0          0          0          1          0
## 6        LIBYA          0          0          0          0          0
## 7        QATAR          0          0          0          0          0
##   2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03
## 1          0          0          0          0          0          0          0
## 2          0          0          0          2          0          0          0
## 3          0          0          0          0          1          0          0
## 4          0          1          0          2          0          0          3
## 5          1          0          0          2          0          0          0
## 6          0          0          0          0          0          0          0
## 7          0          0          0          0          0          0          0
##   2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10
## 1          0          0          0          0          0          0          0
## 2          0          0          0          1          0          0          0
## 3          0          0          0          0          0          1          0
## 4          0          0          1          0          0          0          0
## 5          0          1          0          2          0          0          0
## 6          0          0          0          0          0          0          0
## 7          0          0          0          0          0          0          0
##   2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
## 3          0          0          0          0          0          0          0
## 4          1          0          1          0          0          0          0
## 5          0          0          0          0          0          0          1
## 6          0          0          0          0          0          0          0
## 7          0          0          0          0          0          0          0
##   2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24
## 1          0          0          0          0          0          0          0
## 2          0          0          0         17         42         93         74
## 3          0          0          0          0          0          0          0
## 4          0          0          0          2          0          0          0
## 5          0          0          0          1          0          0          1
## 6          0          0          0          0          0          0          0
## 7          0          0          0          0          0          0          0
##   2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02
## 1          0          0          0          0          0          0          1
## 2         93        131        202        233        240        566        342
## 3          4          7          2         17         13         39         36
## 4          0          0          1          0          8          7         23
## 5          2          0          2          1          6          5          6
## 6          0          0          0          0          0          0          0
## 7          0          0          0          0          1          2          0
##   2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
## 1          0          0          4          0          0          6          4
## 2        466        587        769        778       1247       1492       1797
## 3         45         57         37        141        100        173        400
## 4         19         33         77         53        166        116         75
## 5          5          5          4         14          4         10         14
## 6          0          0          0          0          0          0          0
## 7          4          1          0          0          0          7          3
##   2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16
## 1          5          1         24         41         17          0         15
## 2        977       2313       2651       2547       3497       3590       3233
## 3        622        582          0       2955       1159       1407       2144
## 4        188        365        439        633        759        234       1467
## 5          4         35         14         72         23         60        154
## 6          0          0          0          0          0          0          0
## 7          6        238          0         58         17         64         38
##   2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23
## 1         53          0        103         70         48        119         51
## 2       3526       4207       5322       5986       6557       5560       4789
## 3       1806       2162       4053       2447       4964       3394       6368
## 4       1833       2657       4494       6367       5995       8919      11152
## 5         82        200        189        254        478        392        555
## 6          0          0          0          0          0          0          0
## 7          0         13          8         10         11         13          7
##   2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30
## 1        205        133        112         92         99         96        154
## 2       5249       5210       6203       5909       5974       5217       4050
## 3       4749       9630       8271       7933       7516       6875       7846
## 4      10618      12127      17821      18591      22164      16127      22154
## 5        585        512        853        707        828        908       1147
## 6          1          0          0          0          2          5          0
## 7         25         11         12         13         28         44         59
##   2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
## 1        110        157        165        154        140        223        203
## 2       4053       4782       4668       4585       4805       4316       3599
## 3       7967       8195       7947       7134       6969       5478       5029
## 4      26381      31463      32089      32204      31891      29242      31729
## 5       1106       1281       1692       1340       1358       1242       1366
## 6          2          0          1          0          7          0          1
## 7         88         54        114        126        250        279        228
##   2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13
## 1        190        137        355        364        382        429        472
## 2       3039       3836       4204       3951       4694       4092       3153
## 3       5267       6278       5002       5051       4754       3804       3268
## 4      30065      31078      35611      34339      28873      26451      27042
## 5       1333       1307       1375       1237       1307       1219       1634
## 6          1          1          3          0          0          1          1
## 7        225        153        166        136        216        251        252
##   2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20
## 1        435        493        518        762       1132       1088       1122
## 2       2972       2667       3786       3493       3491       3047       2256
## 3       2442       5103       7304       5891        887       6948       1536
## 4      28350      25630      29669      32794      27561      25641      30026
## 5       1627       1659       1688       2034       1410       1688       1669
## 6          9         13          1          0          0          2          0
## 7        197        283        392        560        345        440        567
##   2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27
## 1       1147       1141       1158       1172       1197       1223       1289
## 2       2729       3370       2646       3021       2357       2324       1739
## 3       3968       4211       4635     -10034       2915       1729       1831
## 4      25927      29385      32377      31942      31065      26140      24629
## 5       1642       1814       1953       1855       1577       1313       1641
## 6          0          8          1          1          0          0          0
## 7        518        608        623        761        833        929        957
##   2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04
## 1       1266       1325       1351       1344       1362       1552       1645
## 2       2091       2086       1872       1965       1900       1389       1221
## 3       1308       2144        518       1781       1366        884        545
## 4      24441      26605      29063      35273      27305      23883      23880
## 5       1671       1633       1697       1769       1544       1341       1407
## 6          0          0          0          2          0          0          0
## 7        677        643        845        687        776        679        640
##   2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11
## 1       1595       1687       1793       1701       1704       1912       1966
## 2       1075       1444       1401       1327       1083        802        744
## 3       1318        996       1122       1410        721        772       3086
## 4      23933      24479      27500      27347      24550      18717      19059
## 5       1373       1573       1364       1378       1184       1064       1110
## 6          0          1          0          0          0          0          0
## 7        951        830        918       1311       1130       1189       1103
##   2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18
## 1       1911       1905       2039       2307       2840       2736       2593
## 2       1402        888        992        789        875        675        451
## 3        594        661        849        643        515          0        908
## 4      23231      19963      25972      24881      24129      18374      22590
## 5       1217       1195       1117       1211       1184       1132       1033
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##   2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25
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##   2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01
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##   2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08
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##   2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15
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##   2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22
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##   2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27
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## 2      30783      36326      44585      50590      54787      24882      30799
## 3      49823      60041      72912          0          0          0     214619
## 4     178753     241504     270510     244068      82844     176880     509074
## 5      11772      14962      20665      24049      20438      18434      21149
## 6        543        561        560        658          0        735        881
## 7        183        185        187        248        279        296        343
##   2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03
## 1        602        744        752        819        846       1024       1746
## 2      78300      98016     127000     144255     141256      61137      68034
## 3      99671     100760     161688          0          0          0     372766
## 4     356767     499445     589420     502575     181206     278018    1076090
## 5      23818      48887      39671      40607      37051      33815      39293
## 6        599        665        640        551          0        916        634
## 7        367        443        542        741        833        998       1177
##   2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10
## 1       2585       3045       3168       3575       3068       3460       4778
## 2     170837     189088     219430     108297     197535     155642     117405
## 3     117775     137180          0     242440          0          0     292394
## 4     805783     644962     815037     856334     386198     475934    1368120
## 5      47842      38354      41674      44511      30903      29183      58891
## 6        651        698        643        592          0        579        536
## 7       1695       2273       2779       3192       3487       3689       3878
##   2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17
## 1       4652       5362       5499       5628       5281       5477       5505
## 2     220519     196205     184577     200869     192936     157465      83387
## 3     134942     179125     159161     162508          0          0     331467
## 4     769444     909434     840531     859485     394376     468310     661569
## 5      30891      33040      31001      40323      19000      18250      44104
## 6        487        599        618        765          0        867        736
## 7       4169       4206       4187       4123       4007       4021       3998
##   2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24
## 1       5873       5928       5591       4884       4608       4535       4838
## 2     228123     200966     198865     185600     177335     142487      77666
## 3      94472     157941     157447     141095          0          0     305432
## 4    1113126     998599     711006     822963     286698     356210     903142
## 5      20918      22592      23531      22989      14512       7475      19121
## 6        885       1173       1331       1700          0       2281       2333
## 7       3816       3723       3294       3204       3087       2981       2748
##   2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31
## 1       4541       4526       4738       4474       3913       3669       4211
## 2     211277     170940     156040     144347     137427     104110      57631
## 3     114877     133553     130888     118922          0          0     182123
## 4     478068     728848     488392     601500     169525     192866     531236
## 5      28564       7106      30034      17688       9867       8541      21757
## 6       3063       2245       3157       3320          0       5694       4429
## 7       2551       2204       1952       1743       1538       1557       1509
##   2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 1       3861       4092       3852       3555       3013       3260       3747
## 2     133306     119323     112956     100900      93192      78943      41602
## 3      77873      86222      74368          0          0          0     195755
## 4     414544     327163     253357     371155     104593      80566     339241
## 5      10697      15338      14023      14115       7464       6342      15823
## 6       4266       4371       3656       3917          0       4242       2832
## 7       1236       1245       1183        997        903        912        923
##   2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 1       3330       3162       2866       2523       1726       2136       2227
## 2     102429      81669      76195      67478      62221      52345      28776
## 3      43831      62839      53055      49004          0          0      68706
## 4     222972     188875     172066     210402      55352      56932     164053
## 5       9023      10806      10923       7664      10790       4401      12519
## 6       3326       3272       3773       3345          0       3648       2800
## 7        819        776        783        657        607        613        601
##   2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 1       1982       1793       1569       1376        997       1013       1052
## 2      71023      59869      58055      53700      50675      42202      24484
## 3      34380      37108      34213      30615          0          0      48778
## 4     115055     115253     101335     145871      33556      22053      80981
## 5       5808       8177       7254       7255       3980       3163       2818
## 6       2490       2884       2457       1208          0       2292       2307
## 7        547        498        447        452        434        442        416
##   2022-02-22 2022-02-23
## 1        841        627
## 2      60137      49162
## 3      22193      33912
## 4      90495      84805
## 5       8547       9211
## 6       1815       1373
## 7        394        365
## 
## $Growth.Rate
##        geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1 SAUDI ARABIA        NaN        NaN        NaN        NaN        NaN
## 2        ITALY        NaN        NaN        NaN        NaN        NaN
## 3        SPAIN        NaN        NaN        NaN        NaN        NaN
## 4           US         NA          0         NA          0        NaN
## 5       CANADA        NaN        NaN         NA          0         NA
## 6        LIBYA        NaN        NaN        NaN        NaN        NaN
## 7        QATAR        NaN        NaN        NaN        NaN        NaN
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN         NA          0        NaN        NaN        NaN
## 3        NaN        NaN        NaN         NA          0        NaN        NaN
## 4         NA          0         NA          0        NaN         NA          0
## 5          0        NaN         NA          0        NaN        NaN        NaN
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN         NA          0        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN         NA          0        NaN
## 4        NaN         NA          0        NaN        NaN        NaN         NA
## 5         NA          0         NA          0        NaN        NaN        NaN
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 3        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 4          0         NA          0        NaN        NaN        NaN        NaN
## 5        NaN        NaN        NaN        NaN        NaN         NA          0
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN         NA   2.470588   2.214286  0.7956989   1.256757
## 3        NaN        NaN        NaN        NaN        NaN        NaN         NA
## 4        NaN        NaN         NA   0.000000        NaN        NaN        NaN
## 5        NaN        NaN         NA   0.000000        NaN         NA   2.000000
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1        NaN        NaN        NaN        NaN        NaN         NA  0.0000000
## 2   1.408602  1.5419847   1.153465  1.0300429  2.3583333  0.6042403  1.3625731
## 3   1.750000  0.2857143   8.500000  0.7647059  3.0000000  0.9230769  1.2500000
## 4        NaN         NA   0.000000         NA  0.8750000  3.2857143  0.8260870
## 5   0.000000         NA   0.500000  6.0000000  0.8333333  1.2000000  0.8333333
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7        NaN        NaN        NaN         NA  2.0000000  0.0000000         NA
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1        NaN         NA  0.0000000        NaN         NA  0.6666667  1.2500000
## 2   1.259657  1.3100511  1.0117035  1.6028278  1.1964715  1.2044236  0.5436839
## 3   1.266667  0.6491228  3.8108108  0.7092199  1.7300000  2.3121387  1.5550000
## 4   1.736842  2.3333333  0.6883117  3.1320755  0.6987952  0.6465517  2.5066667
## 5   1.000000  0.8000000  3.5000000  0.2857143  2.5000000  1.4000000  0.2857143
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7   0.250000  0.0000000        NaN        NaN         NA  0.4285714  2.0000000
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1  0.2000000  24.000000  1.7083333  0.4146341  0.0000000         NA  3.5333333
## 2  2.3674514   1.146131  0.9607695  1.3729878  1.0265942  0.9005571  1.0906279
## 3  0.9356913   0.000000         NA  0.3922166  1.2139776  1.5238095  0.8423507
## 4  1.9414894   1.202740  1.4419134  1.1990521  0.3083004  6.2692308  1.2494888
## 5  8.7500000   0.400000  5.1428571  0.3194444  2.6086957  2.5666667  0.5324675
## 6        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 7 39.6666667   0.000000         NA  0.2931034  3.7647059  0.5937500  0.0000000
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1   0.000000         NA  0.6796117  0.6857143  2.4791667  0.4285714  4.0196078
## 2   1.193137  1.2650345  1.1247651  1.0953892  0.8479488  0.8613309  1.0960535
## 3   1.197121  1.8746531  0.6037503  2.0286065  0.6837228  1.8762522  0.7457601
## 4   1.449536  1.6913813  1.4167779  0.9415737  1.4877398  1.2503644  0.9521162
## 5   2.439024  0.9450000  1.3439153  1.8818898  0.8200837  1.4158163  1.0540541
## 6        NaN        NaN        NaN        NaN        NaN        NaN         NA
## 7         NA  0.6153846  1.2500000  1.1000000  1.1818182  0.5384615  3.5714286
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1  0.6487805  0.8421053  0.8214286  1.0760870  0.9696970  1.6041667  0.7142857
