final_predicitve

library(readxl)
dengue <- read_excel("Documents/Data Analysis and econometrics/dengue.xlsx")
labels <- read_excel("Documents/Data Analysis and econometrics/labels.xlsx")
denguetest <- read_excel("Documents/Data Analysis and econometrics/denguetest.xlsx")
sub <- read_excel("Documents/Data Analysis and econometrics/sub.xlsx")

library(MASS)
library(forecast)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(fpp2)

## Loading required package: ggplot2

## Loading required package: fma

## 
## Attaching package: 'fma'

## The following objects are masked from 'package:MASS':
## 
##     cement, housing, petrol

## Loading required package: expsmooth

library(corrplot)

## corrplot 0.84 loaded

library(magrittr)
library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(RColorBrewer)
library(gridExtra)
library(MASS)
require(Amelia)

## Loading required package: Amelia

## Loading required package: Rcpp

## ## 
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.6, built: 2019-11-24)
## ## Copyright (C) 2005-2020 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##

library(Amelia)
library('broom') 
library('purrr')

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:magrittr':
## 
##     set_names

library('lubridate')

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library('timeDate') 
library('tseries') 
library('forecast') 
library('prophet')

## Loading required package: rlang

## 
## Attaching package: 'rlang'

## The following objects are masked from 'package:purrr':
## 
##     %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
##     flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
##     splice

## The following object is masked from 'package:magrittr':
## 
##     set_names

library('ggplot2') 
library('scales') 

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:lubridate':
## 
##     intersect, setdiff, union

## The following object is masked from 'package:gridExtra':
## 
##     combine

## The following object is masked from 'package:MASS':
## 
##     select

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:scales':
## 
##     alpha, rescale

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

summary(dengue)