## 2  0.9925700  1.1905950  0.9526036  1.0110002  0.8732842  0.7763082  1.0007407
## 3  2.0277953  0.8588785  0.9591343  0.9474348  0.9147153  1.1412364  1.0154219
## 4  1.1421172  1.4695308  1.0432075  1.1921898  0.7276214  1.3737211  1.1908008
## 5  0.8752137  1.6660156  0.8288394  1.1711457  1.0966184  1.2632159  0.9642546
## 6  0.0000000        NaN        NaN         NA  2.5000000  0.0000000         NA
## 7  0.4400000  1.0909091  1.0833333  2.1538462  1.5714286  1.3409091  1.4915254
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1  1.4272727  1.0509554  0.9333333  0.9090909  1.5928571  0.9103139  0.9359606
## 2  1.1798668  0.9761606  0.9822194  1.0479826  0.8982310  0.8338740  0.8444012
## 3  1.0286180  0.9697376  0.8976972  0.9768713  0.7860525  0.9180358  1.0473255
## 4  1.1926386  1.0198964  1.0035838  0.9902807  0.9169358  1.0850489  0.9475559
## 5  1.1582278  1.3208431  0.7919622  1.0134328  0.9145803  1.0998390  0.9758419
## 6  0.0000000         NA  0.0000000         NA  0.0000000         NA  1.0000000
## 7  0.6136364  2.1111111  1.1052632  1.9841270  1.1160000  0.8172043  0.9868421
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1  0.7210526  2.5912409  1.0253521  1.0494505  1.1230366  1.1002331  0.9216102
## 2  1.2622573  1.0959333  0.9398192  1.1880537  0.8717512  0.7705279  0.9425944
## 3  1.1919499  0.7967506  1.0097961  0.9411998  0.8001683  0.8590957  0.7472460
## 4  1.0336937  1.1458588  0.9642807  0.8408224  0.9161154  1.0223432  1.0483692
## 5  0.9804951  1.0520275  0.8996364  1.0565885  0.9326702  1.3404430  0.9957160
## 6  1.0000000  3.0000000  0.0000000        NaN         NA  1.0000000  9.0000000
## 7  0.6800000  1.0849673  0.8192771  1.5882353  1.1620370  1.0039841  0.7817460
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1  1.1333333 1.05070994  1.4710425  1.4855643  0.9611307  1.0312500  1.0222816
## 2  0.8973755 1.41957255  0.9226096  0.9994274  0.8728158  0.7404004  1.2096631
## 3  2.0896806 1.43131491  0.8065444  0.1505687  7.8331454  0.2210708  2.5833333
## 4  0.9040564 1.15758876  1.1053288  0.8404281  0.9303363  1.1710152  0.8634850
## 5  1.0196681 1.01748041  1.2049763  0.6932153  1.1971631  0.9887441  0.9838226
## 6  1.4444444 0.07692308  0.0000000        NaN         NA  0.0000000        NaN
## 7  1.4365482 1.38515901  1.4285714  0.6160714  1.2753623  1.2886364  0.9135802
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1   0.994769  1.0148992  1.0120898  1.0213311  1.0217210  1.0539657  0.9821567
## 2   1.234885  0.7851632  1.1417234  0.7802052  0.9859992  0.7482788  1.2024152
## 3   1.061240  1.1006887 -2.1648328 -0.2905123  0.5931389  1.0589936  0.7143637
## 4   1.133374  1.1018207  0.9865645  0.9725440  0.8414615  0.9421959  0.9923667
## 5   1.104750  1.0766262  0.9498208  0.8501348  0.8325935  1.2498096  1.0182815
## 6         NA  0.1250000  1.0000000  0.0000000        NaN        NaN        NaN
## 7   1.173745  1.0246711  1.2215088  1.0946124  1.1152461  1.0301399  0.7074190
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1  1.0466035  1.0196226  0.9948187  1.0133929  1.1395007  1.0599227  0.9696049
## 2  0.9976088  0.8974113  1.0496795  0.9669211  0.7310526  0.8790497  0.8804259
## 3  1.6391437  0.2416045  3.4382239  0.7669848  0.6471449  0.6165158  2.4183486
## 4  1.0885397  1.0923886  1.2136737  0.7741048  0.8746750  0.9998744  1.0022194
## 5  0.9772591  1.0391917  1.0424278  0.8728095  0.8685233  1.0492170  0.9758351
## 6        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
## 7  0.9497784  1.3141524  0.8130178  1.1295488  0.8750000  0.9425626  1.4859375
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1  1.0576803  1.0628334  0.9486893  1.0017637  1.1220657  1.0282427  0.9720244
## 2  1.3432558  0.9702216  0.9471806  0.8161266  0.7405355  0.9276808  1.8844086
## 3  0.7556904  1.1265060  1.2566845  0.5113475  1.0707351  3.9974093  0.1924822
## 4  1.0228137  1.1234119  0.9944364  0.8977219  0.7624033  1.0182722  1.2188992
## 5  1.1456664  0.8671329  1.0102639  0.8592163  0.8986486  1.0432331  1.0963964
## 6         NA  0.0000000        NaN        NaN        NaN        NaN        NaN
## 7  0.8727655  1.1060241  1.4281046  0.8619375  1.0522124  0.9276703  1.3834995
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1  0.9968603   1.070341  1.1314370  1.2310360  0.9633803  0.9477339  0.9676051
## 2  0.6333809   1.117117  0.7953629  1.1089987  0.7714286  0.6681481  1.8026608
## 3  1.1127946   1.284418  0.7573616  0.8009331  0.0000000         NA  0.4746696
## 4  0.8593259   1.301007  0.9579932  0.9697761  0.7614903  1.2294547  0.9097388
## 5  0.9819228   0.934728  1.0841540  0.9777044  0.9560811  0.9125442  1.0726041
## 6        NaN        NaN        NaN         NA  0.0000000        NaN         NA
## 7  0.9108781   1.246763  0.6653203  1.3417173  1.0549451  0.8363971  1.1992674
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1  1.0725389  0.9409142  1.0434439  0.9242998  0.9823915  0.9316382  0.8639821
## 2  0.8179582  0.9654135  1.0155763  1.0260736  0.7937220  0.5649718  1.3233333
## 3  1.2018561  0.9305019  3.7074689  0.2607722  1.0343348 -0.7717842 -2.3091398
## 4  1.0981947  1.1245957  0.9422009  0.8428536  1.0012899  0.9375186  0.9872628
## 5  1.1308664  0.9393456  0.9473237  0.8484305  1.1088795  0.9027645  0.8130940
## 6  0.3333333  2.0000000  0.5000000  3.0000000  0.0000000        NaN         NA
## 7  0.9108125  1.0422535  1.1776062  0.9464481  0.8666282  1.1665556  0.9948601
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1  0.9399275  0.9057851  0.9616788  1.0234029  1.1600742  1.0021311  0.9936204
## 2  1.4710327  1.0154110  0.8701518  0.8062016  0.8004808  0.6006006  1.5900000
## 3  0.0000000         NA  0.3995143  1.0091185  0.3780120  0.6334661  1.8490566
## 4  1.0125268  1.1138839  1.1980729  0.9166832  0.7903881  0.9440070  1.2285681
## 5  1.1051948  0.9929495  0.9455621  0.9399249  0.8548602  1.2320872  0.8761062
## 6 11.0000000  0.2727273  2.1666667  0.9230769  2.1666667  0.4615385  1.1666667
## 7  0.9988519  1.1304598  1.0132181  1.1816357  0.6997877  0.9241505  1.1989494
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1   1.161584  0.9097190  1.3118987  1.2045542  0.9756488  1.1064039  0.9759573
## 2   1.009434  0.5514019  2.9265537  0.5212355  0.7296296  1.4213198  1.0107143
## 3   1.340136  0.8477157  0.9520958  1.0440252  0.7228916  0.6958333  1.4910180
## 4   0.909901  1.4534950  0.9400734  0.7713311  0.8722714  0.9537820  1.1988536
## 5   0.953824  0.9652042  0.9357367  1.2428811  0.8167116  0.7673267  0.9376344
## 6   1.000000  0.9285714  2.3076923  0.5666667  0.0000000         NA  0.3552632
## 7   1.041073  0.8316675  1.1094244  0.9692132  0.9382353  0.8576803  1.2580409
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1  1.1304745  1.0043045  1.0503616  0.8584545  1.2575758  1.0647295  0.9467495
## 2  0.7137809  1.8762376  0.4300792  2.1226994  0.9768786  0.8905325  0.6976744
## 3  1.2610442  1.3598726  1.1756440  0.7888446  0.8156566  0.5603715  1.2099448
## 4  1.0671366  1.0198623  1.1678971  0.9334910  0.7931536  1.0974230  1.1774055
## 5  1.1192661  0.8504098  0.9807229  1.0565111  0.7976744  1.0787172  0.9783784
## 6  0.7037037  0.7894737  1.0666667  0.5625000  4.0000000  0.3611111  1.3076923
## 7  0.9970947  0.8601399  1.0277778  1.2050099  0.6487965  1.0741990  0.9427002
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1  1.1528006  0.9670665  0.9041413  0.9162985  0.8573966  1.0041432  0.9251400
## 2  1.5619048  1.0091463 -0.4471299 -1.7837838  0.8484848  0.9866071  0.5113122
## 3  1.6210046  1.6478873  0.5247863  1.1824104  0.9201102  0.6946108  1.0689655
## 4  1.0952207  1.0282034  1.1365011  1.0084252  0.8139644  1.1403434  1.2969004
## 5  1.0773481  0.9794872  0.9816754  0.9626667  0.8504155  1.0293160  1.1740506
## 6  0.9411765  0.6250000  1.0000000  2.4000000  1.1250000  0.8888889  1.8333333
## 7  0.9134055  1.1549681  0.8058406  1.0048972  0.8586745  1.1736663  1.1373308
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1  0.9949028  1.0797310  1.1678529  0.9972067  1.0157881  0.9884683  1.1126046
## 2  5.1061947  0.5129983  0.8614865  0.6862745  0.9942857  0.7241379  1.1269841
## 3  1.3467742  1.1976048  1.0475000  1.3460621  0.5336879  0.6644518  1.5050000
## 4  0.9484423  1.0758459  1.2670622  0.8328389  1.0240865  0.9857555  1.1853337
## 5  0.8490566  1.0412698  0.7835366  1.2684825  0.8466258  1.5036232  0.7614458
## 6  0.7045455  0.9032258  0.5357143  0.9333333  2.5000000  1.1428571  0.5500000
## 7  1.0195578  0.8840701  0.8924528  0.9291755  0.8532423  0.9240000  1.4170274
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1  0.7754730  0.9944150  1.2394325  0.9844980  0.8672481  1.1751397  0.8062753
## 2  1.2816901  1.1043956  1.1094527  1.0538117  0.8170213  1.0833333  0.6586538
## 3  1.2890365  1.1443299  0.9954955  0.0000000        NaN         NA  0.2741158
## 4  1.0024503  1.1026423  0.9525127  0.8837876  1.1659231  0.8023258  1.3158923
## 5  0.7025316  1.5900901  0.9065156  0.6750000  0.9120370  1.9949239  0.7099237
## 6  2.2727273  0.3400000  1.5882353  2.6296296  0.8028169  1.2456140  0.9154930
## 7  0.9317719  0.9770492  0.8456376  0.7010582  1.1622642  0.8863636  1.0989011
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1  0.8950472  1.0484190  0.9924599  0.9477683  0.9281897  1.0262684  0.9438990
## 2  1.4087591  1.1088083  1.2897196  0.6811594  1.2446809  0.7222222  0.6745562
## 3  1.1231672  1.4177546  1.5690608  0.0000000        NaN         NA  0.3256724
## 4  1.0792419  0.9728224  1.1582413  0.8830346  0.9684701  1.0370446  1.0665738
## 5  1.0215054  1.2771930  0.9148352  0.6936937  0.8874459  2.6341463  0.7666667
## 6  1.3230769  0.8604651  0.0000000         NA  0.9361702  1.7954545  0.6455696
## 7  1.0133333  0.9161184  0.9335727  0.9576923  0.9437751  0.8893617  1.2368421
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1  0.9921991   1.034818  0.9453690  0.9816303  0.9762183  0.9700479  1.0193495
## 2  1.4210526   1.419753  1.0043478  1.0779221  0.8755020  0.8715596  0.6736842
## 3  1.3138138   1.555429  1.0286554  0.0000000        NaN         NA  0.2964418
## 4  1.0195663   1.060572  0.9474358  0.9709245  0.9142098  1.0330775  1.0989676
## 5  0.8671498   1.206128  1.0623557  0.7826087  0.8361111  2.5481728  0.7079531
## 6  0.5098039   2.423077  0.8253968  1.6730769  0.8620690  1.5200000  0.9473684
## 7  0.8704062   1.097778  0.8522267  0.9738717  0.8292683  1.1441176  1.0102828
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1  0.9414378  0.9601030  1.0625559  0.9255677  0.8941390  1.0127033  0.9518314
## 2  2.1875000  1.0928571  0.8235294  1.0873016  0.9270073  0.6614173  1.2023810
## 3  0.9992636  1.9270450  0.8623327  0.0000000        NaN         NA  0.2873762
## 4  1.0134286  0.9985016  1.1094748  0.8331278  0.9026056  1.0633244  1.0774308
## 5  0.7513812  1.5906863  0.7765794  0.7301587  0.9048913  1.8918919  0.5682540
## 6  0.8148148  1.5681818  0.7971014  1.1181818  0.9918699  1.2950820  1.2025316
## 7  1.1221374  0.8458050  1.0563003  1.0101523  0.6758794  1.0855019  0.9691781
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1  0.9272536  0.9260944  1.0349908  0.9329775  0.8626828  0.9270450  1.0834658
## 2  1.4257426  1.3263889  0.9921466  0.7783641  0.8067797  0.6680672  1.1949686
## 3  1.1110503  1.3732152  1.1086411  0.0000000        NaN         NA  0.6751055
## 4  0.9965060  1.0457670  0.9741670  0.8752180  0.8213079  0.8621810  1.5109110
## 5  1.3324022  0.8888889  1.0990566  0.5815451  0.8708487  1.0847458  2.7343750
## 6  1.0789474  1.0536585  0.8472222  0.3825137  2.0857143  1.5479452  0.7123894
## 7  0.9646643  1.1245421  0.7654723  0.9191489  0.9074074  1.0969388  1.0046512
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1  1.0036684  1.0248538  1.1176890  0.9374601  0.9720899  0.8802521  1.2100239
## 2  2.0210526  1.0442708  1.3765586  0.6286232  1.3342939  0.5593952  1.5907336
## 3  0.5126736  1.3843549  1.1024951  0.0000000        NaN         NA  0.4214435
## 4  0.8851360  1.0908609  1.0265625  1.0410344  0.8598754  0.9473166  1.0834682
## 5  0.5214286  0.9123288  1.3333333  0.5045045  0.9955357  2.8026906  0.4768000
## 6  1.5590062  1.6095618  0.4950495  0.7650000  1.4313725  2.1826484  0.7803347
## 7  1.2361111  1.0749064  1.0139373  0.9175258  1.1123596  1.0606061  1.2190476
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1  1.0315582  0.9445507  0.9331984  1.0216920  0.8683652  1.1181744  1.0269679
## 2  1.1553398  1.0966387  1.0996169  1.0958188  0.7583466  0.6708595  1.2531250
## 3  0.8733480  2.3802018  0.7256954  0.0000000        NaN         NA  0.3143402
## 4  0.9842475  0.9272566  1.3779514  0.7315397  0.6825526  1.0752024  1.1618739
## 5  1.1174497  1.4084084  0.8656716  0.5492611  0.7847534  1.0571429  4.9297297
## 6  0.8284182  1.4207120  0.6309795  1.4837545  1.0559611  0.9377880  1.2014742
## 7  0.7604167  1.1746575  0.7317784  1.1035857  0.9783394  1.0627306  1.0173611
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1  0.9673527  0.9442406  0.9425019  0.9760923  0.9366554  1.0595131  0.9480851
## 2  1.6009975  1.3084112  1.1273810  1.1309398  1.1288515  0.7882548  0.9192025
## 3  1.3044583  1.0551641  1.1575508  0.0000000        NaN         NA  0.3671964
## 4  1.0537612  0.9487880  1.1042347  0.9159310  0.7390115  1.0262352  1.2348682
## 5  0.3782895  0.6347826  3.1735160  0.3237410  1.1955556  2.6988848  0.4944904
## 6  0.8077710  0.6177215  1.6967213  0.7632850  0.0000000         NA  0.4755245
## 7  1.0068259  0.9084746  0.9589552  1.1050584  0.8556338  1.0617284  0.8992248
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1  0.9587074  0.9541199  1.0490677  0.9232928  0.9219858  1.0450549  0.9442692
## 2  1.5593607  1.0314788  1.0361959  0.9890411  0.9452909  0.7296703  0.9789157
## 3  1.0251510  1.3237390  1.0125285  0.0000000        NaN         NA  0.3442644
## 4  1.0199124  1.1743752  0.9375974  0.9205294  0.7781173  1.0075275  1.2287157
## 5  1.2033426  1.0231481  1.2466063  0.6370236  0.7435897  3.8237548  0.5100200
## 6  2.0330882  0.7956600  0.8068182  0.9267606  1.4133739  1.1677419  1.2117864
## 7  1.0517241  1.0081967  0.8455285  1.0144231  0.7962085  1.2083333  1.0640394
##   2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1  0.9086860   1.020833  0.9867947  0.9622871  0.9557522  1.0158730  1.0169271
## 2  1.3600000   1.053544  1.2397996  0.9780600  0.7650531  0.8873457  1.1904348
## 3  1.0574245   1.044051  1.1693269  0.0000000        NaN         NA  0.3375000
## 4  0.9658479   1.125389  1.0671959  0.8889464  0.7036243  0.8235781  1.0621405
## 5  1.0805501   1.109091  1.0344262  0.6212361  0.9923469  1.0591260  3.5072816
## 6  0.8085106   1.159774  1.0891410  0.9657738  1.0092450  1.6564885  0.6903226
## 7  0.9814815   1.009434  1.0140187  1.0460829  1.0176211  1.0952381  0.9130435
##   2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1  0.9923175  0.9135484  0.9703390  0.9359534  0.9346812  1.0099834  1.1070840
## 2  1.0445581  1.1167832  1.0118973  0.9288366  0.9700200  0.6923077  1.2192460
## 3  0.9890674  1.2140762  1.1318283  0.0000000        NaN         NA  0.3443658
## 4  1.2596562  1.1777243  1.1496031  1.0060838  0.8829449  0.8359078  1.0828275
## 5  0.2906574  2.1428571  0.8766667  0.7173638  0.9805654  2.4360360  0.6079882
## 6  1.1735648  0.5426621  2.0314465  0.4540764  0.9840909  1.6951501  0.8569482
## 7  1.1558442  0.7715356  1.1407767  1.0042553  0.9194915  1.0829493  1.0170213
##   2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1  0.9241071  0.9549114  0.9713322  0.9565972  0.8765880  1.0186335  1.1219512
## 2  1.1814483  1.0902204  1.2046747  0.8584164  0.9694563  0.8506616  1.0303704
## 3  1.1860761  1.0087555  1.2743778  0.0000000        NaN         NA  0.3436108
## 4  1.0761053  1.1404193  1.1514817  0.8719246  0.9576638  0.7559560  1.1506099
## 5  1.1484185  1.0296610  1.2695473  0.7560778  0.9817792  1.8100437  0.7726176
## 6  1.2591415  1.1186869  0.6952596  1.2922078  0.8982412  1.1846154  0.7674144
## 7  0.9832636  1.0382979  0.9180328  1.0223214  1.0043668  0.9913043  1.3728070
##   2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1  1.0163043  0.8877005  0.9477912  0.9766949  0.8741866  1.1290323  1.1846154
## 2  1.1790079  1.0890244  1.0705487  0.9775105  0.9448903  0.8459796  1.1024096
## 3  1.0453746  0.9436620  1.1519760  0.0000000        NaN         NA  0.0000000
## 4  1.1262948  1.1566852  0.9809996  0.9058316  0.8044484  1.0876260  1.1076874
## 5  1.0202966  1.0229533  1.2086761  0.8849010  0.9846154  1.6583807  0.7790150
## 6  1.0015385  0.8218126  1.2299065  0.8176292  0.9962825  1.5839552  0.9434629
## 7  0.8242812  0.9689922  0.9000000  0.8888889  1.1700000  0.9700855  0.9779736
##   2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1  0.7755102  1.1770335  0.9776423  0.8711019  0.9307876  0.9717949  1.2585752
## 2  1.1232544  1.3772973  0.9807692  1.1376551  0.9067886  0.8754849  1.1860877
## 3         NA  0.4501960  1.2023569  0.0000000        NaN         NA  0.5109881
## 4  0.9307914  1.1928686  1.1501512  0.9230451  0.6724376  1.1332406  1.1810876
## 5  1.0863112  0.9114372  1.2443087  0.8299866  0.8897849  1.4851964  0.8136697
## 6  0.6379526  1.3365949  0.7452416  0.7269155  1.9513514  0.8698061  1.6417197
## 7  1.0225225  0.8766520  1.0301508  0.8536585  0.9085714  1.2201258  1.2938144
##   2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1  0.9811321  0.8995726  0.9667458  0.9950860  0.7975309  1.0773994  1.3620690
## 2  1.3735525  1.2124014  1.2050247  1.0655249  0.9531796  0.8465909  1.2768998
## 3  0.8743957  1.1841578  1.0293810  0.0000000        NaN         NA  0.2555284
## 4  1.1157098  1.1705667  0.9568175  0.9965993  0.8386242  0.9480564  1.1318373
## 5  0.9380000  1.3086354  1.0574338  0.7222650  0.9722667  0.9517279  1.9417867
## 6  1.0135790  0.7454545  1.3812580  0.2955390  3.2264151  1.0808967  1.0495942
## 7  0.9482072  0.8949580  0.9671362  0.8640777  1.1629213  0.9951691  1.0388350
##   2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1  1.0569620  0.9421158  0.9173729  0.8290993  0.9693593  1.0948276  1.0104987
## 2  1.2431333  1.2006274  1.1369988  1.0915176  1.0713959  0.7976933  1.1642926
## 3  1.6816521  1.1126149  1.1402613  0.0000000        NaN         NA  0.3661485
## 4  1.1670062  1.1102681  1.0776942  0.7923588  0.9519643  1.3143623  0.9005187
## 5  0.7117839  1.0529608  0.9714851  0.7737464  0.9483667  1.7205556  0.8082015
## 6  0.7182131  1.0227273  1.3672515  0.0000000         NA  1.2264550  0.8257118
## 7  0.9252336  1.0101010  0.9450000  1.2433862  0.8680851  1.1764706  1.1375000
##   2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1  1.0519481  0.9901235  0.9551122  1.0313316  0.8177215  1.1052632  1.1176471
## 2  1.3981234  1.0578328  1.1906332  1.0259625  1.0831466  0.7994641  1.2929382
## 3  1.2234556  1.2364343  0.9459163  0.0000000        NaN         NA  0.3529164
## 4  0.9826074  1.2924440  1.0516727  0.9383525  0.7967493  1.0527407  1.1814936
## 5  1.0307631  0.9352713  1.2387070  0.6878555  1.0170233  2.0516499  0.6249417
## 6  0.7513062  1.3838665  0.7678392  1.2958115  1.6555556  0.7382550  0.6214876
## 7  0.9743590  0.9473684  0.9880952  1.0200803  0.8070866  1.2780488  0.9809160
##   2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1  1.0426065  1.0456731  0.9149425  1.0100503  0.9303483  1.0187166  1.2414698
## 2  1.1363864  1.0736754  1.1584107  1.0217832  0.9417118  0.7441231  1.2690873
## 3  1.0731350  1.1930180  1.0854538  0.0000000        NaN         NA  0.3393191
## 4  1.1178156  1.0656699  1.0932384  0.8868849  0.8602768  1.0861380  1.4911218
## 5  1.0298396  1.0829410  1.1043478  0.7362205  0.9827232  1.3733780  1.4827796
## 6  1.1954787  0.8698554  1.2429668  0.4804527  2.0342612  0.9073684  0.9060325
## 7  0.9727626  0.8440000  0.9146919  1.1036269  0.7699531  1.2012195  1.1472081
##   2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1  0.9006342  1.0563380  0.9688889  0.9334862  0.8918919  1.0798898  1.2015306
## 2  1.0816897  1.1294356  1.0956466  1.0530924  0.8192620  0.7747900  1.3886580
## 3  1.3413680  0.8748503  1.0277524  0.0000000        NaN         NA  0.0000000
## 4  0.8145132  1.2758905  0.9733982  1.0280330  0.9177740  1.0225954  1.1220016
## 5  0.5967112  1.2979676  1.2032909  0.9373622  0.9327059  1.1755802  0.9538627
## 6  1.1510883  0.9488320  1.1770223  0.5926295  1.8117647  0.8562152  1.0509209
## 7  1.0044248  1.0969163  0.7710843  1.0520833  0.9405941  1.2105263  1.0000000
##   2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1  0.8365180  0.7893401  1.4180064  0.7913832  0.8739255  0.9868852  1.2026578
## 2  0.9393274  1.1521798  1.0770203  0.9106890  0.9121587  0.8050152  1.1769158
## 3         NA  0.5346798  1.0953308  0.0000000        NaN         NA  0.3438194
## 4  1.0841853  1.0861170  1.1181799  0.9277662  0.8407806  1.1252492  1.0400087
## 5  0.9079865  1.3587711  0.8780088  0.9154725  0.9174229  1.4638971  0.7464527
## 6  0.9020619  1.0502857  0.8966268  0.0000000         NA  0.7412731  0.8476454
## 7  0.9739130  1.0937500  0.9591837  0.8638298  1.0591133  1.1302326  0.7983539
##   2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
## 1  0.8011050  1.1000000  0.8965517  0.7727273  1.0135747  1.0312500  1.0909091
## 2  1.0648939  1.0553092  1.0293841  0.9335374  0.8151248  0.8090835  1.0130850
## 