##      city                year        weekofyear    week_start_date   
##  Length:1456        Min.   :1990   Min.   : 1.00   Length:1456       
##  Class :character   1st Qu.:1997   1st Qu.:13.75   Class :character  
##  Mode  :character   Median :2002   Median :26.50   Mode  :character  
##                     Mean   :2001   Mean   :26.50                     
##                     3rd Qu.:2005   3rd Qu.:39.25                     
##                     Max.   :2010   Max.   :53.00                     
##                                                                      
##     ndvi_ne            ndvi_nw            ndvi_se            ndvi_sw        
##  Min.   :-0.40625   Min.   :-0.45610   Min.   :-0.01553   Min.   :-0.06346  
##  1st Qu.: 0.04495   1st Qu.: 0.04922   1st Qu.: 0.15509   1st Qu.: 0.14421  
##  Median : 0.12882   Median : 0.12143   Median : 0.19605   Median : 0.18945  
##  Mean   : 0.14229   Mean   : 0.13055   Mean   : 0.20378   Mean   : 0.20231  
##  3rd Qu.: 0.24848   3rd Qu.: 0.21660   3rd Qu.: 0.24885   3rd Qu.: 0.24698  
##  Max.   : 0.50836   Max.   : 0.45443   Max.   : 0.53831   Max.   : 0.54602  
##  NA's   :194        NA's   :52         NA's   :22         NA's   :22        
##  precipitation_amt_mm reanalysis_air_temp_k reanalysis_avg_temp_k
##  Min.   :  0.00       Min.   :294.6         Min.   :294.9        
##  1st Qu.:  9.80       1st Qu.:297.7         1st Qu.:298.3        
##  Median : 38.34       Median :298.6         Median :299.3        
##  Mean   : 45.76       Mean   :298.7         Mean   :299.2        
##  3rd Qu.: 70.23       3rd Qu.:299.8         3rd Qu.:300.2        
##  Max.   :390.60       Max.   :302.2         Max.   :302.9        
##  NA's   :13           NA's   :10            NA's   :10           
##  reanalysis_dew_point_temp_k reanalysis_max_air_temp_k
##  Min.   :289.6               Min.   :297.8            
##  1st Qu.:294.1               1st Qu.:301.0            
##  Median :295.6               Median :302.4            
##  Mean   :295.2               Mean   :303.4            
##  3rd Qu.:296.5               3rd Qu.:305.5            
##  Max.   :298.4               Max.   :314.0            
##  NA's   :10                  NA's   :10               
##  reanalysis_min_air_temp_k reanalysis_precip_amt_kg_per_m2
##  Min.   :286.9             Min.   :  0.00                 
##  1st Qu.:293.9             1st Qu.: 13.05                 
##  Median :296.2             Median : 27.25                 
##  Mean   :295.7             Mean   : 40.15                 
##  3rd Qu.:297.9             3rd Qu.: 52.20                 
##  Max.   :299.9             Max.   :570.50                 
##  NA's   :10                NA's   :10                     
##  reanalysis_relative_humidity_percent reanalysis_sat_precip_amt_mm
##  Min.   :57.79                        Min.   :  0.00              
##  1st Qu.:77.18                        1st Qu.:  9.80              
##  Median :80.30                        Median : 38.34              
##  Mean   :82.16                        Mean   : 45.76              
##  3rd Qu.:86.36                        3rd Qu.: 70.23              
##  Max.   :98.61                        Max.   :390.60              
##  NA's   :10                           NA's   :13                  
##  reanalysis_specific_humidity_g_per_kg reanalysis_tdtr_k station_avg_temp_c
##  Min.   :11.72                         Min.   : 1.357    Min.   :21.40     
##  1st Qu.:15.56                         1st Qu.: 2.329    1st Qu.:26.30     
##  Median :17.09                         Median : 2.857    Median :27.41     
##  Mean   :16.75                         Mean   : 4.904    Mean   :27.19     
##  3rd Qu.:17.98                         3rd Qu.: 7.625    3rd Qu.:28.16     
##  Max.   :20.46                         Max.   :16.029    Max.   :30.80     
##  NA's   :10                            NA's   :10        NA's   :43        
##  station_diur_temp_rng_c station_max_temp_c station_min_temp_c
##  Min.   : 4.529          Min.   :26.70      Min.   :14.7      
##  1st Qu.: 6.514          1st Qu.:31.10      1st Qu.:21.1      
##  Median : 7.300          Median :32.80      Median :22.2      
##  Mean   : 8.059          Mean   :32.45      Mean   :22.1      
##  3rd Qu.: 9.567          3rd Qu.:33.90      3rd Qu.:23.3      
##  Max.   :15.800          Max.   :42.20      Max.   :25.6      
##  NA's   :43              NA's   :20         NA's   :14        
##  station_precip_mm
##  Min.   :  0.00   
##  1st Qu.:  8.70   
##  Median : 23.85   
##  Mean   : 39.33   
##  3rd Qu.: 53.90   
##  Max.   :543.30   
##  NA's   :22

describe(dengue)