3  1.1640702  1.0597336  0.9336537  0.0000000        NaN         NA  0.4723789
## 4  1.0590728  1.1155626  1.0460383  0.9015253  0.8062178  1.2536768  0.9428078
## 5  1.0558950  1.0171453  1.0225453  1.0135998  0.9849563  1.4379773  0.7189608
## 6  0.8643791  1.0226843  1.4824399  0.0000000         NA  0.6403941  1.0876923
## 7  1.1288660  0.9497717  1.1490385  0.7280335  0.9597701  1.1137725  1.2204301
##   2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
## 1  1.2936508  0.9877301  0.9378882  0.7284768  0.9863636  1.0691244  1.1336207
## 2  1.1130150  1.1218088  0.9772766  0.9284807  0.7845715  0.7931803  1.1814240
## 3  0.8359503  1.2022109  0.8831475  0.0000000        NaN         NA  0.4132839
## 4  1.0437757  0.6471000  1.8317910  0.7974390  0.8102465  1.1403530  1.2452485
## 5  1.0588940  1.0380845  1.0844533  0.9536091  0.9580260  1.4287809  0.7273544
## 6  0.8727016  0.9886548  1.4196721  0.0000000         NA  0.3275713  1.6042216
## 7  0.9207048  1.0287081  0.8558140  1.2336957  0.7533040  1.0818713  0.9081081
##   2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
## 1  0.9467681  0.9236948  1.0173913  0.8119658  0.9842105  1.1176471  0.9234450
## 2  1.0703985  1.1212033  1.0383737  0.8731647  0.8971594  0.7242548  1.0846553
## 3  1.1300715  1.0853070  0.8635331  0.0000000        NaN         NA  0.0000000
## 4  1.0058224  1.1139888  1.0686462  0.9543103  0.7961657  1.1024663  1.1421870
## 5  1.1181866  1.0294999  1.0301869  0.8862115  1.0253441  1.2971026  0.7664059
## 6  1.1019737  1.1373134  0.8923885  0.0000000         NA  0.4919125  1.7195358
## 7  1.5178571  0.8666667  0.7511312  0.8433735  0.8928571  1.4240000  0.6573034
##   2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
## 1  0.8238342  0.8867925   1.191489  0.9880952  0.8373494  0.8992806  1.1360000
## 2  0.8596751  1.3326539   1.101659  1.0628004  0.9012662  0.6704020  1.2340125
## 3         NA  0.8139773   1.322313  0.0000000        NaN         NA  0.4846778
## 4  0.9826903  1.0612705   1.026977  0.9251139  0.8623937  1.1052390  1.0526512
## 5  1.0395192  1.0544907   1.046868  0.8753049  0.9277049  1.4827708  0.7627220
## 6  0.6029246  1.2332090   1.054463  0.0000000         NA  0.6429366  1.1418685
## 7  1.2820513  1.0866667   1.006135  0.8963415  0.9115646  1.1940299  0.9437500
##   2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
## 1  1.2676056  1.0055556  0.9613260  0.9080460  1.0253165  1.0370370  1.0773810
## 2  1.1839073  1.0378529  0.8446772  1.0586975  0.9262190  0.7197060  1.2251357
## 3  1.0726181  1.0950533  0.9739510  0.0000000        NaN         NA  0.4839867
## 4  1.0609460  1.0092194  1.0481910  0.7935940  0.9729904  1.0387049  1.0126383
## 5  1.0370313  1.1253578  0.9441692  0.8882587  0.9613665  1.2484225  0.8204310
## 6  0.8484848  1.2607143  0.6926346  0.0000000         NA  0.8121827  0.7906250
## 7  0.9602649  0.9655172  1.1357143  0.8930818  1.0070423  1.1048951  0.9430380
##   2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
## 1  0.9779006  1.0677966  0.9417989  0.9157303  0.9447853  0.7727273  1.2521008
## 2  1.0444578  1.2970233  1.0553246  0.5465672  0.8589140  0.9601656  1.3063745
## 3  1.1625680  1.0222832  0.0000000        NaN        NaN         NA  0.5759545
## 4  1.0937342  0.9505811  0.5706586  1.8804557  0.5981645  1.2786191  1.1669365
## 5  1.1058690  1.0033719  0.8588545  0.8977543  1.0130756  1.0065470  1.6246051
## 6  1.2648221  1.3218750  0.5449173  0.0000000         NA  0.7307692  0.8214286
## 7  0.9395973  1.1214286  0.8216561  1.3100775  0.9408284  1.0062893  1.2875000
##   2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
## 1  0.7583893  1.2389381  0.9785714  0.7372263  0.8118812  1.1463415  1.1063830
## 2  1.4453167  1.4490186  0.9460323  0.5324178  1.2046512  0.7580204  1.4238748
## 3  1.1864575  1.0796243  0.0000000        NaN        NaN         NA  0.7750417
## 4  1.0855153  1.4188954  0.5524666  1.6381067  0.7224817  0.9093159  1.2387806
## 5  0.8884695  0.8926226  0.7937401  1.1481010  1.4678696  1.0542377  0.7466503
## 6  1.3386728  0.5846154  1.3654971  0.0000000         NA  0.8373134  0.8573975
## 7  0.9368932  1.1036269  0.9765258  0.9519231  0.9949495  1.0507614  1.0048309
##   2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
## 1  1.1346154  0.9152542  0.8981481  1.1340206  1.0636364  1.1965812  1.0500000
## 2  1.3220163  0.9060317  0.9518354  1.1395972  0.9323688  0.6727517  1.1366321
## 3  0.0000000         NA  0.6009443  0.0000000        NaN         NA  0.4141513
## 4  1.1322977  1.1062674  1.0577899  0.8651835  0.8082585  0.9562900  1.0827526
## 5  1.1473973  1.0329513  1.0833333  0.7476795  1.0361016  1.0765735  0.8368939
## 6  0.8814969  1.4976415  0.7669291  0.0000000         NA  0.8519515  1.0300158
## 7  1.0048077  1.0047847  0.9285714  1.0564103  0.9368932  1.0518135  1.0394089
##   2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
## 1  1.1904762  0.9657143  1.0236686  0.8092486  1.2571429  0.9659091  1.3294118
## 2  1.1074989  1.0931972  0.9362640  1.0102825  0.7690987  0.7035236  1.1893484
## 3  1.5279896  0.9230492  1.1203802  0.0000000        NaN         NA  0.4068362
## 4  1.0422191  1.0202762  1.0574826  0.8693851  0.8283170  0.7929400  1.0912250
## 5  1.0391318  1.0851721  0.9185306  0.9115998  1.0038854  0.9932269  0.7811333
## 6  0.9815951  1.1937500  0.7630890  0.0000000         NA  0.7292250  0.7631242
## 7  0.9526066  1.0398010  0.9377990  1.0408163  0.9215686  1.2074468  0.9911894
##   2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
## 1   1.053097  0.8907563  1.0047170  0.9248826  0.9441624  1.1451613  1.0469484
## 2   1.292683  1.0358933  0.9699751  0.9778479  0.8722526  0.7360681  1.2365931
## 3   1.212446  1.0668896  0.9668147  0.0000000        NaN         NA  0.3883418
## 4   1.220442  1.0149367  1.0098670  0.9203086  0.7952866  0.9150457  1.1578809
## 5   1.209312  1.0529392  0.9565785  0.8138225  0.9561753  1.2375000  0.7435761
## 6   1.105705  0.9438543  1.2765273  0.0000000         NA  0.6454704  1.1740891
## 7   1.204444  0.9520295  1.0193798  0.9543726  0.9840637  1.1214575  1.0794224
##   2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
## 1  0.9686099  1.1712963  1.0553360  1.0112360  0.9666667  0.9770115  1.2156863
## 2  1.4352797  0.9452966  0.9444986  0.9373295  0.8848423  0.7045071  1.2177918
## 3  1.1056676  0.8663026  1.0922376  0.0000000        NaN         NA  0.3647316
## 4  1.0419898  1.0541828  1.0145506  0.8965166  0.7603065  1.0971299  0.9436753
## 5  0.9887989  1.1557002  0.9762252  0.8803674  0.8684785  1.3134954  0.6258243
## 6  0.8793103  0.9346405  1.2181818  0.0000000         NA  0.7859327  1.3385214
## 7  1.1304348  1.0266272  0.9827089  1.0645161  0.9669421  1.0968661  0.9740260
##   2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
## 1  0.9870968  0.9901961  1.0792079  1.1804281  0.8212435  1.1230284  0.9915730
## 2  1.3658688  1.0359581  1.0407879  0.9455428  0.8661359  0.6846220  1.3316602
## 3  1.0871181  0.9482213  0.9534379  0.0000000        NaN         NA  0.3482748
## 4  1.0426139  1.0198013  1.0499186  0.8843485  0.7776594  0.9147840  1.1377256
## 5  1.1094494  1.2469363  1.0171990  0.7591787  0.9414572  1.2794863  0.7448494
## 6  0.7839147  0.9517923  1.1441558  0.0000000         NA  0.7561837  0.7932243
## 7  1.0560000  1.0277778  0.9778870  0.9899497  1.0355330  1.0465686  1.1170960
##   2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15  2021-02-16
## 1  1.0453258  0.9864499  0.9697802  0.9546742  0.9554896  0.9751553 1.025477707
## 2  1.2200339  1.1691512  0.9178173  0.9733679  0.8178659  0.6640145 1.411764706
## 3  1.1043775  0.9855913  0.8167255  0.0000000        NaN         NA 0.332451820
## 4  1.0193490  1.1044100  0.9519663  0.9009087  0.7523015  0.8146995 1.059221506
## 5  1.1907801  0.9157236  1.0344715  0.8896573  0.7932862  0.9082405 1.899950956
## 6  0.6877761  0.7130621  1.5615616  0.0000000         NA  0.7420719 0.943019943
## 7  0.9454927  0.9933481  1.0044643  1.0066667  0.9713024  2.0181818 0.006756757
##   2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
## 1  1.0372671  0.9790419  1.0305810  0.9643917  0.9692308  1.0380952  1.0244648
## 2  1.1637731  1.1399801  1.1246819  0.9640595  0.9018372  0.7150186  1.3828637
## 3  1.0767625  1.3403823  0.7878057  0.0000000        NaN         NA  0.3578589
## 4  1.1634786  1.0508941  1.0463370  0.9770629  0.7527074  0.9964886  1.3233705
## 5  0.6677852  1.2655586  0.9367746  0.8686012  0.9388138  1.4778089  0.7697511
## 6  0.9425982  1.2564103  1.4923469  0.0000000         NA  0.8792373  1.1783133
## 7 75.5000000  1.0198675  1.0064935  0.9655914  1.0222717  1.0087146  0.9827214
##   2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
## 1   1.053731   1.008499  0.9719101  0.9768786  0.9526627  0.9844720  0.9526814
## 2   1.233326   1.211499  1.0310503  0.9225888  0.9226537  0.7514908  1.3019228
## 3   1.234687   1.038645  0.8717600  0.0000000        NaN         NA -4.6530855
## 4   1.020906   1.037879  0.9779888  0.9389371  0.7301382  0.9711875  1.1381835
## 5   1.020738   1.048554  1.0650246  0.7918594  0.8380062  1.6589219  0.8092437
## 6   1.147239   1.017825  1.0945709  0.0000000         NA  0.8965909  1.0646388
## 7   1.008791   1.013072  1.0086022  0.9808102  1.0152174  1.0128480  0.9788584
##    2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
## 1  1.09602649  1.1329305  1.0240000  0.9947917  0.9345550  0.9831933  1.1111111
## 2  1.22135615  1.0962092  1.0509958  0.9842982  0.8769094  0.6698514  1.4209048
## 3 -0.08254536  0.9837054  1.1022031  0.0000000        NaN         NA  0.3355076
## 4  1.17089499  1.0229591  0.9890249  0.8974415  0.6892705  1.0292108  1.2792060
## 5  0.95777085  1.0780629  1.0972176  0.7091353  1.0297286  1.7405858  0.7137019
## 6  0.73571429  1.6213592  0.8932136  0.0000000         NA  0.8791019  1.0117878
## 7  1.01727862  1.0084926  0.9873684  0.9808102  1.0304348  0.9873418  1.0064103
##   2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
## 1  0.9897436  1.0103627  0.9230769  0.9750000  0.9914530  0.9913793  1.0260870
## 2  1.1292776  1.1514703  1.0444852  0.9716685  0.8184088  0.7159219  1.3359559
## 3  1.7744267  0.8786346  0.8549960  0.0000000        NaN         NA  0.4368727
## 4  1.0367680  1.0766302  1.0182784  0.8784549  0.6972093  1.3761651  1.0064531
## 5  1.0596160  0.9653528  1.1636483  0.8073005  1.0119173  1.3411846  0.7696281
## 6  0.8834951  1.1791209  0.9058714  0.0000000         NA  0.8051852  0.9576817
## 7  1.0042463  0.9894292  0.9722222  1.0615385  1.0041408  0.9917526  0.9958420
##   2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
## 1   1.110169  0.9694656   1.026247  0.9769821  0.9607330   1.100817  1.0148515
## 2   1.130742  1.0807726   1.032368  0.9302136  0.8382470   1.222300  0.7648259
## 3   1.227731  1.0203546   0.000000        NaN        NaN         NA  0.3348916
## 4   1.122476  1.0265288   1.001119  0.9424242  0.5952852   1.417475  1.0834879
## 5   1.140268  1.0797528   1.159444  0.7893747  0.9785587   1.499391  0.7544145
## 6   1.012488  0.9791271   1.098837  0.0000000         NA   0.715981  0.9955801
## 7   1.020877  1.0204499   0.995992  1.0241449  0.9882122   1.031809  1.0289017
##   2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
## 1   1.136585  1.0343348  1.0580913  0.9843137  1.0577689  1.0188324  1.0277264
## 2   1.133785  1.1146098  1.0166800  0.9899485  0.8225224  0.6580290  1.2445736
## 3   0.000000         NA  0.5653178  0.0000000        NaN         NA  0.3221728
## 4   1.645176  0.7775682  1.1202052  0.8313493  0.7182798  1.4812959  0.9105140
## 5   1.155233  1.2242664  0.9792657  0.8457653  0.9710611  1.5059130  0.7870269
## 6   1.008879  1.0033003  0.9692982  0.0000000         NA  0.9495225  1.0143678
## 7   1.067416  1.0298246  1.0255537  1.0199336  1.0407166  1.0798122  1.0434783
##   2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
## 1   1.052158  1.0085470  1.2338983  0.9395604  0.9839181  1.0326895  1.1395683
## 2   1.487823  0.9287060  0.9879643  0.9694301  0.8479785  0.5927735  0.7268727
## 3   1.708851  0.8250527  0.0000000         NA  0.0000000         NA  0.6392857
## 4   1.124142  1.1223490  0.9428981  0.8773321  0.5832592  1.8586037  0.9008642
## 5   1.181201  0.9824295  0.7994841  1.2626371  0.7522998  1.6779891  1.4448043
## 6   1.000000  1.4490085  1.0830890  0.0000000         NA  0.9519071  0.7630662
## 7   1.083333  1.0769231  1.0404762  0.9954233  1.0068966  1.0388128  1.0186813
##   2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
## 1  0.9886364  1.1519796  1.0022173  0.9712389  0.9100228  1.0538173  1.1294537
## 2  1.7629782  1.2574163  1.0996572  0.9274466  0.8966441  0.6214653  1.3741309
## 3  1.3268911  1.1266500  1.0983739  0.0000000        NaN         NA  0.2766444
## 4  1.2100820  1.0630359  1.0495538  0.8268932  0.6700130  1.3597521  1.2767857
## 5  0.7022231  1.0782123  1.1624722  0.7577205  1.0613445  1.4092109  0.7111153
## 6  1.1061644  0.8968008  0.8423475  0.0000000         NA  0.9082177  0.9729730
## 7  1.0140237  1.0095745  1.0010537  1.0147368  0.9968880  1.0124870  1.0082220
##   2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
## 1  0.9768665  1.0602799  0.9786802  0.9834025  0.9662447  1.0589520  1.1030928
## 2  1.2024704  1.0496906  0.9386901  0.9648936  0.8258266  0.6985340  1.3617285
## 3  1.6646535  0.9225702  1.0967608  0.0000000        NaN         NA  0.0000000
## 4  0.9236679  0.9879905  1.0884341  0.6800987  0.7887332  1.4531968  0.9879949
## 5  1.1264156  1.1380641  0.9526451  0.8183093  0.9578337  1.4279818  0.7087669
## 6  0.6533816  0.9482440  1.1169591  0.0000000         NA  0.7797063  1.0702055
## 7  1.0030581  1.0050813  0.9888777  0.8456033  0.9951632  1.0886999  0.9877232
##   2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
## 1  0.9607477  1.0262646  1.0407583  0.9763206  0.8889925  1.0052466  1.0908142
## 2  1.1464082  1.1597282  0.9197308  0.9360347  0.9524396  0.6414836  1.2319905
## 3         NA  0.6103398  1.0847975  0.0000000        NaN         NA  0.3861072
## 4  1.0339905  1.0413093  0.9445449  0.8591572  0.6288519  1.2633491  1.2137392
## 5  1.1421775  0.9790551  1.0472833  0.8717502  0.9131940  1.3136026  0.8054384
## 6  0.9504000  0.8973064  1.0056285  0.0000000         NA  0.8745318  1.0728051
## 7  0.9254237  0.9768010  0.9975000  0.8997494  0.9818942  0.9971631  0.9886202
##   2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
## 1  1.0162679  0.9661017  1.0292398  0.9924242  0.8940840  1.0170758  1.0482686
## 2  1.2869783  1.0696458  0.9392902  0.9642246  0.7054150  0.6501914  1.5326270
## 3  1.1304631  1.1705713  0.9006211  0.0000000        NaN         NA  0.2760961
## 4  1.0533705  1.0674223  0.9853558  0.8321063  0.6230031  1.5256751  0.9278676
## 5  1.0640761  1.0902616  1.0062953  0.8768886  0.9321578  1.3175451  0.7453968
## 6  0.8922156  0.8299776  1.1752022  0.0000000         NA  1.2782369  0.7262931
## 7  0.9928058  0.9956522  0.9839884  0.9615385  0.9938462  0.9969040  0.9937888
##   2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
## 1  1.0170170  1.0728346  0.9532110  0.9595765  0.9448345  1.0467091  1.0131846
## 2  1.1608691  1.1152283  0.8941346  0.9644516  0.8147238  0.6124985  1.3675399
## 3  1.3991141  1.2600918  1.0283920  0.0000000        NaN         NA  0.3533324
## 4  1.0754846  1.0179344  1.0338848  0.7296899  0.6397667  1.4757459  1.0606490
## 5  1.0842523  1.1025224  0.9731857  0.8415066  0.9678582  1.1707014  0.7368770
## 6  0.7566766  1.0431373  1.8947368  0.0000000         NA  0.9377289  1.8203125
## 7  1.0078125  0.9193798  1.0118044  0.8883333  0.7298311  1.0205656  0.8639798
##   2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
## 1  1.0210210  1.0941176  0.8306452  0.9029126  0.9856631  1.0739394  1.1817156
## 2  1.1303471  1.0298165  0.9356595  0.8796615  0.8641010  0.6009047  1.2880718
## 3  1.2989273  0.8882830  1.1133135  0.0000000        NaN         NA  0.3605461
## 4  1.0203670  1.0825701  1.0812611  0.7229291  0.5734447  1.4812690  1.1148722
## 5  1.1383342  1.0625790  0.8839976  0.8689435  0.9184898  1.1707420  0.7328052
## 6  0.5429185  0.9249012  0.0000000        NaN         NA  1.3160173  0.9802632
## 7  1.1428571  0.7627551  0.8160535  1.0655738  0.9846154  1.1796875  1.2251656
##   2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
## 1  1.1575931  1.0973597  0.8541353  1.0052817  0.9343257  1.0843486  1.2005186
## 2  1.2362329  1.0429091  0.9091702  0.9039310  0.8470513  0.6233408  1.2936922
## 3  1.5245737  0.9429276  0.8358626  0.0000000        NaN         NA  0.4842324
## 4  1.0248385  1.0202705  0.9531878  0.7300453  0.6459776  1.6448633  1.1019996
## 5  1.0965602  1.1362312  0.8830605  0.8115230  0.9045129  0.8536660  1.2587313
## 6  1.1342282  0.8846154  0.8361204  0.0000000         NA  0.6820388  1.2099644
## 7  0.7972973  1.0610169  1.1725240  0.8991826  0.8575758  1.0565371  1.1672241
##   2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
## 1  0.9503240  0.8962121  1.0270499  0.9102881  0.8200723  1.3726571  1.0048193
## 2  1.2220497  1.0528590  0.9020034  0.8959058  0.8805257  0.6166893  1.3657866
## 3  0.9343161  1.0565209  1.0362949  0.0000000        NaN         NA  0.4508837
## 4  0.9998369  1.0944089  0.8283649  0.5914980  0.5465054  0.8456721  3.4518677
## 5  0.7771801  1.0724954  1.0805027  0.7393901  0.9332483  1.1763098  0.6626646
## 6  0.6441176  1.4657534  1.0685358  0.0000000         NA  1.6256831  0.4974790
## 7  0.8767908  0.9346405  0.7062937  0.7722772  1.2115385  1.2063492  1.0087719
##   2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
## 1  1.0143885  0.9936958  0.9524187  0.9525396  0.8601399  1.1798780  1.0861326
## 2  1.1663311  0.6778315  1.3020886  0.9522692  0.9346754  0.5582418  1.4913386
## 3  1.1358250  1.0533708  0.9464762  0.0000000        NaN         NA  0.3672186
## 4  0.7960147  1.1000719  0.9694594  0.8673630  0.3834002  2.3907464  0.9143241
## 5  1.2109877  1.0468147  0.9303827  0.8191278  0.8233515  1.1403380  0.8485825
## 6  0.8479730  0.9721116  1.5819672  0.0000000         NA  1.2317073  0.6014851
## 7  0.8434783  1.0206186  0.9242424  1.0491803  0.8958333  0.9941860  1.0643275
##   2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
## 1  1.0103093  0.0000000         NA  0.4376270  0.9442897  1.0904621  1.1442741
## 2  1.1605069  0.9454049  0.9104909  0.9101480  0.8072009  0.6525180  1.3814774
## 3  1.2634132  3.1633160  0.2957726  0.0000000        NaN         NA  0.4202277
## 4  1.4322905  0.8051346  1.6747722  0.3617055  0.5732611  2.2266590  0.9412631
## 5  1.0508732  1.0859827  0.9381238  0.8773050  0.8843977  1.0447898  0.7751531
## 6  0.9423868  1.0000000  1.6419214  0.0000000         NA  0.7575758  1.2044444
## 7  0.8681319  0.9050633  1.2937063  0.7945946  0.7959184  1.3418803  0.9299363
##   2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
## 1  0.9763593  1.0564972  0.9442322  0.9328479  0.9358196  1.1232623  1.2202970
## 2  1.1173184  0.9450000  0.8669690  1.0401046  0.7384744  0.5493757  1.7231405
## 3  1.1165501  1.0952505  1.0040505  0.0000000        NaN         NA  0.5604106
## 4  1.0927750  0.9767540  1.8350435  0.4196701  0.5418206  2.2043811  1.0012187
## 5  1.1715576  1.0732177  0.9066427  0.8079208  0.8198529  1.1270553  0.8885942
## 6  1.2287823  0.7747748  1.0852713  0.0000000         NA  0.9006211  0.7413793
## 7  1.1301370  0.7696970  1.4488189  1.0000000  0.5815217  1.2149533  1.4538462
##   2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
## 1  0.8471941  1.0015962  1.0454183  0.9916159  0.9362029  1.0821018  1.1889226
## 2  1.3669065  0.2333333  2.8233083  1.1158455  0.9307876  0.4961538  1.7493540
## 3  1.0745050  1.0382400  1.0925227  0.0000000        NaN         NA  0.6966303
## 4  1.1816479  1.1343106  1.7036675  0.3819501  0.4856683  3.0183466  0.5133651
## 5  1.0029851  1.0505952  0.8328612  1.0595238  0.7768860  1.5082645  0.9452055
## 6  1.0372093  0.8251121  2.5489130  0.0000000         NA  0.9266862  1.4303797
## 7  0.8148148  0.6818182  0.8285714  1.4367816  0.8160000  1.1568627  1.2118644
##   2021-06-30 2021-07-01 2021-07-02 2021-07-03 2021-07-04 2021-07-05 2021-07-06
## 1  0.9483089  1.0323015  0.8722295  0.8579970  1.0217770  1.0630861  1.0240577
## 2  1.1462334  1.1327320  0.9032992  1.1700252  0.8675996  0.5955335  1.8895833
## 3  1.3012269  1.3379213  1.0176590  0.0000000        NaN         NA  0.4335572
## 4  2.1654779  0.9885375  1.9786752  0.1734474  0.7957282  2.1261591  2.6111557
## 5  0.7260870  0.7465070  1.7593583  0.5699088  0.9280000  1.7500000  0.7011494
## 6  0.5221239  1.8262712  0.9698376  0.0000000         NA  1.0876217  1.3682864
## 7  0.8251748  1.2372881  0.9109589  0.7744361  1.1747573  0.7685950  1.0000000
##   2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12 2021-07-13
## 1  0.9451840  1.0414250  0.9013524  1.0388350  0.9447749  1.1187050  1.0409968
## 2  1.1135612  1.3772277  0.9964055  1.0057720  0.9978479  0.6376707  1.7249154
## 3  1.2296810  0.9961459  1.2634405  0.0000000        NaN         NA  1.2955322
## 4  0.8829942  1.0345463  2.2892268  0.2044475  1.1846407  2.6049640  0.8767698
## 5  1.2341920  1.0891841  0.9494774  