## Warning in describe(dengue): NAs introduced by coercion

## Warning in describe(dengue): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf

## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf

## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

##                                       vars    n    mean    sd  median trimmed
## city*                                    1 1456     NaN    NA      NA     NaN
## year                                     2 1456 2001.03  5.41 2002.00 2001.30
## weekofyear                               3 1456   26.50 15.02   26.50   26.50
## week_start_date*                         4 1456     NaN    NA      NA     NaN
## ndvi_ne                                  5 1262    0.14  0.14    0.13    0.14
## ndvi_nw                                  6 1404    0.13  0.12    0.12    0.13
## ndvi_se                                  7 1434    0.20  0.07    0.20    0.20
## ndvi_sw                                  8 1434    0.20  0.08    0.19    0.20
## precipitation_amt_mm                     9 1443   45.76 43.72   38.34   40.16
## reanalysis_air_temp_k                   10 1446  298.70  1.36  298.65  298.72
## reanalysis_avg_temp_k                   11 1446  299.23  1.26  299.29  299.25
## reanalysis_dew_point_temp_k             12 1446  295.25  1.53  295.64  295.37
## reanalysis_max_air_temp_k               13 1446  303.43  3.23  302.40  303.10
## reanalysis_min_air_temp_k               14 1446  295.72  2.57  296.20  295.93
## reanalysis_precip_amt_kg_per_m2         15 1446   40.15 43.43   27.24   32.38
## reanalysis_relative_humidity_percent    16 1446   82.16  7.15   80.30   81.69
## reanalysis_sat_precip_amt_mm            17 1443   45.76 43.72   38.34   40.16
## reanalysis_specific_humidity_g_per_kg   18 1446   16.75  1.54   17.09   16.85
## reanalysis_tdtr_k                       19 1446    4.90  3.55    2.86    4.36
## station_avg_temp_c                      20 1413   27.19  1.29   27.41   27.27
## station_diur_temp_rng_c                 21 1413    8.06  2.13    7.30    7.85
## station_max_temp_c                      22 1436   32.45  1.96   32.80   32.52
## station_min_temp_c                      23 1442   22.10  1.57   22.20   22.14
## station_precip_mm                       24 1434   39.33 47.46   23.85   30.46
##                                         mad     min     max  range  skew
## city*                                    NA     Inf    -Inf   -Inf    NA
## year                                   5.93 1990.00 2010.00  20.00 -0.40
## weekofyear                            19.27    1.00   53.00  52.00  0.00
## week_start_date*                         NA     Inf    -Inf   -Inf    NA
## ndvi_ne                                0.15   -0.41    0.51   0.91 -0.11
## ndvi_nw                                0.12   -0.46    0.45   0.91 -0.01
## ndvi_se                                0.07   -0.02    0.54   0.55  0.57
## ndvi_sw                                0.07   -0.06    0.55   0.61  0.75
## precipitation_amt_mm                  44.23    0.00  390.60 390.60  1.73
## reanalysis_air_temp_k                  1.57  294.64  302.20   7.56 -0.08
## reanalysis_avg_temp_k                  1.43  294.89  302.93   8.04 -0.19
## reanalysis_dew_point_temp_k            1.52  289.64  298.45   8.81 -0.72
## reanalysis_max_air_temp_k              2.67  297.80  314.00  16.20  0.85
## reanalysis_min_air_temp_k              2.82  286.90  299.90  13.00 -0.67
## reanalysis_precip_amt_kg_per_m2       25.12    0.00  570.50 570.50  3.38
## reanalysis_relative_humidity_percent   5.68   57.79   98.61  40.82  0.57
## reanalysis_sat_precip_amt_mm          44.23    0.00  390.60 390.60  1.73
## reanalysis_specific_humidity_g_per_kg  1.63   11.72   20.46   8.75 -0.54
## reanalysis_tdtr_k                      1.16    1.36   16.03  14.67  1.07
## station_avg_temp_c                     1.28   21.40   30.80   9.40 -0.57
## station_diur_temp_rng_c                1.63    4.53   15.80  11.27  0.84
## station_max_temp_c                     1.63   26.70   42.20  15.50 -0.26
## station_min_temp_c                     1.63   14.70   25.60  10.90 -0.31
## station_precip_mm                     26.76    0.00  543.30 543.30  2.98
##                                       kurtosis   se
## city*                                       NA   NA
## year                                     -0.89 0.14
## weekofyear                               -1.20 0.39
## week_start_date*                            NA   NA
## ndvi_ne                                  -0.14 0.00
## ndvi_nw                                   0.05 0.00
## ndvi_se                                   0.56 0.00
## ndvi_sw                                   0.70 0.00
## precipitation_amt_mm                      6.74 1.15
## reanalysis_air_temp_k                    -0.69 0.04
## reanalysis_avg_temp_k                    -0.54 0.03
## reanalysis_dew_point_temp_k              -0.12 0.04
## reanalysis_max_air_temp_k                -0.19 0.09
## reanalysis_min_air_temp_k                -0.22 0.07
## reanalysis_precip_amt_kg_per_m2          22.12 1.14
## reanalysis_relative_humidity_percent     -0.40 0.19
## reanalysis_sat_precip_amt_mm              6.74 1.15
## reanalysis_specific_humidity_g_per_kg    -0.49 0.04
## reanalysis_tdtr_k                        -0.21 0.09
## station_avg_temp_c                       -0.16 0.03
## station_diur_temp_rng_c                  -0.26 0.06
## station_max_temp_c                        0.21 0.05
## station_min_temp_c                        0.21 0.04
## station_precip_mm                        15.29 1.25

summary(labels)

##      city                year        weekofyear     total_cases    
##  Length:1456        Min.   :1990   Min.   : 1.00   Min.   :  0.00  
##  Class :character   1st Qu.:1997   1st Qu.:13.75   1st Qu.:  5.00  
##  Mode  :character   Median :2002   Median :26.50   Median : 12.00  
##                     Mean   :2001   Mean   :26.50   Mean   : 24.68  
##                     3rd Qu.:2005   3rd Qu.:39.25   3rd Qu.: 28.00  
##                     Max.   :2010   Max.   :53.00   Max.   :461.00

describe(labels)

## Warning in describe(labels): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf

## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

##             vars    n    mean    sd median trimmed   mad  min  max range  skew
## city*          1 1456     NaN    NA     NA     NaN    NA  Inf -Inf  -Inf    NA
## year           2 1456 2001.03  5.41 2002.0 2001.30  5.93 1990 2010    20 -0.40
## weekofyear     3 1456   26.50 15.02   26.5   26.50 19.27    1   53    52  0.00
## total_cases    4 1456   24.68 43.60   12.0   15.97 13.34    0  461   461  5.26
##             kurtosis   se
## city*             NA   NA
## year           -0.89 0.14
## weekofyear     -1.20 0.39
## total_cases    36.33 1.14

trainiq = dengue[dengue$city == 'iq',]
trainsj = dengue[dengue$city == 'sj',]
labelsiq = labels[labels$city == 'iq',]
labelssj = labels[labels$city == 'sj',]

sjtrain<-cbind(trainsj,labelssj)
iqtrain<-cbind(trainiq,labelsiq)

plot(sjtrain$week_start_date,sjtrain$total_cases,xlim=c(0,936))

## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

trainsj<-trainsj%>%mutate(week_start_date=ymd(week_start_date))
train.sj<-cbind(trainsj,labelssj)
train.iq<-trainiq%>%mutate(week_start_date=ymd(week_start_date))
train.iq<-cbind(trainiq,labelsiq)

plot(train.sj$week_start_date,train.sj$total_cases, type = 'l',xlim=c(0,936))

plot(train.iq$week_start_date,train.iq$total_cases, type = 'l',xlim=c(0,520))

## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

train.sj$city <- NULL
train.iq$city <- NULL
mycorSJdata = train.iq
mycorSJdata$week_start_date <- NULL 
mycorIQdata = train.iq
mycorIQdata$week_start_date <- NULL
mycorSJdata$city<-NULL
mycorIQdata$city<-NULL
mycorSJ <- cor(mycorSJdata)
mycorIQ <- cor(mycorIQdata)
corrplot(mycorSJ)
corrplot(mycorIQ)

sj.ts = ts(train.sj$total_cases, frequency = 52)
plot(sj.ts)

iq.ts = ts(train.iq$total_cases, frequency = 52)
plot(iq.ts)

dec.sj.ts<-decompose(sj.ts,type="additive")
plot(dec.sj.ts)

dec.iq.ts<-decompose(iq.ts,type="additive")
plot(dec.iq.ts)

Slight upward trend in IQ, slight downward trend in SJ. More obvious yearly seasonality in SJ than IQ.

ggseasonplot(sj.ts)

ggseasonplot(iq.ts)

arima.sj=auto.arima(sj.ts)
arima.sj.fc=forecast(arima.sj,h=260)
autoplot(arima.sj.fc)

checkresiduals(arima.sj.fc)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,1)
## Q* = 95.049, df = 102, p-value = 0.6741
## 
## Model df: 2.   Total lags used: 104

summary(arima.sj.fc)