0.6770642  0.8292683  1.4836601  0.7797357
## 6  1.1663551  1.1089744  1.2355491  0.0000000         NA  0.9386826  0.9854423
## 7  1.6989247  0.7151899  1.2743363  0.6736111  0.8865979  1.6976744  0.9726027
##   2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19 2021-07-20
## 1  0.9467181  0.9502447  1.1141631  0.8459168  0.9608379  1.2255924  0.9845321
## 2  1.4071895  1.1486298  1.1706429  1.0770294  1.0025657  0.6621881  1.7173913
## 3  0.6003185  1.0491853  1.1217856  0.0000000        NaN         NA  0.4427533
## 4  1.1865263  1.0171006  2.4347470  0.1744601  1.5256097  2.4185696  0.7911569
## 5  0.9378531  1.2108434  0.8781095  1.0453258  0.9214092  1.6294118  0.5703971
## 6  0.9863636  0.9811828  1.1217221  0.0000000         NA  0.8433883  0.5200000
## 7  0.9436620  0.9776119  1.0152672  0.8721805  1.0172414  1.0508475  0.8709677
##   2021-07-21 2021-07-22 2021-07-23 2021-07-24 2021-07-25 2021-07-26 2021-07-27
## 1  0.8970935  1.0175131  1.0731497  1.0072173  0.9506369  1.0485762  1.1014377
## 2  1.1966245  1.1885284  1.0170095  0.9992221  0.9229272  0.6568958  1.4497592
## 3  1.1209778  0.9656063  1.0553919  0.0000000        NaN         NA  0.4283813
## 4  1.2084910  1.1848552  2.1415626  0.2276698  0.9853792  2.9834275  0.8018434
## 5  1.5727848  0.7706237  1.5822454  0.6551155  0.8312343  2.8242424  0.5901288
## 6  0.0000000         NA  2.9658470  0.0000000         NA  0.9133940  0.9533030
## 7  1.1851852  1.5312500  0.5816327  1.0877193  1.0161290  1.4126984  0.8202247
##   2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02 2021-08-03
## 1  0.9673677  0.0000000        NaN         NA  0.2992822  0.0000000        NaN
## 2  1.2606289  1.0832601  1.0723204  0.9842734  0.8173299  0.5986842  1.5199372
## 3  1.0284102  0.9830565  0.9274608  0.0000000        NaN         NA  0.3633780
## 4  1.1284773  0.9957205  2.6167905  0.2199210  1.0230488  2.6214521  0.8604804
## 5  1.3872727  1.1808650  0.9977802  0.5906563  0.8229755  0.8100686  5.5847458
## 6  0.9441458  0.8636507  1.0673993  0.0000000         NA  0.6691347  0.7396266
## 7  1.5410959  0.7022222  1.0886076  0.9418605  0.9320988  0.9933775  1.1333333
##   2021-08-04 2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10
## 1        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
## 2  1.3612890  1.0962064  0.9134828  1.0451584   0.831231  0.7326007  1.3390476
## 3  1.0761057  0.9777361  1.0081358  0.0000000        NaN         NA  0.3955800
## 4  1.0645588  1.0594783  2.3035587  0.2405665   0.540934  5.0591743  0.7083809
## 5  0.4850784  1.5057351  1.0505540  0.4957152   1.061170  3.5639098  0.4708158
## 6  1.1612903  0.8039452  0.9409114  0.0000000         NA  0.6628023  1.0664668
## 7  0.9647059  1.1036585  1.2044199  0.9128440   1.090452  0.9677419  1.0476190
##   2021-08-11 2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17
## 1        NaN        NaN         NA  0.0000000        NaN         NA  0.0000000
## 2   1.230619  1.0499928  1.0180267  0.9706677  0.7883303  0.6481187  1.4358136
## 3   1.085651  1.0227339  0.8993107  0.0000000        NaN         NA  0.5572572
## 4   1.305305  0.9475518  1.5588112  0.3020558  0.6452204  4.6281140  0.7301508
## 5   1.395818  1.1428571  1.1268727  0.4732032  0.8560140  4.0943590  0.4541583
## 6   1.158388  0.9247573  1.0323710  0.0000000         NA  0.9494878  0.8467262
## 7   1.031818  0.8678414  1.2385787  0.7950820  0.9639175  1.3903743  1.0346154
##   2021-08-18 2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24
## 1        NaN         NA  0.0000000         NA  0.0000000         NA  0.0000000
## 2  1.3589598  1.0113144  0.9972376  1.0340720  0.7931958  0.7034786  1.4589534
## 3  0.8339844  1.0409000  1.0004018  0.0000000        NaN         NA  0.4214402
## 4  1.1193718  0.9267862  1.5546990  0.3437096  0.4866834  5.5550352  0.6952712
## 5  1.3276338  1.1350229  1.0948023  0.4811100  0.9763725  3.5117438  0.4685853
## 6  1.0215290  0.8382796  1.2129297  0.0000000         NA  0.9436702  1.1655385
## 7  0.8104089  1.4036697  0.7222222  0.8597285  1.0789474  1.4097561  0.7508651
##   2021-08-25 2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31
## 1        NaN        NaN         NA  0.0000000        NaN        NaN        NaN
## 2  1.2403751  0.9570235  1.0842689  0.8766458  0.8681831  0.7143097  1.2913238
## 3  1.0703932  0.6599573  1.3336613  0.0000000        NaN         NA  0.5014526
## 4  1.1119646  1.0883487  1.2707299  0.3056174  0.6257890  5.2518803  0.7525552
## 5  1.4411765  1.0105042  1.1155331  0.4776358  0.8790412  4.1223843  0.4399323
## 6  0.8880676  1.0237812  0.9367015  0.0000000         NA  0.8377434  0.8945173
## 7  0.9953917  1.0787037  0.9098712  0.8632075  0.9781421  1.1452514  0.8439024
##   2021-09-01 2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07
## 1        NaN        NaN        NaN        NaN         NA  0.0000000         NA
## 2  1.1828114  1.0397167  0.9962985  0.9148462  0.8632229  0.6298457  1.4087242
## 3  0.8778164  1.4023174  0.6600774  0.0000000        NaN         NA  0.5907876
## 4  1.0909091  0.8942644  1.3495203  0.3249712  0.6771617  1.5420337  3.0081093
## 5  1.3500000  1.0435120  1.0330107  0.4735704  1.0091324  0.7677225  5.8506876
## 6  1.1092605  0.8882883  0.9384719  0.0000000         NA  0.7204807  0.9224075
## 7  0.9595376  1.0662651  1.0903955  0.8134715  0.8280255  1.4538462  0.9100529
##   2021-09-08 2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14
## 1  0.0000000         NA  0.4594595  0.0000000        NaN        NaN        NaN
## 2  1.2557794  0.9319372  1.0179413  0.9241588  0.8984781  0.5986278  1.4358883
## 3  1.0162808  0.8478106  0.9321856  0.0000000        NaN         NA  0.4178626
## 4  0.7344320  0.9297976  1.4041690  0.3362754  0.5750080  5.2372693  0.6064725
## 5  0.4081039  1.1505760  1.1039333  0.2656014  2.4284553  2.9450954  0.4529953
## 6  1.1784591  1.2021348  0.6198668  0.0000000         NA  0.8946639  0.8900077
## 7  0.9593023  0.8666667  0.8811189  1.1666667  0.9047619  0.9849624  0.9312977
##   2021-09-15 2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21
## 1        NaN         NA  0.1479290  0.0000000        NaN         NA  0.3432836
## 2   1.203791  1.0598840  0.8883675  1.0070423  0.8382867  0.6269552  1.4029106
## 3   1.141674  1.0945474  0.7906748  0.0000000        NaN         NA  0.4091516
## 4   1.236034  0.8899892  1.3419342  0.3385950  0.6054894  4.1522051  0.6529323
## 5   1.075282  1.0970828  1.0801957  0.4261520  0.9242144  4.2890000  0.4466076
## 6   1.247171  0.7348221  0.9192783  0.0000000         NA  0.9643176  0.9602220
## 7   1.303279  0.8364780  0.8796992  0.7008547  1.2317073  1.3762376  1.0287770
##   2021-09-22 2021-09-23 2021-09-24 2021-09-25 2021-09-26 2021-09-27 2021-09-28
## 1  0.7826087  0.0000000        NaN         NA  0.2993197  1.3409091  0.8474576
## 2  1.1745702  1.0227101  0.9351098  0.9284960  0.8806479  0.5721200  1.6706148
## 3  1.1591837  1.0672535  0.9059716  0.0000000        NaN         NA  0.4544552
## 4  1.1669287  0.9339371  1.3839400  0.3340603  0.8858461  3.0579239  0.7054817
## 5  1.0107022  1.1887913  1.0004345  0.2740499  1.0388273  8.4004577  0.3167166
## 6  0.9489403  1.0213198  0.9304175  0.0000000         NA  0.9201213  0.7538462
## 7  0.9650350  0.7898551  0.9816514  1.0093458  0.6203704  1.3432836  1.0444444
##   2021-09-29 2021-09-30 2021-10-01 2021-10-02 2021-10-03 2021-10-04 2021-10-05
## 1  0.0000000         NA  0.0000000        NaN        NaN        NaN         NA
## 2  1.0844024  1.2745953  0.8312164  0.9723773  0.8966455  0.5433097  1.5279156
## 3  1.2056769  0.8692503  0.8487500  0.0000000        NaN         NA  0.4216811
## 4  1.0730790  0.9205365  1.3886963  0.3039625  0.9276260  3.3366521  0.6556433
## 5  1.2241972  0.8686183  1.4416285  0.3688049  0.7778905  5.2653194  0.3301969
## 6  1.0102041  1.1760462  1.2355828  0.0000000         NA  0.9612299  0.9485396
## 7  0.8085106  1.1184211  1.0823529  1.0760870  0.7979798  0.9746835  0.7532468
##   2021-10-06 2021-10-07 2021-10-08 2021-10-09 2021-10-10 2021-10-11 2021-10-12
## 1  0.2017937  1.0444444  1.0212766  0.7291667  1.6857143  0.9830508  0.9482759
## 2  1.3122209  0.9074876  1.0286396  0.9105071  0.8292683  0.6654960  1.6398417
## 3  1.2787340  0.7846287  1.2778085  0.0000000        NaN         NA  0.0000000
## 4  1.1846123  0.8980233  1.2983331  0.2861850  1.0320475  2.5057581  1.1400302
## 5  1.3967004  1.0947651  1.0134870  0.4536656  0.9296000  0.8502582  4.8144399
## 6  1.0146628  1.3222543  0.6601093  0.0000000         NA  0.8786207  0.8649922
## 7  1.7068966  0.7878788  0.9230769  0.8611111  1.0483871  0.9076923  1.6271186
##   2021-10-13 2021-10-14 2021-10-15 2021-10-16 2021-10-17 2021-10-18 2021-10-19
## 1  1.0363636  0.6315789   1.333333  0.9375000  0.9111111  0.9268293  1.2894737
## 2  1.1138375  0.9628025   1.023631  1.0927080  0.8165661  0.6546201  1.6907152
## 3         NA  0.7005076   1.163561  0.0000000        NaN         NA  0.4205254
## 4  0.9793933  0.7990977   1.262840  0.3571695  0.7920416  3.2188858  0.8126062
## 5  0.3656622  1.2188578   1.077358  0.4900759  0.8189398  3.9905455  0.4049572
## 6  1.3139746  0.7720994   1.007156  0.0000000         NA  0.8179487  0.9341693
## 7  0.8645833  0.9518072   0.721519  1.2105263  0.8985507  1.0806452  1.2388060
##   2021-10-20 2021-10-21 2021-10-22 2021-10-23 2021-10-24 2021-10-25 2021-10-26
## 1  0.9591837  0.9787234  1.1086957  0.8431373  1.0930233  1.0851064  1.2745098
## 2  1.3725417  1.0248716  1.0232129  1.0067028  0.9533931  0.6809025  1.5988166
## 3  1.3382742  0.7440665  1.3588517  0.0000000        NaN         NA  0.4294314
## 4  1.1311645  0.8474416  1.2370005  0.3302342  0.5832207  5.9861538  0.6229018
## 5  1.1989199  1.0777027  0.9285963  0.6804201  0.8908490  2.0891089  0.5183649
## 6  0.8926174  0.8195489  1.5802752  0.0000000         NA  0.7906040  1.1052632
## 7  1.0361446  1.2325581  0.8301887  0.8750000  1.0649351  1.4878049  0.8360656
##   2021-10-27 2021-10-28 2021-10-29 2021-10-30 2021-10-31 2021-11-01 2021-11-02
## 1  0.8461538  0.9272727  1.0980392  0.7321429  1.1219512  1.0652174  0.9183673
## 2  1.1337281  1.0583243  1.0964425  0.9146662  0.9278245  0.6227624  1.0046132
## 3  1.3146417  0.8736177  1.0221519  0.0000000        NaN        NaN         NA
## 4  1.4790348  0.7399476  1.2557694  0.3559301  0.9457392  3.4812293  0.6365219
## 5  1.4348571  1.0525687  0.9795687  0.6709154  0.9090386  1.9303357  0.6023622
## 6  0.9585253  0.9551282  0.9546980  0.0000000         NA  0.9165447  0.7971246
## 7  1.0098039  1.0194175  0.9619048  0.9108911  1.1304348  1.2211538  1.0551181
##   2021-11-03 2021-11-04 2021-11-05 2021-11-06 2021-11-07 2021-11-08 2021-11-09
## 1  1.0888889  0.9183673  0.9555556  0.9767442  0.9523810  1.0750000  1.1395349
## 2  1.8325680  1.1376253  1.1455439  0.9998521  0.8612426  0.7810031  1.3439631
## 3  0.3929553  1.4390031  0.9398359  0.0000000        NaN         NA  0.0000000
## 4  1.2028810  0.9575942  1.1830207  0.3579127  1.0490998  3.0032505  0.7333798
## 5  1.2249455  1.1511783  0.9787563  0.5564325  0.9865248  3.3055356  0.5097869
## 6  1.1142285  1.1654676  0.9243827  0.0000000         NA  1.2268519  0.7660377
## 7  1.0298507  0.7681159  1.1226415  1.0420168  0.8306452  1.4466019  0.9261745
##   2021-11-10 2021-11-11 2021-11-12 2021-11-13 2021-11-14 2021-11-15 2021-11-16
## 1  0.7755102  1.1315789  1.0465116  0.9777778  0.6818182  1.2666667  0.9736842
## 2  1.3145148  1.0826590  0.9798781  1.0019948  0.8859351  0.6872439  1.5031737
## 3         NA  0.6633648  1.0156323  0.0000000        NaN         NA  0.4175342
## 4  1.1969458  0.5920713  2.5556889  0.3003736  0.7175043  4.3194489  0.6457607
## 5  1.0994027  0.6041909  2.4489403  0.3978495  0.9723138  2.7661017  0.4674020
## 6  0.9802956  0.9514238  1.0440141  0.0000000         NA  0.9382304  1.0551601
## 7  1.2463768  0.8604651  0.8310811  1.1138211  0.9051095  1.1532258  1.0209790
##   2021-11-17 2021-11-18 2021-11-19 2021-11-20 2021-11-21 2021-11-22 2021-11-23
## 1   1.135135  0.9047619  0.9210526  0.8857143  1.1612903  1.0833333  0.9743590
## 2   1.300960  1.0470149  0.9914514  1.0934243  0.8411612  0.6596271  1.5686397
## 3   1.629675  0.9472026  1.0508314  0.0000000        NaN         NA  0.4268976
## 4   1.294366  0.9777780  1.1518466  0.3529514  0.8758260  3.7126434  0.6267139
## 5   1.278448  1.1230517  1.0504018  0.6477747  0.8588298  2.6325000  0.5263533
## 6   0.688027  1.3504902  0.7785844  0.0000000         NA  0.9797639  1.0240964
## 7   1.020548  0.9731544  1.0137931  1.0204082  0.7866667  1.1949153  1.0425532
##   2021-11-24 2021-11-25 2021-11-26 2021-11-27 2021-11-28 2021-11-29 2021-11-30
## 1  0.8947368  0.8235294  0.8571429  1.2083333  0.8275862  1.0416667  1.2800000
## 2  1.2395460  1.1048996  0.9945478  0.9406476  1.0045070  0.6166937  1.6002258
## 3  1.2582264  1.0857277  1.0706416  0.0000000        NaN         NA  0.4478635
## 4  1.2218583  0.3501569  1.3492945  0.4932820  1.6348632  4.4172256  0.6250067
## 5  1.1849346  1.5866007  0.7324856  0.7150344  0.8873110  1.9901910  0.6114137
## 6  0.7865546  1.5641026  0.8196721  0.0000000         NA  0.8137755  0.6692790
## 7  0.9727891  1.0839161  0.9741935  1.0264901  0.9870968  1.0326797  0.9936709
##   2021-12-01 2021-12-02 2021-12-03 2021-12-04 2021-12-05 2021-12-06 2021-12-07
## 1   1.062500  0.7058824  1.5833333  0.7631579  1.2068966  1.2285714  0.9767442
## 2   1.181547  1.1152392  1.0121951  0.9771966  0.9027485  0.6325117  1.6580999
## 3   1.026801  1.3762339  0.9474483  0.0000000        NaN        NaN         NA
## 4   1.167371  1.0164213  1.1394490  0.4134424  0.8277497  3.3065601  0.6308429
## 5   1.327959  1.0332268  1.0782313  0.7771724  0.9697417  1.7233638  0.6617355
## 6   1.344262  0.9216028  1.0226843  0.0000000         NA  0.6755994  0.8371608
## 7   1.019108  0.9437500  1.0198675  1.0324675  0.9559748  1.0789474  0.9634146
##   2021-12-08 2021-12-09 2021-12-10   2021-12-11 2021-12-12 2021-12-13
## 1   1.095238  0.9782609  1.0666667  1.104166667  0.9622642  1.2549020
## 2   1.140008  0.6974256  1.6371045  1.026598341  0.9133349  0.6612534
## 3   0.000000         NA  0.6472437 -0.004855221  0.0000000         NA
## 4   1.321823  0.8181800  1.3123137  0.347575015  0.9188807  3.6450268
## 5   1.175175  1.2106758  0.6857411  1.981190150  0.6022786  1.7004872
## 6   1.269327  0.9724951  1.1656566  0.000000000         NA  1.0854962
## 7   1.031646  0.9754601  1.0251572  0.969325153  1.0506329  1.0180723
##   2021-12-14 2021-12-15 2021-12-16 2021-12-17 2021-12-18 2021-12-19 2021-12-20
## 1  1.0156250   1.353846  0.9659091  0.9411765  1.4500000  0.8965517  1.4038462
## 2  1.6266530   1.122187  1.1256576  1.0961922  0.9804997  0.8662009  0.6667901
## 3  0.5247982   1.038414  1.0648489  1.1542907  0.0000000        NaN         NA
## 4  0.6137662   1.250488  0.9864750  1.3614414  0.4036663  1.1294786  2.7588431
## 5  0.7406034   1.332499  1.2174210  1.2807239  0.8272538  1.1581038  1.6410931
## 6  0.7201125   0.953125  1.3278689  0.8626543  0.0000000         NA  0.8154270
## 7  0.9881657   0.988024  1.0242424  0.9704142  1.0914634  0.9497207  1.0411765
##   2021-12-21 2021-12-22 2021-12-23 2021-12-24 2021-12-25 2021-12-26 2021-12-27
## 1  1.5205479   1.135135  1.1388889  1.1567944  0.9789157  1.1969231   1.347044
## 2  1.8995989   1.180067  1.2273578  1.1346866  1.0829611  0.4541588   1.237802
## 3  0.6251004   1.205086  1.2143702  0.0000000        NaN        NaN         NA
## 4  0.7369343   1.351049  1.1201057  0.9022513  0.3394300  2.1350973   2.878076
## 5  0.8201770   1.270982  1.3811656  1.1637551  0.8498482  0.9019474   1.147282
## 6  0.9172297   1.033149  0.9982175  1.1750000  0.0000000         NA   1.198639
## 7  1.0338983   1.010929  1.0108108  1.3262032  1.1250000  1.0609319   1.158784
##   2021-12-28 2021-12-29 2021-12-30 2021-12-31 2022-01-01 2022-01-02 2022-01-03
## 1  1.1488550   1.235880  1.0107527  1.0890957  1.0329670  1.2104019  1.7050781
## 2  2.5422903   1.251801  1.2957068  1.1358661  0.9792104  0.4328099  1.1128122
## 3  0.4644090   1.010926  1.6046844  0.0000000        NaN        NaN         NA
## 4  0.7008156   1.399919  1.1801500  0.8526602  0.3605551  1.5342649  3.8705767
## 5  1.1261998   2.052523  0.8114836  1.0235941  0.9124289  0.9126609  1.1619991
## 6  0.6799092   1.110184  0.9624060  0.8609375  0.0000000         NA  0.6921397
## 7  1.0699708   1.207084  1.2234763  1.3671587  1.1241565  1.1980792  1.1793587
##   2022-01-04 2022-01-05 2022-01-06 2022-01-07 2022-01-08 2022-01-09 2022-01-10
## 1  1.4805269  1.1779497  1.0403941  1.1284722  0.8581818  1.1277705  1.3809249
## 2  2.5110533  1.1068328  1.1604650  0.4935378  1.8240117  0.7879211  0.7543272
## 3  0.3159489  1.1647633  0.0000000         NA  0.0000000        NaN         NA
## 4  0.7488063  0.8004165  1.2636977  1.0506689  0.4509899  1.2323575  2.8746003
## 5  1.2175706  0.8016805  1.0865620  1.0680760  0.6942778  0.9443420  2.0179899
## 6  1.0268139  1.0721966  0.9212034  0.9206843  0.0000000         NA  0.9257340
## 7  1.4401020  1.3410029  1.2226133  1.1486146  1.0924185  1.0579295  1.0512334
##   2022-01-11 2022-01-12 2022-01-13 2022-01-14 2022-01-15 2022-01-16 2022-01-17
## 1  0.9736291  1.1526225  1.0255502  1.0234588  0.9383440  1.0371142  1.0051123
## 2  1.8782761  0.8897419  0.9407355  1.0882667  0.9605066  0.8161515  0.5295589
## 3  0.4615074  1.3274222  0.8885471  1.0210290  0.0000000        NaN         NA
## 4  0.5624097  1.1819366  0.9242353  1.0225500  0.4588515  1.1874708  1.4126732
## 5  0.5245453  1.0695672  0.9382869  1.3007000  0.4711951  0.9605263  2.4166575
## 6  0.9085821  1.2299795  1.0317195  1.2378641  0.0000000         NA  0.8489043
## 7  1.0750387  1.0088750  0.9954826  0.9847146  0.9718651  1.0034939  0.9942800
##   2022-01-18 2022-01-19 2022-01-20 2022-01-21 2022-01-22 2022-01-23 2022-01-24
## 1  1.0668483  1.0093649  0.9431511  0.8735468  0.9434889  0.9841580  1.0668137
## 2  2.7357142  0.8809546  0.9895455  0.9332965  0.9554687  0.8034906  0.5450743
## 3  0.2850118  1.6718287  0.9968722  0.8961428  0.0000000        NaN         NA
## 4  1.6825547  0.8971123  0.7120035  1.1574628  0.3483729  1.2424572  2.5354201
## 5  0.4742880  1.0800268  1.0415634  0.9769666  0.6312584  0.5150910  2.5579933
## 6  1.2024457  1.3254237  1.1346974  1.2772352  0.0000000         NA  1.0227970
## 7  0.9544772  0.9756289  0.8847703  0.9726776  0.9634831  0.9656625  0.9218383
##   2022-01-25 2022-01-26 2022-01-27 2022-01-28 2022-01-29 2022-01-30 2022-01-31
## 1  0.9386110  0.9966968  1.0468405  0.9442803  0.8746089  0.9376438  1.1477242
## 2  2.7203281  0.8090800  0.9128349  0.9250641  0.9520600  0.7575658  0.5535587
## 3  0.3761132  1.1625739  0.9800454  0.9085783  0.0000000        NaN         NA
## 4  0.5293387  1.5245697  0.6700876  1.2315927  0.2818371  1.1376847  2.7544305
## 5  1.4938549  0.2487747  4.2265691  0.5889325  0.5578358  0.8656126  2.5473598
## 6  1.3129018  0.7329416  1.4062361  1.0516313  0.0000000         NA  0.7778363
## 7  0.9283115  0.8639749  0.8856624  0.8929303  0.8823867  1.0123537  0.9691715
##   2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 1  0.9168844  1.0598291  0.9413490  0.9228972  0.8475387  1.0819781  1.1493865
## 2  2.3130954  0.8951060  0.9466406  0.8932682  0.9236075  0.8471006  0.5269878
## 3  0.4275847  1.1072130  0.8625177  0.0000000        NaN        NaN         NA
## 4  0.7803387  0.7892118  0.7744060  1.4649487  0.2818041  0.7702810  4.2107216
## 5  0.4916579  1.4338600  0.9142652  1.0065607  0.5287991  0.8496785  2.4949543
## 6  0.9631971  1.0246132  0.8364219  1.0713895  0.0000000         NA  0.6676096
## 7  0.8190855  1.0072816  0.9502008  0.8427726  0.9057172  1.0099668  1.0120614
##   2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 1  0.8887110  0.9495495  0.9063884  0.8803210  0.6841062  1.2375435  1.0426030
## 2  2.4621172  0.7973230  0.9329733  0.8855962  0.9220931  0.8412755  0.5497373
## 3  0.2239074  1.4336657  0.8443005  0.9236453  0.0000000        NaN         NA
## 4  0.6572673  0.8470795  0.9110046  1.2227982  0.2630773  1.0285446  2.8815605
## 5  0.5702458  1.1976061  1.0108273  0.7016387  1.4078810  0.4078777  2.8445808
## 6  1.1744350  0.9837643  1.1531174  0.8865624  0.0000000         NA  0.7675439
## 7  0.8873239  0.9474969  1.0090206  0.8390805  0.9238965  1.0098847  0.9804241
##   2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 1  0.8899865  0.9046418  0.8750697  0.8769917  0.7245640  1.0160481  1.0384995
## 2  2.4681332  0.8429523  0.9697005  0.9249849  0.9436685  0.8327972  0.5801621
## 3  0.5003930  1.0793485  0.9219845  0.8948353  0.0000000        NaN         NA
## 4  0.7013282  1.0017209  0.8792396  1.4394928  0.2300389  0.6571999  3.6721081
## 5  0.4639348  1.4078857  0.8871224  1.0001379  0.5485872  0.7947236  0.8909263
## 6  0.8892857  1.1582329  0.8519417  0.4916565  0.0000000         NA  1.0065445
## 7  0.9101498  0.9104205  0.8975904  1.0111857  0.9601770  1.0184332  0.9411765
##   2022-02-22 2022-02-23 NA
## 1  0.7994297  0.7455410 NA
## 2  2.4561755  0.8175000 NA
## 3  0.4549797  1.5280494 NA
## 4  1.1174843  0.9371236 NA
## 5  3.0330021  1.0776881 NA
## 6  0.7867360  0.7564738 NA
## 7  0.9471154  0.9263959 NA