## 
## Forecast method: ARIMA(1,1,1)
## 
## Model Information:
## Series: sj.ts 
## ARIMA(1,1,1) 
## 
## Coefficients:
##          ar1      ma1
##       0.7116  -0.5929
## s.e.  0.0948   0.1078
## 
## sigma^2 estimated as 180.9:  log likelihood=-3755.85
## AIC=7517.71   AICc=7517.73   BIC=7532.23
## 
## Error measures:
##                       ME     RMSE      MAE MPE MAPE      MASE        ACF1
## Training set 0.001467535 13.42959 8.047587 NaN  Inf 0.2202839 0.001092614
## 
## Forecasts:
##          Point Forecast      Lo 80     Hi 80      Lo 95     Hi 95
## 19.00000       5.281867  -11.95649  22.52023  -21.08193  31.64567
## 19.01923       5.482448  -20.38332  31.34821  -34.07583  45.04072
## 19.03846       5.625183  -27.52871  38.77907  -45.07931  56.32968
## 19.05769       5.726756  -33.93894  45.39245  -54.93668  66.39020
## 19.07692       5.799036  -39.81021  51.40828  -63.95428  75.55236
## 19.09615       5.850472  -45.24684  56.94778  -72.29612  83.99706
## 19.11538       5.887074  -50.31797  62.09212  -80.07112  91.84527
## 19.13462       5.913120  -55.07530  66.90154  -87.36062  99.18686
## 19.15385       5.931655  -59.55999  71.42330  -94.22917 106.09248
## 19.17308       5.944845  -63.80587  75.69556 -100.72967 112.61936
## 19.19231       5.954231  -67.84125  79.74971 -106.90622 118.81468
## 19.21154       5.960911  -71.69005  83.61187 -112.79599 124.71781
## 19.23077       5.965664  -75.37262  87.30395 -118.43050 130.36183
## 19.25000       5.969046  -78.90634  90.84443 -123.83666 135.77475
## 19.26923       5.971453  -82.30617  94.24907 -129.03752 140.98042
## 19.28846       5.973166  -85.58498  97.53131 -134.05293 145.99926
## 19.30769       5.974384  -88.75392 100.70269 -138.90006 150.84883
## 19.32692       5.975252  -91.82271 103.77321 -143.59382 155.54433
## 19.34615       5.975869  -94.79980 106.75154 -148.14722 160.09896
## 19.36538       5.976308  -97.69263 109.64525 -152.57165 164.52427
## 19.38462       5.976621 -100.50773 112.46097 -156.87714 168.83038
## 19.40385       5.976843 -103.25088 115.20457 -161.07255 173.02623
## 19.42308       5.977001 -105.92721 117.88121 -165.16572 177.11972
## 19.44231       5.977114 -108.54129 120.49552 -169.16367 181.11790
## 19.46154       5.977194 -111.09722 123.05161 -173.07267 185.02706
## 19.48077       5.977251 -113.59866 125.55316 -176.89833 188.85283
## 19.50000       5.977292 -116.04893 128.00352 -180.64572 192.60030
## 19.51923       5.977321 -118.45103 130.40567 -184.31943 196.27407
## 19.53846       5.977341 -120.80768 132.76236 -187.92361 199.87829
## 19.55769       5.977356 -123.12134 135.07605 -191.46206 203.41678
## 19.57692       5.977366 -125.39429 137.34902 -194.93824 206.89298
## 19.59615       5.977374 -127.62859 139.58334 -198.35531 210.31006
## 19.61538       5.977379 -129.82615 141.78091 -201.71619 213.67095
## 19.63462       5.977383 -131.98871 143.94348 -205.02355 216.97832
## 19.65385       5.977385 -134.11791 146.07268 -208.27988 220.23465
## 19.67308       5.977387 -136.21523 148.17000 -211.48745 223.44223
## 19.69231       5.977389 -138.28206 150.23684 -214.64840 226.60318
## 19.71154       5.977390 -140.31970 152.27448 -217.76470 229.71948
## 19.73077       5.977390 -142.32935 154.28413 -220.83819 232.79297
## 19.75000       5.977391 -144.31213 156.26691 -223.87059 235.82537
## 19.76923       5.977391 -146.26908 158.22387 -226.86350 238.81828
## 19.78846       5.977391 -148.20120 160.15599 -229.81842 241.77321
## 19.80769       