# retrieve time series data
TS.data <- covid19.data("ts-ALL")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:31:34 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

## Data retrieved on 2022-02-25 06:31:36 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

## Data retrieved on 2022-02-25 06:31:38 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 269

## --------------------------------------------------------------------------------

# static and interactive plot 
totals.plt(TS.data)

## Loading required package: plotly

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

totals.plt(TS.data, c("Libya","Egypt","Saudia Arabia","Kuwait"), with.totals=TRUE,one.plt.per.page=FALSE)

## Warning in if (toupper(geo.loc0) != "ALL") {: the condition has length > 1 and
## only the first element will be used

## Warning in checkGeoLoc(total.cases, geo.loc0): Unrecognized region: Saudia
## Arabia will skip it!

## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...

totals.plt(TS.data, c("Algeria","Morocco","Tunisia","Egypt"), with.totals=TRUE,one.plt.per.page=FALSE)

## Warning in if (toupper(geo.loc0) != "ALL") {: the condition has length > 1 and
## only the first element will be used

## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...

# retrieve aggregated data
data <- covid19.data("aggregated")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

# interactive map of aggregated cases -- with more spatial resolution
live.map(data)

## Warning: Ignoring 87 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning: Ignoring 87 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

# or
live.map()

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## ================================================================================

## A problem was detected when trying to retrieve the data for the package: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv

## The URL or file was not found! Please contact the developer about this!

## simpleWarning in file(file, "rt"): cannot open URL 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2022.csv': HTTP status was '404 Not Found'

## ================================================================================

## We will load the preserved data instead, please notice that this data is not the latest one but instead an 'image' from previous records.

## Data being read from *local* repo in the 'covid19.analytics' package

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from C:/Users/DR.dardery/Documents/R/win-library/4.1/covid19.analytics/extdata/latest.RDS

## Warning: Ignoring 87 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning: Ignoring 87 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

# interactive map of the time series data of the confirmed cases with less spatial resolution, ie. aggregated by country
live.map(covid19.data("ts-confirmed"))

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:32:06 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

## Warning: Ignoring 2 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning: Ignoring 2 observations

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

data <- covid19.data("ts-confirmed")

## Data being read from JHU/CCSE repository

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

## Data retrieved on 2022-02-25 06:32:15 || Range of dates on data: 2020-01-22--2022-02-23 | Nbr of records: 284

## --------------------------------------------------------------------------------

# run a SIR model for a given geographical location
generate.SIR.model(data,"Hubei", t0=1,t1=15)

## ################################################################################

## This is an experimental feature, being currently under active development!

## Please check the development version of the package for the latest updates on it

## ################################################################################ 
## Processing...  HUBEI 
##  [1]   444   444   549   761  1058  1423  3554  3554  4903  5806  7153 11177
## [13] 13522 16678 19665
## ------------------------  Parameters used to create model ------------------------ 
##      Region: HUBEI 
##      Time interval to consider: t0=1 - t1=15 ; tfinal=90 
##          t0: 2020-01-23 -- t1: 2020-02-06 
##      Number of days considered for initial guess: 15 
##      Fatality rate: 0.02 
##      Population of the region: 1.4e+09 
## --------------------------------------------------------------------------------

## Loading required package: deSolve

## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
##      beta     gamma 
## 0.6384639 0.3615361 
##   R0 = 1.76597526188731 
##   Max nbr of infected: 156344176.33  ( 11.17 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 3126883.53 
##   Max reached at day : 53 ==>  2020-03-16 
## ================================================================================

## Warning in xy.coords(x, y, xlabel, ylabel, log = log, recycle = TRUE): 1 y value
## <= 0 omitted from logarithmic plot

## $Infected
##  [1]   444   444   549   761  1058  1423  3554  3554  4903  5806  7153 11177
## [13] 13522 16678 19665
## 
## $model
##    time          S            I            R
## 1     1 1399999556 4.440000e+02 0.000000e+00
## 2     2 1399999229 5.856674e+02 1.849505e+02
## 3     3 1399998799 7.725366e+02 4.289133e+02
## 4     4 1399998230 1.019030e+03 7.507177e+02
## 5     5 1399997481 1.344172e+03 1.175200e+03
## 6     6 1399996492 1.773056e+03 1.735122e+03
## 7     7 1399995188 2.338783e+03 2.473697e+03
## 8     8 1399993467 3.085013e+03 3.447928e+03
## 9     9 1399991198 4.069338e+03 4.733006e+03
## 10   10 1399988204 5.367722e+03 6.428108e+03
## 11   11 1399984256 7.080364e+03 8.664056e+03
## 12   12 1399979047 9.339428e+03 1.161341e+04
## 13   13 1399972177 1.231924e+04 1.550378e+04
## 14   14 1399963115 1.624971e+04 2.063539e+04
## 15   15 1399951162 2.143411e+04 2.740423e+04
## 16   16 1399935395 2.827240e+04 3.633262e+04
## 17   17 1399914599 3.729204e+04 4.810945e+04
## 18   18 1399887168 4.918865e+04 6.364331e+04
## 19   19 1399850988 6.487949e+04 8.413251e+04
## 20   20 1399803269 8.557398e+04 1.111574e+05
## 21   21 1399740332 1.128665e+05 1.468019e+05
## 22   22 1399657328 1.488587e+05 1.938138e+05
## 23   23 1399547864 1.963198e+05 2.558162e+05
## 24   24 1399403517 2.588983e+05 3.375846e+05
## 25   25 1399213188 3.413983e+05 4.454132e+05
## 26   26 1398962262 4.501426e+05 5.875953e+05
## 27   27 1398631500 5.934466e+05 7.750536e+05
## 28   28 1398195596 7.822358e+05 1.022168e+06
## 29   29 1397621294 1.030847e+06 1.347859e+06
## 30   30 1396864941 1.358063e+06 1.776996e+06
## 31   31 1395869326 1.788435e+06 2.342239e+06
## 32   32 1394559624 2.353962e+06 3.086414e+06
## 33   33 1392838241 3.096189e+06 4.065571e+06
## 34   34 1390578343 4.068773e+06 5.352884e+06
## 35   35 1387615890 5.340542e+06 7.043568e+06
## 36   36 1383740046 6.998967e+06 9.260987e+06
## 37   37 1378682122 9.153829e+06 1.216405e+07
## 38   38 1372103515 1.194058e+07 1.595590e+07
## 39   39 1363583873 1.552246e+07 2.089366e+07
## 40   40 1352611807 2.008977e+07 2.729843e+07
## 41   41 1338582022 2.585378e+07 3.556420e+07
## 42   42 1320804880 3.303203e+07 4.616309e+07
## 43   43 1298536002 4.182090e+07 5.964309e+07
## 44   44 1271034744 5.235216e+07 7.661309e+07
## 45   45 1237657551 6.463330e+07 9.770915e+07
## 46   46 1197984811 7.847806e+07 1.235371e+08
## 47   47 1151965271 9.344396e+07 1.545908e+08
## 48   48 1100044035 1.088036e+08 1.911524e+08
## 49   49 1043226901 1.235792e+08 2.331939e+08
## 50   50  983038577 1.366571e+08 2.803043e+08
## 51   51  921361541 1.469663e+08 3.316721e+08
## 52   52  860188039 1.536758e+08 3.861361e+08
## 53   53  801353923 1.563442e+08 4.423019e+08
## 54   54  746328839 1.549751e+08 4.986960e+08
## 55   55  696107910 1.499711e+08 5.539210e+08
## 56   56  651207460 1.420129e+08 6.067796e+08
## 57   57  611736122 1.319149e+08 6.563490e+08
## 58   58  577501208 1.204941e+08 7.020047e+08
## 59   59  548117296 1.084789e+08 7.434038e+08
## 60   60  523097613 9.645958e+07 7.804428e+08
## 61   61  501921095 8.487493e+07 8.132040e+08
## 62   62  484076488 7.402144e+07 8.419021e+08
## 63   63  469088002 6.407545e+07 8.668365e+08
## 64   64  456527797 5.511940e+07 8.883528e+08
## 65   65  446019988 4.716705e+07 9.068130e+08
## 66   66  437239591 4.018535e+07 9.225751e+08
## 67   67  429908740 3.411188e+07 9.359794e+08
## 68   68  423791696 2.886792e+07 9.473404e+08
## 69   69  418689550 2.436786e+07 9.569426e+08
## 70   70  414435098 2.052556e+07 9.650393e+08
## 71   71  410888182 1.725847e+07 9.718533e+08
## 72   72  407931519 1.448997e+07 9.775785e+08
## 73   73  405467099 1.215058e+07 9.823823e+08
## 74   74  403413091 1.017842e+07 9.864085e+08
## 75   75  401701212 8.519066e+06 9.897797e+08
## 76   76  400274509 7.125142e+06 9.926003e+08
## 77   77  399085495 5.955753e+06 9.949588e+08
## 78   78  398094580 4.975819e+06 9.969296e+08
## 79   79  397268763 4.155402e+06 9.985758e+08
## 80   80  396580538 3.469061e+06 9.999504e+08
## 81   81  396006982 2.895252e+06 1.001098e+09
## 82   82  395528989 2.415777e+06 1.002055e+09
## 83   83  395130636 2.015305e+06 1.002854e+09
## 84   84  394798655 1.680941e+06 1.003520e+09
## 85   85  394521985 1.401858e+06 1.004076e+09
## 86   86  394291412 1.168976e+06 1.004540e+09
## 87   87  394099255 9.746878e+05 1.004926e+09
## 88   88  393939113 8.126258e+05 1.005248e+09
## 89   89  393805652 6.774646e+05 1.005517e+09
## 90   90  393694427 5.647529e+05 1.005741e+09
## 
## $params
## $params$beta
##      beta 
## 0.6384639 
## 
## $params$gamma
##     gamma 
## 0.3615361 
## 
## $params$R0
##       R0 
## 1.765975

generate.SIR.model(data,"Libya",tot.population=6800000)

## ################################################################################

## This is an experimental feature, being currently under active development!
## Please check the development version of the package for the latest updates on it

## ################################################################################ 
## Processing...  LIBYA 
##   [1]      0      0      0      0      0      0      0      0      0      0
##  [11]      0      0      0      0      0      0      0      0      0      0
##  [21]      0      0      0      0      0      0      0      0      0      0
##  [31]      0      0      0      0      0      0      0      0      0      0
##  [41]      0      0      0      0      0      0      0      0      0      0
##  [51]      0      0      0      0      0      0      0      0      0      0
##  [61]      0      0      1      1      1      1      3      8      8     10
##  [71]     10     11     11     18     18     19     20     21     24     24
##  [81]     24     25     26     35     48     49     49     49     51     51
##  [91]     51     59     60     61     61     61     61     61     61     61
## [101]     63     63     63     63     63     64     64     64     64     64
## [111]     64     64     64     64     64     65     65     65     68     69
## [121]     71     72     75     75     75     77     99    105    118    130
## [131]    156    168    182    196    209    239    256    256    332    359
## [141]    378    393    409    418    454    467    484    500    510    520
## [151]    544    571    595    639    670    698    713    727    762    802
## [161]    824    874    891    918    989   1046   1117   1182   1268   1342
## [171]   1342   1389   1433   1512   1563   1589   1652   1704   1791   1866
## [181]   1980   2088   2176   2314   2424   2547   2669   2827   3017   3222
## [191]   3438   3621   3691   3837   4063   4224   4475   4879   5079   5232
## [201]   5451   5929   6302   6611   7050   7327   7738   8172   8579   9068
## [211]   9463   9707  10121  10437  10437  11009  11281  11834  12274  12629
## [221]  12958  13423  13966  14624  15156  15773  16445  17094  17749  18834
## [231]  19583  20462  20939  21908  22348  22781  23515  24144  24936  25822
## [241]  26438  27234  27949  28796  29446  30097  30632  31290  31828  32364
## [251]  33213  34014  34525  35208  35717  36087  36809  37437  38468  39513
## [261]  40292  41368  41686  42712  43821  44985  45821  46676  47845  47845
## [271]  48790  49949  50906  51625  52620  53384  54374  56013  57223  57975
## [281]  58874  59656  60628  61095  62045  62907  63688  64587  65440  66444
## [291]  67039  68117  69040  70010  70885  71804  72628  72628  73602  74324
## [301]  74936  75465  76006  76808  76808  77823  78473  79180  79797  80407
## [311]  81273  81273  82430  82809  83417  84087  84849  85529  85529  86580
## [321]  87097  87986  88522  89183  89880  89880  90779  91357  92017  92577
## [331]  93283  93772  93772  94560  95200  95706  96346  97192  97653  97653
## [341]  98381  98913  99350  99935 100277 100744 100744 101414 101975 102456
## [351] 102880 103515 104002 104002 104745 105378 106030 106670 107434 108017
## [361] 108017 109088 109869 110465 111124 111746 112540 112540 113688 114429
## [371] 115299 116064 116779 117650 117650 118631 119402 120434 121243 122013
## [381] 122894 122894 124026 124882 125561 126028 126361 126881 126881 127354
## [391] 127705 128036 128348 128740 129325 129325 129797 130212 130701 131262
## [401] 131833 132458 132458 133338 134127 134967 135585 136587 137482 137482
## [411] 138640 139658 140688 141598 142671 143643 143643 144993 146080 147121
## [421] 148175 149207 150341 150341 151605 152510 153411 154320 155232 156116
## [431] 156116 156849 157545 158251 158957 159980 161088 161088 162294 163442
## [441] 164318 165287 166156 166888 166888 167825 168676 169504 170045 170558
## [451] 171131 171131 171880 172464 173089 173683 174216 174752 174752 175286
## [461] 175753 176254 176701 177072 177508 177508 177871 178335 178672 178927
## [471] 179193 179697 179697 179970 180226 180692 180945 181179 181179 181179
## [481] 181410 181714 182012 182350 182649 182899 182899 183311 183592 183932
## [491] 184151 184472 184815 184815 185181 185776 186072 186323 186567 186953
## [501] 186953 187281 187685 187928 188157 188386 188762 188762 189059 189284
## [511] 189555 189888 190146 190426 190426 190748 191038 191253 191476 191660
## [521] 192129 192129 192470 192786 193238 193474 193905 194323 194323 195042
## [531] 195824 196894 198142 199526 201236 201236 204090 206769 209409 212013
## [541] 214568 217434 217434 221495 224920 226701 226701 227433 229604 229604
## [551] 233449 236961 240309 243470 246200 249114 249114 253436 256328 258467
## [561] 260951 262948 264827 264827 267846 269847 271981 274453 276739 279099
## [571] 279099 281930 284618 286894 289219 291168 293532 293532 295254 296879
## [581] 298773 300455 302177 303790 303790 305793 307471 308972 310637 312116
## [591] 313504 313504 315418 316797 318069 319568 321370 322487 322487 323930
## [601] 325221 326370 327803 328856 329824 329824 330945 332026 333064 334049
## [611] 335055 335991 335991 336980 337890 338576 339269 340084 341091 341091
## [621] 341839 342558 343240 343932 344847 345451 345451 346176 346813 347364
## [631] 348088 348647 349210 349210 349990 350628 351224 351756 352192 352881
## [641] 352881 353626 354215 354866 355490 356086 356655 356655 357338 357964
## [651] 358463 359019 359667 360266 360266 360914 361709 362318 362915 363483
## [661] 364076 364076 364675 365237 365830 366238 366789 367218 367218 367811
## [671] 368392 368987 369455 370187 370787 370787 371571 372209 372636 373210
## [681] 373739 374280 374280 374989 375468 375869 376378 376873 377450 377450
## [691] 378105 378816 379328 379816 380464 381023 381023 381749 382341 382884
## [701] 383445 384005 384663 384663 385398 386279 386878 387543 388183 388734
## [711] 388734 389650 390284 390935 391633 392276 392868 392868 393447 393983
## [721] 394470 395069 395687 396452 396452 397319 398055 398940 400113 401444
## [731] 403144 403144 405425 407758 410821 413066 416223 419543 419543 425237
## [741] 429666 433932 438303 441959 445876 445876 450118 452950 456276 459548
## [751] 463321 466666 466666 470314 473114 475604 478488 480945 482153 482153
## [761] 484445 486752 488567 489940
## [1] 84
##  [1] 35 48 49 49 49 51 51 51 59 60 61 61 61 61 61 61 61 63 63 63 63 63 64 64 64
## [26] 64
## ------------------------  Parameters used to create model ------------------------ 
##      Region: LIBYA 
##      Time interval to consider: t0=84 - t1= ; tfinal=90 
##          t0: 2020-04-15 -- t1:  
##      Number of days considered for initial guess: 26 
##      Fatality rate: 0.02 
##      Population of the region: 6800000 
## -------------------------------------------------------------------------------- 
## [1] "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH"
##      beta     gamma 
## 1.0000000 0.9690821 
##   R0 = 1.03190432034675 
##   Max nbr of infected: 514.58  ( 0.01 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 10.29 
##   Max reached at day : 90 ==>  2020-07-14 
## ================================================================================

## Warning in xy.coords(x, y, xlabel, ylabel, log = log, recycle = TRUE): 1 y value
## <= 0 omitted from logarithmic plot

## $Infected
##  [1] 35 48 49 49 49 51 51 51 59 60 61 61 61 61 61 61 61 63 63 63 63 63 64 64 64
## [26] 64
## 
## $model
##    time       S         I           R
## 1     1 6799965  35.00000     0.00000
## 2     2 6799929  36.09875    34.44753
## 3     3 6799893  37.23179    69.97638
## 4     4 6799855  38.40019   106.62028
## 5     5 6799816  39.60503   144.41402
## 6     6 6799776  40.84744   183.39346
## 7     7 6799734  42.12856   223.59557
## 8     8 6799691  43.44960   265.05843
## 9     9 6799647  44.81178   307.82131
## 10   10 6799602  46.21635   351.92470
## 11   11 6799555  47.66463   397.41031
## 12   12 6799507  49.15795   444.32113
## 13   13 6799457  50.69768   492.70148
## 14   14 6799405  52.28526   542.59702
## 15   15 6799352  53.92213   594.05481
## 16   16 6799297  55.60981   647.12337
## 17   17 6799241  57.34984   701.85266
## 18   18 6799183  59.14381   758.29420
## 19   19 6799123  60.99338   816.50104
## 20   20 6799061  62.90022   876.52788
## 21   21 6798997  64.86607   938.43105
## 22   22 6798931  66.89273  1002.26862
## 23   23 6798863  68.98203  1068.10038
## 24   24 6798793  71.13586  1135.98796
## 25   25 6798721  73.35617  1205.99483
## 26   26 6798646  75.64497  1278.18638
## 27   27 6798569  78.00432  1352.62999
## 28   28 6798490  80.43633  1429.39503
## 29   29 6798409  82.94318  1508.55297
## 30   30 6798324  85.52712  1590.17742
## 31   31 6798237  88.19044  1674.34421
## 32   32 6798148  90.93553  1761.13139
## 33   33 6798056  93.76480  1850.61938
## 34   34 6797960  96.68077  1942.89098
## 35   35 6797862  99.68601  2038.03143
## 36   36 6797761 102.78316  2136.12852
## 37   37 6797657 105.97492  2237.27263
## 38   38 6797549 109.26411  2341.55679
## 39   39 6797438 112.65357  2449.07680
## 40   40 6797324 116.14625  2559.93124
## 41   41 6797206 119.74517  2674.22161
## 42   42 6797084 123.45343  2792.05235
## 43   43 6796959 127.27422  2913.53098
## 44   44 6796830 131.21081  3038.76810
## 45   45 6796697 135.26655  3167.87756
## 46   46 6796560 139.44488  3300.97648
## 47   47 6796418 143.74932  3438.18536
## 48   48 6796272 148.18351  3579.62818
## 49   49 6796122 152.75115  3725.43245
## 50   50 6795967 157.45605  3875.72934
## 51   51 6795807 162.30211  4030.65374
## 52   52 6795642 167.29333  4190.34437
## 53   53 6795473 172.43380  4354.94389
## 54   54 6795298 177.72772  4524.59896
## 55   55 6795117 183.17939  4699.46035
## 56   56 6794932 188.79319  4879.68306
## 57   57 6794740 194.57364  5065.42639
## 58   58 6794543 200.52535  5256.85405
## 59   59 6794339 206.65301  5454.13427
## 60   60 6794130 212.96146  5657.43992
## 61   61 6793914 219.45562  5866.94855
## 62   62 6793691 226.14053  6082.84256
## 63   63 6793462 233.02132  6305.30929
## 64   64 6793225 240.10325  6534.54110
## 65   65 6792982 247.39169  6770.73551
## 66   66 6792731 254.89210  7014.09528
## 67   67 6792473 262.61008  7264.82853
## 68   68 6792206 270.55132  7523.14885
## 69   69 6791932 278.72162  7789.27540
## 70   70 6791649 287.12690  8063.43302
## 71   71 6791358 295.77317  8345.85232
## 72   72 6791059 304.66658  8636.76982
## 73   73 6790750 313.81335  8936.42802
## 74   74 6790432 323.21983  9245.07551
## 75   75 6790104 332.89247  9562.96710
## 76   76 6789767 342.83781  9890.36387
## 77   77 6789419 353.06250 10227.53330
## 78   78 6789062 363.57327 10574.74937
## 79   79 6788693 374.37697 10932.29261
## 80   80 6788314 385.48051 11300.45025
## 81   81 6787924 396.89090 11679.51625
## 82   82 6787522 408.61524 12069.79140
## 83   83 6787108 420.66069 12471.58341
## 84   84 6786682 433.03449 12885.20696
## 85   85 6786243 445.74392 13310.98376
## 86   86 6785792 458.79636 13749.24266
## 87   87 6785327 472.19921 14200.31963
## 88   88 6784849 485.95992 14664.55786
## 89   89 6784358 500.08598 15142.30777
## 90   90 6783851 514.58490 15633.92705
## 
## $params
## $params$beta
## beta 
##    1 
## 
## $params$gamma
##     gamma 
## 0.9690821 
## 
## $params$R0
##       R0 
## 1.031904

generate.SIR.model(data,"Saudi Arabia", tot.population=33700000)

## ################################################################################

## This is an experimental feature, being currently under active development!
## Please check the development version of the package for the latest updates on it