5.977391 -150.10941 162.06419 -232.73677 244.69156
## 19.82692       5.977392 -151.99457 163.94935 -235.61987 247.57466
## 19.84615       5.977392 -153.85750 165.81228 -238.46897 250.42376
## 19.86538       5.977392 -155.69896 167.65374 -241.28525 253.24003
## 19.88462       5.977392 -157.51968 169.47446 -244.06980 256.02459
## 19.90385       5.977392 -159.32035 171.27513 -246.82369 258.77847
## 19.92308       5.977392 -161.10161 173.05640 -249.54790 261.50268
## 19.94231       5.977392 -162.86409 174.81887 -252.24337 264.19815
## 19.96154       5.977392 -164.60835 176.56313 -254.91099 266.86577
## 19.98077       5.977392 -166.33496 178.28974 -257.55161 269.50639
## 20.00000       5.977392 -168.04444 179.99922 -260.16603 272.12081
## 20.01923       5.977392 -169.73729 181.69207 -262.75502 274.70980
## 20.03846       5.977392 -171.41398 183.36876 -265.31930 277.27408
## 20.05769       5.977392 -173.07497 185.02976 -267.85957 279.81435
## 20.07692       5.977392 -174.72070 186.67548 -270.37649 282.33127
## 20.09615       5.977392 -176.35157 188.30636 -272.87069 284.82548
## 20.11538       5.977392 -177.96798 189.92277 -275.34279 287.29757
## 20.13462       5.977392 -179.57032 191.52510 -277.79334 289.74813
## 20.15385       5.977392 -181.15893 193.11371 -280.22292 292.17770
## 20.17308       5.977392 -182.73417 194.68895 -282.63204 294.58682
## 20.19231       5.977392 -184.29637 196.25115 -285.02122 296.97600
## 20.21154       5.977392 -185.84585 197.80063 -287.39094 299.34572
## 20.23077       5.977392 -187.38291 199.33769 -289.74167 301.69645
## 20.25000       5.977392 -188.90785 200.86263 -292.07386 304.02865
## 20.26923       5.977392 -190.42095 202.37573 -294.38795 306.34273
## 20.28846       5.977392 -191.92248 203.87726 -296.68434 308.63912
## 20.30769       5.977392 -193.41270 205.36748 -298.96344 310.91822
## 20.32692       5.977392 -194.89187 206.84665 -301.22563 313.18042
## 20.34615       5.977392 -196.36022 208.31501 -303.47129 315.42607
## 20.36538       5.977392 -197.81800 209.77278 -305.70076 317.65555
## 20.38462       5.977392 -199.26542 211.22020 -307.91440 319.86919
## 20.40385       5.977392 -200.70271 212.65749 -310.11254 322.06733
## 20.42308       5.977392 -202.13007 214.08485 -312.29550 324.25028
## 20.44231       5.977392 -203.54770 215.50248 -314.46359 326.41837
## 20.46154       5.977392 -204.95581 216.91059 -316.61710 328.57188
## 20.48077       5.977392 -206.35458 218.30936 -318.75634 330.71112
## 20.50000       5.977392 -207.74420 219.69898 -320.88157 332.83635
## 20.51923       5.977392 -209.12483 221.07962 -322.99307 334.94786
## 20.53846       5.977392 -210.49667 222.45145 -325.09111 337.04590
## 20.55769       5.977392 -211.85986 223.81465 -327.17594 339.13072
## 20.57692       5.977392 -213.21458 225.16936 -329.24780 341.20258
## 20.59615       5.977392 -214.56097 226.51576 -331.30693 343.26172
## 20.61538       5.977392 -215.89920 227.85398 -333.35357 345.30835
## 20.63462       5.977392 -217.22940 229.18418 -335.38794 347.34272
## 20.65385       5.977392 -218.55172 230.50651 -337.41025 349.36504
## 20.67308       5.977392 -219.86630 231.82108 -339.42073 351.37551
## 20.69231       5.977392 -221.17327 233.12806 -341.41957 353.37435
## 20.71154       5.977392 -222.47277 234.42755 -343.40698 355.36176
## 20.73077       5.977392 -223.76491 235.71970 -345.38314 357.33792
## 20.75000       5.977392 -225.04983 237.00461 -347.34825 359.30304
## 20.76923       5.977392 -226.32764 