## ################################################################################ 
## Processing...  SAUDI ARABIA 
##   [1]      0      0      0      0      0      0      0      0      0      0
##  [11]      0      0      0      0      0      0      0      0      0      0
##  [21]      0      0      0      0      0      0      0      0      0      0
##  [31]      0      0      0      0      0      0      0      0      0      0
##  [41]      1      1      1      5      5      5     11     15     20     21
##  [51]     45     86    103    103    118    171    171    274    344    392
##  [61]    511    562    767    900   1012   1104   1203   1299   1453   1563
##  [71]   1720   1885   2039   2179   2402   2605   2795   2932   3287   3651
##  [81]   4033   4462   4934   5369   5862   6380   7142   8274   9362  10484
##  [91]  11631  12772  13930  15102  16299  17522  18811  20077  21402  22753
## [101]  24097  25459  27011  28656  30251  31938  33731  35432  37136  39048
## [111]  41014  42925  44830  46869  49176  52016  54752  57345  59854  62545
## [121]  65077  67719  70161  72560  74795  76726  78541  80185  81766  83384
## [131]  85261  87142  89011  91182  93157  95748  98869 101914 105283 108571
## [141] 112288 116021 119942 123308 127541 132048 136315 141234 145991 150292
## [151] 154233 157612 161005 164144 167267 170639 174577 178504 182493 186436
## [161] 190823 194225 197608 201801 205929 209509 213716 217108 220144 223327
## [171] 226486 229480 232259 235111 237803 240474 243238 245851 248416 250920
## [181] 253349 255825 258156 260394 262772 264973 266941 268934 270831 272590
## [191] 274219 275905 277478 278835 280093 281456 282824 284226 285793 287262
## [201] 288690 289947 291468 293037 294519 295902 297315 298542 299914 301323
## [211] 302686 303973 305186 306370 307479 308654 309768 310836 311855 312924
## [221] 313911 314821 315772 316670 317486 318319 319141 319932 320688 321456
## [231] 322237 323012 323720 324407 325050 325651 326258 326930 327551 328144
## [241] 328720 329271 329754 330246 330798 331359 331857 332329 332790 333193
## [251] 333648 334187 334605 335097 335578 335997 336387 336766 337243 337711
## [261] 338132 338539 338944 339267 339615 340089 340590 341062 341495 341854
## [271] 342202 342583 342968 343373 343774 344157 344552 344875 345232 345631
## [281] 346047 346482 346880 347282 347656 348037 348510 348936 349386 349822
## [291] 350229 350592 350984 351455 351849 352160 352601 352950 353255 353556
## [301] 353918 354208 354527 354813 355034 355258 355489 355741 356067 356389
## [311] 356691 356911 357128 357360 357623 357872 358102 358336 358526 358713
## [321] 358922 359115 359274 359415 359583 359749 359888 360013 360155 360335
## [331] 360516 360690 360848 361010 361178 361359 361536 361725 361903 362066
## [341] 362220 362339 362488 362601 362741 362878 362979 363061 363155 363259
## [351] 363377 363485 363582 363692 363809 363949 364096 364271 364440 364613
## [361] 364753 364929 365099 365325 365563 365775 365988 366185 366371 366584
## [371] 366807 367023 367276 367543 367813 368074 368329 368639 368945 369248
## [381] 369575 369961 370278 370634 370987 371356 371720 372073 372410 372732
## [391] 373046 373368 373702 374029 374366 374691 375006 375333 375668 376021
## [401] 376377 376723 377061 377383 377700 378002 378333 378708 379092 379474
## [411] 379831 380182 380572 380958 381348 381708 382059 382407 382752 383106
## [421] 383499 383880 384271 384653 385020 385424 385834 386300 386782 387292
## [431] 387794 388325 388866 389422 390007 390597 391325 392009 392682 393377
## [441] 394169 394952 395854 396758 397636 398435 399277 400228 401157 402142
## [451] 403106 404054 404970 405940 407010 408038 409093 410191 411263 412216
## [461] 413174 414219 415281 416307 417363 418411 419348 420301 421300 422316
## [471] 423406 424445 425442 426384 427370 428369 429389 430505 431432 432269
## [481] 433094 433980 435027 436239 437569 438705 439847 440914 442071 443460
## [491] 444780 445963 447178 448284 449191 450436 451687 452956 454217 455418
## [501] 456562 457546 458707 459968 461242 461242 463703 464780 465797 466906
## [511] 468175 469414 470723 471959 473112 474191 475403 476882 478135 479390
## [521] 480702 482003 483221 484539 486106 487592 489126 490464 491612 492785
## [531] 494032 495309 496516 497773 498906 500083 501195 502439 503734 504960
## [541] 506125 507423 508521 509576 510869 512142 513284 514446 515693 516949
## [551] 518143 519395 520774 522108 522108 522108 525730 526814 526814 526814
## [561] 526814 526814 531935 531935 531935 531935 531935 531935 531935 537374
## [571] 537374 537374 539129 539129 539129 540244 540244 541201 541201 541994
## [581] 541994 541994 541994 543318 543318 543318 543318 543318 543318 543318
## [591] 543318 543318 545243 545243 545505 545505 545727 545829 545829 545829
## [601] 545829 545829 545829 546336 546411 546411 546411 546612 546681 546735
## [611] 546735 546735 546882 546926 546985 547035 547035 547134 547134 547134
## [621] 547134 547134 547357 547402 547449 547497 547532 547591 547649 547704
## [631] 547761 547797 547845 547890 547931 547969 548018 548065 548111 548162
## [641] 548205 548252 548303 548368 548423 548474 548530 548571 548617 548666
## [651] 548711 548760 548805 548848 548890 548930 548973 549022 549060 549103
## [661] 549148 549192 549222 549260 549297 549339 549377 549412 549443 549479
## [671] 549518 549556 549590 549618 549642 549671 549695 549720 549752 549786
## [681] 549810 549848 549877 549912 549955 549997 550043 550088 550136 550189
## [691] 550240 550304 550369 550457 550542 550622 550738 550842 550988 551210
## [701] 551462 551749 552081 552406 552795 553319 553921 554665 555417 556236
## [711] 557082 558106 559852 562437 565482 568650 572225 575293 578753 583531
## [721] 588183 593545 599044 604672 609953 615430 620935 626808 632736 638327
## [731] 643211 647819 652354 657192 661733 666259 670997 675471 679384 683053
## [741] 687264 691125 695217 699069 702624 705637 708897 712644 715974 719136
## [751] 722002 724525 726251 728387 730614 732596 734389 735958 737334 738331
## [761] 739344 740396 741237 741864
## [1] 50
##  [1]   21   45   86  103  103  118  171  171  274  344  392  511  562  767  900
## [16] 1012 1104 1203 1299 1453 1563 1720 1885 2039 2179 2402
## ------------------------  Parameters used to create model ------------------------ 
##      Region: SAUDI ARABIA 
##      Time interval to consider: t0=50 - t1= ; tfinal=90 
##          t0: 2020-03-12 -- t1:  
##      Number of days considered for initial guess: 26 
##      Fatality rate: 0.02 
##      Population of the region: 33700000 
## -------------------------------------------------------------------------------- 
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
##      beta     gamma 
## 0.5994712 0.4005289 
##   R0 = 1.49669913525661 
##   Max nbr of infected: 2103442.7  ( 6.24 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 42068.85 
##   Max reached at day : 67 ==>  2020-05-18 
## ================================================================================

## Warning in xy.coords(x, y, xlabel, ylabel, log = log, recycle = TRUE): 1 y value
## <= 0 omitted from logarithmic plot

## $Infected
##  [1]   21   45   86  103  103  118  171  171  274  344  392  511  562  767  900
## [16] 1012 1104 1203 1299 1453 1563 1720 1885 2039 2179 2402
## 
## $model
##    time        S            I            R
## 1     1 33699979 2.100000e+01 0.000000e+00
## 2     2 33699965 2.562233e+01 9.306122e+00
## 3     3 33699948 3.126208e+01 2.066063e+01
## 4     4 33699927 3.814319e+01 3.451437e+01
## 5     5 33699902 4.653888e+01 5.141747e+01
## 6     6 33699871 5.678253e+01 7.204114e+01
## 7     7 33699834 6.928085e+01 9.720422e+01
## 8     8 33699788 8.453008e+01 1.279059e+02
## 9     9 33699731 1.031357e+02 1.653652e+02
## 10   10 33699663 1.258364e+02 2.110696e+02
## 11   11 33699580 1.535335e+02 2.668337e+02
## 12   12 33699478 1.873264e+02 3.348716e+02
## 13   13 33699354 2.285568e+02 4.178847e+02
## 14   14 33699202 2.788612e+02 5.191688e+02
## 15   15 33699017 3.402365e+02 6.427449e+02
## 16   16 33698791 4.151185e+02 7.935189e+02
## 17   17 33698516 5.064789e+02 9.774761e+02
## 18   18 33698180 6.179427e+02 1.201918e+03
## 19   19 33697770 7.539321e+02 1.475754e+03
## 20   20 33697270 9.198410e+02 1.809851e+03
## 21   21 33696660 1.122248e+03 2.217467e+03
## 22   22 33695916 1.369178e+03 2.714774e+03
## 23   23 33695008 1.670416e+03 3.321500e+03
## 24   24 33693900 2.037894e+03 4.061707e+03
## 25   25 33692549 2.486160e+03 4.964743e+03
## 26   26 33690901 3.032948e+03 6.066402e+03
## 27   27 33688890 3.699872e+03 7.410329e+03
## 28   28 33686437 4.513270e+03 9.049745e+03
## 29   29 33683445 5.505224e+03 1.104953e+04
## 30   30 33679796 6.714800e+03 1.348877e+04
## 31   31 33675347 8.189549e+03 1.646383e+04
## 32   32 33669921 9.987317e+03 2.009214e+04
## 33   33 33663305 1.217843e+04 2.451670e+04
## 34   34 33655240 1.484832e+04 2.991161e+04
## 35   35 33645411 1.810066e+04 3.648874e+04
## 36   36 33633433 2.206112e+04 4.450572e+04
## 37   37 33618843 2.688180e+04 5.427568e+04
## 38   38 33601075 3.274648e+04 6.617882e+04
## 39   39 33579447 3.987669e+04 8.067627e+04
## 40   40 33553135 4.853880e+04 9.832665e+04
## 41   41 33521142 5.905197e+04 1.198055e+05
## 42   42 33482275 7.179711e+04 1.459284e+05
## 43   43 33435096 8.722643e+04 1.776772e+05
## 44   44 33377896 1.058735e+05 2.162310e+05
## 45   45 33308637 1.283628e+05 2.630005e+05
## 46   46 33224916 1.554183e+05 3.196662e+05
## 47   47 33123913 1.878682e+05 3.882193e+05
## 48   48 33002351 2.266455e+05 4.710038e+05
## 49   49 32856464 2.727787e+05 5.707571e+05
## 50   50 32681986 3.273702e+05 6.906443e+05
## 51   51 32474163 3.915564e+05 8.342805e+05
## 52   52 32227824 4.664432e+05 1.005733e+06
## 53   53 31937500 5.530110e+05 1.209489e+06
## 54   54 31597630 6.519857e+05 1.450384e+06
## 55   55 31202859 7.636742e+05 1.733467e+06
## 56   56 30748438 8.877706e+05 2.063791e+06
## 57   57 30230714 1.023152e+06 2.446133e+06
## 58   58 29647684 1.167693e+06 2.884623e+06
## 59   59 28999548 1.318136e+06 3.382316e+06
## 60   60 28289185 1.470082e+06 3.940733e+06
## 61   61 27522453 1.618126e+06 4.559421e+06
## 62   62 26708203 1.756183e+06 5.235615e+06
## 63   63 25857975 1.877972e+06 5.964053e+06
## 64   64 24985353 1.977627e+06 6.737020e+06
## 65   65 24105055 2.050307e+06 7.544638e+06
## 66   66 23231896 2.092720e+06 8.375384e+06
## 67   67 22379772 2.103443e+06 9.216785e+06
## 68   68 21560814 2.082996e+06 1.005619e+07
## 69   69 20784812 2.033668e+06 1.088152e+07
## 70   70 20058949 1.959145e+06 1.168191e+07
## 71   71 19387804 1.864038e+06 1.244816e+07
## 72   72 18773582 1.753385e+06 1.317303e+07
## 73   73 18216474 1.632208e+06 1.385132e+07
## 74   74 17715079 1.505172e+06 1.447975e+07
## 75   75 17266829 1.376356e+06 1.505682e+07
## 76   76 16868367 1.249128e+06 1.558251e+07
## 77   77 16515873 1.126120e+06 1.605801e+07
## 78   78 16205320 1.009262e+06 1.648542e+07
## 79   79 15932662 8.998565e+05 1.686748e+07
## 80   80 15693972 7.986744e+05 1.720735e+07
## 81   81 15485530 7.060597e+05 1.750841e+07
## 82   82 15303879 6.220259e+05 1.777410e+07
## 83   83 15145852 5.463430e+05 1.800780e+07
## 84   84 15008579 4.786118e+05 1.821281e+07
## 85   85 14889482 4.183239e+05 1.839219e+07
## 86   86 14786262 3.649091e+05 1.854883e+07
## 87   87 14696884 3.177708e+05 1.868535e+07
## 88   88 14619548 2.763123e+05 1.880414e+07
## 89   89 14552676 2.399554e+05 1.890737e+07
## 90   90 14494883 2.081518e+05 1.899697e+07
## 
## $params
## $params$beta
##      beta 
## 0.5994712 
## 
## $params$gamma
##     gamma 
## 0.4005289 
## 
## $params$R0
##       R0 
## 1.496699

generate.SIR.model(data,"Egypt",tot.population=101463702)

## ################################################################################

## This is an experimental feature, being currently under active development!
## Please check the development version of the package for the latest updates on it

## ################################################################################ 
## Processing...  EGYPT 
##   [1]      0      0      0      0      0      0      0      0      0      0
##  [11]      0      0      0      0      0      0      0      0      0      0
##  [21]      0      0      0      1      1      1      1      1      1      1
##  [31]      1      1      1      1      1      1      1      1      1      2
##  [41]      2      2      2      3     15     15     49     55     59     60
##  [51]     67     80    109    110    150    196    196    256    285    294
##  [61]    327    366    402    456    495    536    576    609    656    710
##  [71]    779    865    985   1070   1173   1322   1450   1560   1699   1794
##  [81]   1939   2065   2190   2350   2505   2673   2844   3032   3144   3333
##  [91]   3490   3659   3891   4092   4319   4534   4782   5042   5268   5537
## [101]   5895   6193   6465   6813   7201   7588   7981   8476   8964   9400
## [111]   9746  10093  10431  10829  11228  11719  12229  12764  13484  14229
## [121]  15003  15786  16513  17265  17967  18756  19666  20793  22082  23449
## [131]  24985  26384  27536  28615  29767  31115  32612  34079  35444  36829
## [141]  38284  39726  41303  42980  44598  46289  47856  49219  50437  52211
## [151]  53758  55233  56809  58141  59561  61130  62755  63923  65188  66754
## [161]  68311  69814  71299  72711  74035  75253  76222  77279  78304  79254
## [171]  80235  81158  82070  83001  83930  84843  85771  86474  87172  87775
## [181]  88402  89078  89745  90413  91072  91583  92062  92482  92947  93356
## [191]  93757  94078  94316  94483  94640  94752  94875  95006  95147  95314
## [201]  95492  95666  95834  95963  96108  96220  96336  96475  96590  96753
## [211]  96914  97025  97148  97237  97340  97478  97619  97825  98062  98285
## [221]  98497  98727  98939  99115  99280  99425  99582  99712  99863 100041
## [231] 100228 100403 100557 100708 100856 101009 101177 101340 101500 101641
## [241] 101772 101900 102015 102141 102254 102375 102513 102625 102736 102840
## [251] 102955 103079 103198 103317 103466 103575 103683 103781 103902 104035
## [261] 104156 104262 104387 104516 104648 104787 104915 105033 105159 105297
## [271] 105424 105547 105705 105883 106060 106230 106397 106540 106707 106877
## [281] 107030 107209 107376 107555 107736 107925 108122 108329 108530 108754
## [291] 108962 109201 109422 109654 109881 110095 110319 110547 110767 111009
## [301] 111284 111613 111955 112318 112676 113027 113381 113742 114107 114475
## [311] 114832 115183 115541 115911 116303 116724 117156 117583 118014 118432
## [321] 118847 119281 119702 120147 120611 121089 121575 122086 122609 123153
## [331] 123701 124280 124891 125555 126273 127061 127972 128993 130126 131315
## [341] 132541 133900 135233 136644 138062 139471 140878 142187 143464 144583
## [351] 145590 146809 147810 148799 149792 150753 151723 152719 153741 154620
## [361] 155507 156397 157275 158174 158963 159715 160463 161143 161817 162486
## [371] 163129 163761 164282 164871 165418 165951 166492 167013 167525 168057
## [381] 168597 169106 169640 170207 170780 171390 171993 172602 173202 173813
## [391] 174426 175059 175677 176333 176943 177543 178151 178774 179407 180051
## [401] 180640 181241 181829 182424 183010 183591 184168 184755 185334 185922
## [411] 186503 187094 187716 188361 189000 189639 190280 190924 191555 192195
## [421] 192840 193482 194127 194771 195418 196061 196709 197350 198011 198681
## [431] 199364 200050 200739 201432 202131 202843 203546 204256 204965 205732
## [441] 206510 207293 208082 208876 209677 210489 211307 212130 212961 213798
## [451] 214639 215484 216334 217186 218041 218902 219774 220658 221570 222523
## [461] 223514 224517 225528 226531 227552 228584 229635 230713 231803 232905
## [471] 234015 235140 236272 237410 238560 239740 240927 242120 243317 244520
## [481] 245721 246909 248078 249238 250391 251539 252690 253835 254984 256124
## [491] 257275 258407 259540 260659 261666 262650 263606 264557 265489 266350
## [501] 267171 267972 268754 269527 270292 271047 271780 272491 273182 273795
## [511] 274404 275010 275601 276190 276756 277288 277797 278295 278761 279184
## [521] 279596 280005 280394 280770 281031 281282 281524 281722 281903 282082
## [531] 282257 282421 282582 282737 282864 282985 283102 283212 283320 283409
## [541] 283490 283567 283636 283636 283762 283813 283862 283906 283947 283985
## [551] 284024 284059 284090 284128 284170 284215 284262 284311 284362 284415
## [561] 284472 284523 284580 284641 284706 284789 284875 284966 285061 285158
## [571] 285257 285358 285465 285577 285700 285831 285995 286168 286352 286541
## [581] 286735 286938 287159 287393 287644 287899 288162 288441 288732 289035
## [591] 289353 289684 290027 290395 290773 291172 291585 292018 292476 292957
## [601] 293448 293951 294482 295051 295639 296276 296929 297608 298296 298988
## [611] 299710 300278 300945 301625 302327 303045 303783 304524 305269 306030
## [621] 306798 307569 308347 309135 309934 310745 311576 312413 313259 314116
## [631] 314977 315842 316711 317585 318456 319339 320207 321084 321967 322852
## [641] 323733 324619 325508 326379 327286 328209 329136 330084 331017 331968
## [651] 332889 333840 334751 335673 336582 337485 338414 339335 340269 341188
## [661] 342097 343026 343026 344907 345848 346808 347719 348611 349513 350397
## [671] 351267 352123 353024 353923 354836 355767 356718 357629 358578 359516
## [681] 360435 361368 362260 363162 364033 364922 365831 366634 367456 368335
## [691] 369198 369198 370819 371698 372599 373509 374411 375330 376233 377081
## [701] 377960 378843 379654 380520 381343 382194 383003 383857 384728 385575
## [711] 386358 387159 387882 388651 389454 390294 391115 391945 392857 393808
## [721] 394740 395688 396699 397778 398879 400076 401308 402611 403990 405393
## [731] 406926 408495 410098 411749 413558 415468 417453 419460 421478 423688
## [741] 425911 428202 430480 432761 435052 437350 439651 441923 444117 446308
## [751] 448497 450676 452821 452821 457081 459198 461299 463370 465423 467448
## [761] 469457 471460 473449 475341
## [1] 44
##  [1]   3  15  15  49  55  59  60  67  80 109 110 150 196 196 256 285 294 327 366
## [20] 402 456 495 536 576 609 656
## ------------------------  Parameters used to create model ------------------------ 
##      Region: EGYPT 
##      Time interval to consider: t0=44 - t1= ; tfinal=90 
##          t0: 2020-03-06 -- t1:  
##      Number of days considered for initial guess: 26 
##      Fatality rate: 0.02 
##      Population of the region: 101463702 
## -------------------------------------------------------------------------------- 
## [1] "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH"
##      beta     gamma 
## 0.6134295 0.3865706 
##   R0 = 1.58684989175993 
##   Max nbr of infected: 7990478.47  ( 7.88 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 159809.57 
##   Max reached at day : 73 ==>  2020-05-18 
## ================================================================================

## Warning in xy.coords(x, y, xlabel, ylabel, log = log, recycle = TRUE): 1 y value
## <= 0 omitted from logarithmic plot

## $Infected
##  [1]   3  15  15  49  55  59  60  67  80 109 110 150 196 196 256 285 294 327 366
## [20] 402 456 495 536 576 609 656
## 
## $model
##    time         S            I            R
## 1     1 101463699 3.000000e+00 0.000000e+00
## 2     2 101463697 3.763958e+00 1.301796e+00
## 3     3 101463694 4.722461e+00 2.935097e+00
## 4     4 101463691 5.925049e+00 4.984324e+00
## 5     5 101463687 7.433880e+00 7.555392e+00
## 6     6 101463682 9.326938e+00 1.078119e+01
## 7     7 101463675 1.170208e+01 1.482846e+01
## 8     8 101463667 1.468204e+01 1.990636e+01
## 9     9 101463657 1.842085e+01 2.627736e+01
## 10   10 101463645 2.311178e+01 3.427076e+01
## 11   11 101463629 2.899725e+01 4.429970e+01
## 12   12 101463609 3.638147e+01 5.688253e+01
## 13   13 101463584 4.564609e+01 7.266962e+01
## 14   14 101463552 5.726996e+01 9.247691e+01
## 15   15 101463513 7.185385e+01 1.173282e+02
## 16   16 101463463 9.015153e+01 1.485078e+02
## 17   17 101463401 1.131087e+02 1.876275e+02
## 18   18 101463323 1.419119e+02 2.367089e+02
## 19   19 101463226 1.780497e+02 2.982890e+02
## 20   20 101463103 2.233899e+02 3.755504e+02
## 21   21 101462949 2.802757e+02 4.724862e+02
## 22   22 101462756 3.516470e+02 5.941066e+02
## 23   23 101462514 4.411921e+02 7.466970e+02
## 24   24 101462210 5.535385e+02 9.381436e+02
## 25   25 101461829 6.944917e+02 1.178341e+03
## 26   26 101461351 8.713351e+02 1.479701e+03
## 27   27 101460751 1.093206e+03 1.857798e+03
## 28   28 101459998 1.371566e+03 2.332171e+03
## 29   29 101459054 1.720797e+03 2.927330e+03
## 30   30 101457869 2.158935e+03 3.674027e+03
## 31   31 101456383 2.708607e+03 4.610840e+03
## 32   32 101454518 3.398193e+03 5.786162e+03
## 33   33 101452178 4.263287e+03 7.260701e+03
## 34   34 101449243 5.348528e+03 9.110606e+03
## 35   35 101445561 6.709888e+03 1.143139e+04
## 36   36 101440942 8.417545e+03 1.434285e+04
## 37   37 101435147 1.055947e+04 1.799521e+04
## 38   38 101427879 1.324590e+04 2.257686e+04
## 39   39 101418763 1.661497e+04 2.832398e+04
## 40   40 101407330 2.083966e+04 3.553265e+04
## 41   41 101392992 2.613655e+04 4.457392e+04
## 42   42 101375013 3.277657e+04 5.591268e+04
## 43   43 101352472 4.109848e+04 7.013121e+04
## 44   44 101324218 5.152543e+04 8.795843e+04
## 45   45 101288810 6.458538e+04 1.103064e+05
## 46   46 101244450 8.093616e+04 1.383154e+05
## 47   47 101188896 1.013959e+05 1.734101e+05
## 48   48 101119354 1.269797e+05 2.173680e+05
## 49   49 101032354 1.589439e+05 2.724043e+05
## 50   50 100923591 1.988371e+05 3.412743e+05
## 51   51 100787744 2.485599e+05 4.273982e+05
## 52   52 100618261 3.104312e+05 5.350095e+05
## 53   53 100407112 3.872594e+05 6.693302e+05
## 54   54 100144515 4.824130e+05 8.367743e+05
## 55   55  99818643 5.998826e+05 1.045176e+06
## 56   56  99415342 7.443199e+05 1.304040e+06
## 57   57  98917874 9.210313e+05 1.624797e+06
## 58   58  98306765 1.135895e+06 2.021042e+06
## 59   59  97559815 1.395162e+06 2.508725e+06
## 60   60  96652395 1.705079e+06 3.106228e+06
## 61   61  95558151 2.071298e+06 3.834253e+06
## 62   62  94250262 2.498002e+06 4.715438e+06
## 63   63  92703367 2.986761e+06 5.773574e+06
## 64   64  90896210 3.535158e+06 7.032334e+06
## 65   65  88814878 4.135368e+06 8.513456e+06
## 66   66  86456307 4.772984e+06 1.023441e+07
## 67   67  83831483 5.426494e+06 1.220573e+07
## 68   68  80967539 6.067851e+06 1.442831e+07
## 69   69  77908010 6.664425e+06 1.689127e+07
## 70   70  74710724 7.182281e+06 1.957070e+07
## 71   71  71443390 7.590314e+06 2.243000e+07
## 72   72  68177551 7.864382e+06 2.542177e+07
## 73   73  64982073 7.990478e+06 2.849115e+07
## 74   74  61917409 7.966189e+06 3.158010e+07
## 75   75  59031625 7.800232e+06 3.463185e+07
## 76   76  56358548 7.510357e+06 3.759480e+07
## 77   77  53917922 7.120282e+06 4.042550e+07
## 78   78  51717017 6.656405e+06 4.309028e+07
## 79   79  49753067 6.144916e+06 4.556572e+07
## 80   80  48015926 5.609655e+06 4.783812e+07
## 81   81  46490568 5.070806e+06 4.990233e+07
## 82   82  45159173 4.544347e+06 5.176018e+07
## 83   83  44002748 4.042074e+06 5.341888e+07
## 84   84  43002284 3.571984e+06 5.488943e+07
## 85   85  42139536 3.138865e+06 5.618530e+07
## 86   86  41397489 2.744937e+06 5.732128e+07
## 87   87  40760605 2.390480e+06 5.831262e+07
## 88   88  40214921 2.074379e+06 5.917440e+07
## 89   89  39748029 1.794587e+06 5.992109e+07
## 90   90  39349007 1.548482e+06 6.056621e+07
## 
## $params
## $params$beta
##      beta 
## 0.6134295 
## 
## $params$gamma
##     gamma 
## 0.3865706 
## 
## $params$R0
##      R0 
## 1.58685

generate.SIR.model(data,"Egypt",tot.population=101463702)

## ################################################################################

## This is an experimental feature, being currently under active development!

## Please check the development version of the package for the latest updates on it

## ################################################################################ 
## Processing...  EGYPT 
##   [1]      0      0      0      0      0      0      0      0      0      0
##  [11]      0      0      0      0      0      0      0      0      0      0
##  [21]      0      0      0      1      1      1      1      1      1      1
##  [31]      1      1      1      1      1      1      1      1      1      2
##  [41]      2      2      2      3     15     15     49     55     59     60
##  [51]     67     80    109    110    150    196    196    256    285    294
##  [61]    327    366    402    456    495    536    576    609    656    710
##  [71]    779    865    985   1070   1173   1322   1450   1560   1699   1794
##  [81]   1939   2065   2190   2350   2505   2673   2844   3032   3144   3333
##  [91]   3490   3659   3891   4092   4319   4534   4782   5042   5268   5537
## [101]   5895   6193   6465   6813   7201   7588   7981   8476   8964   9400
## [111]   9746  10093  10431  10829  11228  11719  12229  12764  13484  14229
## [121]  15003  15786  16513  17265  17967  18756  19666  20793  22082  23449
## [131]  24985  26384  27536  28615  29767  31115  32612  34079  35444  36829
## [141]  38284  39726  41303  42980  44598  46289  47856  49219  50437  52211
## [151]  53758  55233  56809  58141  59561  61130  62755  63923  65188  66754
## [161]  68311  69814  71299  72711  74035  75253  76222  77279  78304  79254
## [171]  80235  81158  82070  83001  83930  84843  85771  86474  87172  87775
## [181]  88402  89078  89745  90413  91072  91583  92062  92482  92947  93356
## [191]  93757  94078  94316  94483  94640  94752  94875  95006  95147  95314
## [201]  95492  95666  95834  95963  96108  96220  96336  96475  96590  96753
## [211]  96914  97025  97148  97237  97340  97478  97619  97825  98062  98285
## [221]  98497  98727  98939  99115  99280  99425  99582  99712  99863 100041
## [231] 100228 100403 100557 100708 100856 101009 101177 101340 101500 101641
## [241] 101772 101900 102015 102141 102254 102375 102513 102625 102736 102840
## [251] 102955 103079 103198 103317 103466 103575 103683 103781 103902 104035
## [261] 104156 104262 104387 104516 104648 104787 104915 105033 105159 105297
## [271] 105424 105547 105705 105883 106060 106230 106397 106540 106707 106877
## [281] 107030 107209 107376 107555 107736 107925 108122 108329 108530 108754
## [291] 108962 109201 109422 109654 109881 110095 110319 110547 110767 111009
## [301] 111284 111613 111955 112318 112676 113027 113381 113742 114107 114475
## [311] 114832 115183 115541 115911 116303 116724 117156 117583 118014 118432
## [321] 118847 119281 119702 120147 120611 121089 121575 122086 122609 123153
## [331] 123701 124280 124891 125555 126273 127061 127972 128993 130126 131315
## [341] 132541 133900 135233 136644 138062 139471 140878 142187 143464 144583
## [351] 145590 146809 147810 148799 149792 150753 151723 152719 153741 154620
## [361] 155507 156397 157275 158174 158963 159715 160463 161143 161817 162486
## [371] 163129 163761 164282 164871 165418 165951 166492 167013 167525 168057
## [381] 168597 169106 169640 170207 170780 171390 171993 172602 173202 173813
## [391] 174426 175059 175677 176333 176943 177543 178151 178774 179407 180051
## [401] 180640 181241 181829 182424 183010 183591 184168 184755 185334 185922
## [411] 186503 187094 187716 188361 189000 189639 190280 190924 191555 192195
## [421] 192840 193482 194127 194771 195418 196061 196709 197350 198011 198681
## [431] 199364 200050 200739 201432 202131 202843 203546 204256 204965 205732
## [441] 206510 207293 208082 208876 209677 210489 211307 212130 212961 213798
## [451] 214639 215484 216334 217186 218041 218902 219774 220658 221570 222523
## [461] 223514 224517 225528 226531 227552 228584 229635 230713 231803 232905
## [471] 234015 235140 236272 237410 238560 239740 240927 242120 243317 244520
## [481] 245721 246909 248078 249238 250391 251539 252690 253835 254984 256124
## [491] 257275 258407 259540 260659 261666 262650 263606 264557 265489 266350
## [501] 267171 267972 268754 269527 270292 271047 271780 272491 273182 273795
## [511] 274404 275010 275601 276190 276756 277288 277797 278295 278761 279184
## [521] 279596 280005 280394 280770 281031 281282 281524 281722 281903 282082
## [531] 282257 282421 282582 282737 282864 282985 283102 283212 283320 283409
## [541] 283490 283567 283636 283636 283762 283813 283862 283906 283947 283985
## [551] 284024 284059 284090 284128 284170 284215 284262 284311 284362 284415
## [561] 284472 284523 284580 284641 284706 284789 284875 284966 285061 285158
## [571] 285257 285358 285465 285577 285700 285831 285995 286168 286352 286541
## [581] 286735 286938 287159 287393 287644 287899 288162 288441 288732 289035
## [591] 289353 289684 290027 290395 290773 291172 291585 292018 292476 292957
## [601] 293448 293951 294482 295051 295639 296276 296929 297608 298296 298988
## [611] 299710 300278 300945 301625 302327 303045 303783 304524 305269 306030
## [621] 306798 307569 308347 309135 309934 310745 311576 312413 313259 314116
## [631] 314977 315842 316711 317585 318456 319339 320207 321084 321967 322852
## [641] 323733 324619 325508 326379 327286 328209 329136 330084 331017 331968
## [651] 332889 333840 334751 335673 336582 337485 338414 339335 340269 341188
## [661] 342097 343026 343026 344907 345848 346808 347719 348611 349513 350397
## [671] 351267 352123 353024 353923 354836 355767 356718 357629 358578 359516
## [681] 360435 361368 362260 363162 364033 364922 365831 366634 367456 368335
## [691] 369198 369198 370819 371698 372599 373509 374411 375330 376233 377081
## [701] 377960 378843 379654 380520 381343 382194 383003 383857 384728 385575
## [711] 386358 387159 387882 388651 389454 390294 391115 391945 392857 393808
## [721] 394740 395688 396699 397778 398879 400076 401308 402611 403990 405393
## [731] 406926 408495 410098 411749 413558 415468 417453 419460 421478 423688
## [741] 425911 428202 430480 432761 435052 437350 439651 441923 444117 446308
## [751] 448497 450676 452821 452821 457081 459198 461299 463370 465423 467448
## [761] 469457 471460 473449 475341
## [1] 44
##  [1]   3  15  15  49  55  59  60  67  80 109 110 150 196 196 256 285 294 327 366
## [20] 402 456 495 536 576 609 656
## ------------------------  Parameters used to create model ------------------------ 
##      Region: EGYPT 
##      Time interval to consider: t0=44 - t1= ; tfinal=90 
##          t0: 2020-03-06 -- t1:  
##      Number of days considered for initial guess: 26 
##      Fatality rate: 0.02 
##      Population of the region: 101463702 
## -------------------------------------------------------------------------------- 
## [1] "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH"
##      beta     gamma 
## 0.6134295 0.3865706 
##   R0 = 1.58684989175993 
##   Max nbr of infected: 7990478.47  ( 7.88 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 159809.57 
##   Max reached at day : 73 ==>  2020-05-18 
## ================================================================================

## Warning in xy.coords(x, y, xlabel, ylabel, log = log, recycle = TRUE): 1 y value
## <= 0 omitted from logarithmic plot

## $Infected
##  [1]   3  15  15  49  55  59  60  67  80 109 110 150 196 196 256 285 294 327 366
## [20] 402 456 495 536 576 609 656
## 
## $model
##    time         S            I            R
## 1     1 101463699 3.000000e+00 0.000000e+00
## 2     2 101463697 3.763958e+00 1.301796e+00
## 3     3 101463694 4.722461e+00 2.935097e+00
## 4     4 101463691 5.925049e+00 4.984324e+00
## 5     5 101463687 7.433880e+00 7.555392e+00
## 6     6 101463682 9.326938e+00 1.078119e+01
## 7     7 101463675 1.170208e+01 1.482846e+01
## 8     8 101463667 1.468204e+01 1.990636e+01
## 9     9 101463657 1.842085e+01 2.627736e+01
## 10   10 101463645 2.311178e+01 3.427076e+01
## 11   11 101463629 2.899725e+01 4.429970e+01
## 12   12 101463609 3.638147e+01 5.688253e+01
## 13   13 101463584 4.564609e+01 7.266962e+01
## 14   14 101463552 5.726996e+01 9.247691e+01
## 15   15 101463513 7.185385e+01 1.173282e+02
## 16   16 101463463 9.015153e+01 1.485078e+02
## 17   17 101463401 1.131087e+02 1.876275e+02
## 18   18 101463323 1.419119e+02 2.367089e+02
## 19   19 101463226 1.780497e+02 2.982890e+02
## 20   20 101463103 2.233899e+02 3.755504e+02
## 21   21 101462949 2.802757e+02 4.724862e+02
## 22   22 101462756 3.516470e+02 5.941066e+02
## 23   23 101462514 4.411921e+02 7.466970e+02
## 24   24 101462210 5.535385e+02 9.381436e+02
## 25   25 101461829 6.944917e+02 1.178341e+03
## 26   26 101461351 8.713351e+02 1.479701e+03
## 27   27 101460751 1.093206e+03 1.857798e+03
## 28   28 101459998 1.371566e+03 2.332171e+03
## 29   29 101459054 1.720797e+03 2.927330e+03
## 30   30 101457869 2.158935e+03 3.674027e+03
## 31   31 101456383 2.708607e+03 4.610840e+03
## 32   32 101454518 3.398193e+03 5.786162e+03
## 33   33 101452178 4.263287e+03 7.260701e+03
## 34   34 101449243 5.348528e+03 9.110606e+03
## 35   35 101445561 6.709888e+03 1.143139e+04
## 36   36 101440942 8.417545e+03 1.434285e+04
## 37   37 101435147 1.055947e+04 1.799521e+04
## 38   38 101427879 1.324590e+04 2.257686e+04
## 39   39 101418763 1.661497e+04 2.832398e+04
## 40   40 101407330 2.083966e+04 3.553265e+04
## 41   41 101392992 2.613655e+04 4.457392e+04
## 42   42 101375013 3.277657e+04 5.591268e+04
## 43   43 101352472 4.109848e+04 7.013121e+04
## 44   44 101324218 5.152543e+04 8.795843e+04
## 45   45 101288810 6.458538e+04 1.103064e+05
## 46   46 101244450 8.093616e+04 1.383154e+05
## 47   47 101188896 1.013959e+05 1.734101e+05
## 48   48 101119354 1.269797e+05 2.173680e+05
## 49   49 101032354 1.589439e+05 2.724043e+05
## 50   50 100923591 1.988371e+05 3.412743e+05
## 51   51 100787744 2.485599e+05 4.273982e+05
## 52   52 100618261 3.104312e+05 5.350095e+05
## 53   53 100407112 3.872594e+05 6.693302e+05
## 54   54 100144515 4.824130e+05 8.367743e+05
## 55   55  99818643 5.998826e+05 1.045176e+06
## 56   56  99415342 7.443199e+05 1.304040e+06
## 57   57  98917874 9.210313e+05 1.624797e+06
## 58   58  98306765 1.135895e+06 2.021042e+06
## 59   59  97559815 1.395162e+06 2.508725e+06
## 60   60  96652395 1.705079e+06 3.106228e+06
## 61   61  95558151 2.071298e+06 3.834253e+06
## 62   62  94250262 2.498002e+06 4.715438e+06
## 63   63  92703367 2.986761e+06 5.773574e+06
## 64   64  90896210 3.535158e+06 7.032334e+06
## 65   65  88814878 4.135368e+06 8.513456e+06
## 66   66  86456307 4.772984e+06 1.023441e+07
## 67   67  83831483 5.426494e+06 1.220573e+07
## 68   68  80967539 6.067851e+06 1.442831e+07
## 69   69  77908010 6.664425e+06 1.689127e+07
## 70   70  74710724 7.182281e+06 1.957070e+07
## 71   71  71443390 7.590314e+06 2.243000e+07
## 72   72  68177551 7.864382e+06 2.542177e+07
## 73   73  64982073 7.990478e+06 2.849115e+07
## 74   74  61917409 7.966189e+06 3.158010e+07
## 75   75  59031625 7.800232e+06 3.463185e+07
## 76   76  56358548 7.510357e+06 3.759480e+07
## 77   77  53917922 7.120282e+06 4.042550e+07
## 78   78  51717017 6.656405e+06 4.309028e+07
## 79   79  49753067 6.144916e+06 4.556572e+07
## 80   80  48015926 5.609655e+06 4.783812e+07
## 81   81  46490568 5.070806e+06 4.990233e+07
## 82   82  45159173 4.544347e+06 5.176018e+07
## 83   83  44002748 4.042074e+06 5.341888e+07
## 84   84  43002284 3.571984e+06 5.488943e+07
## 85   85  42139536 3.138865e+06 5.618530e+07
## 86   86  41397489 2.744937e+06 5.732128e+07
## 87   87  40760605 2.390480e+06 5.831262e+07
## 88   88  40214921 2.074379e+06 5.917440e+07
## 89   89  39748029 1.794587e+06 5.992109e+07
## 90   90  39349007 1.548482e+06 6.056621e+07
## 
## $params
## $params$beta
##      beta 
## 0.6134295 
## 
## $params$gamma
##     gamma 
## 0.3865706 
## 
## $params$R0
##      R0 
## 1.58685