238.28242 -349.30249 361.25728
## 20.78846       5.977392 -227.59846 239.55324 -351.24605 363.20083
## 20.80769       5.977392 -228.86240 240.81719 -353.17908 365.13386
## 20.82692       5.977392 -230.11958 242.07436 -355.10177 367.05655
## 20.84615       5.977392 -231.37010 243.32488 -357.01427 368.96905
## 20.86538       5.977392 -232.61406 244.56884 -358.91675 370.87153
## 20.88462       5.977392 -233.85157 245.80636 -360.80936 372.76414
## 20.90385       5.977392 -235.08273 247.03751 -362.69225 374.64704
## 20.92308       5.977392 -236.30763 248.26242 -364.56558 376.52036
## 20.94231       5.977392 -237.52637 249.48116 -366.42948 378.38427
## 20.96154       5.977392 -238.73905 250.69383 -368.28410 380.23889
## 20.98077       5.977392 -239.94574 251.90052 -370.12958 382.08436
## 21.00000       5.977392 -241.14654 253.10132 -371.96604 383.92083
## 21.01923       5.977392 -242.34153 254.29631 -373.79363 385.74841
## 21.03846       5.977392 -243.53080 255.48558 -375.61246 387.56724
## 21.05769       5.977392 -244.71443 256.66921 -377.42266 389.37745
## 21.07692       5.977392 -245.89249 257.84728 -379.22436 391.17914
## 21.09615       5.977392 -247.06508 259.01986 -381.01767 392.97245
## 21.11538       5.977392 -248.23225 260.18703 -382.80270 394.75749
## 21.13462       5.977392 -249.39409 261.34887 -384.57958 396.53436
## 21.15385       5.977392 -250.55066 262.50545 -386.34841 398.30320
## 21.17308       5.977392 -251.70205 263.65683 -388.10930 400.06409
## 21.19231       5.977392 -252.84831 264.80309 -389.86236 401.81714
## 21.21154       5.977392 -253.98952 265.94430 -391.60769 403.56247
## 21.23077       5.977392 -255.12574 267.08052 -393.34539 405.30017
## 21.25000       5.977392 -256.25704 268.21182 -395.07556 407.03034
## 21.26923       5.977392 -257.38348 269.33826 -396.79830 408.75308
## 21.28846       5.977392 -258.50512 270.45990 -398.51370 410.46848
## 21.30769       5.977392 -259.62202 271.57681 -400.22186 412.17664
## 21.32692       5.977392 -260.73425 272.68903 -401.92286 413.87765
## 21.34615       5.977392 -261.84186 273.79664 -403.61680 415.57159
## 21.36538       5.977392 -262.94490 274.89969 -405.30377 417.25855
## 21.38462       5.977392 -264.04344 275.99823 -406.98384 418.93862
## 21.40385       5.977392 -265.13753 277.09232 -408.65710 420.61189
## 21.42308       5.977392 -266.22722 278.18201 -410.32364 422.27843
## 21.44231       5.977392 -267.31257 279.26736 -411.98354 423.93832
## 21.46154       5.977392 -268.39362 280.34841 -413.63687 425.59165
## 21.48077       5.977392 -269.47044 281.42522 -415.28371 427.23849
## 21.50000       5.977392 -270.54305 282.49784 -416.92413 428.87892
## 21.51923       5.977392 -271.61153 283.56631 -418.55822 430.51301
## 21.53846       5.977392 -272.67590 284.63069 -420.18604 432.14083
## 21.55769       5.977392 -273.73623 285.69101 -421.80767 433.76246
## 21.57692       5.977392 -274.79255 286.74733 -423.42318 435.37796
## 21.59615       5.977392 -275.84491 287.79969 -425.03263 436.98741
## 21.61538       5.977392 -276.89336 288.84814 -426.63609 438.59087
## 21.63462       5.977392 -277.93793 289.89272 -428.23363 440.18841
## 21.65385       5.977392 -278.97868 290.93346 -429.82531 441.78009
## 21.67308       5.977392 -280.01564 291.97042 -431.41120 443.36599
## 21.69231       5.977392 -281.04885 293.00363 -432.99137 444.94615
## 21.71154       5.977392 -282.07836 294.03314 -434.56586 446.52064
## 21.73077       5.977392 -283.10420 295.05898 -436.13475 448.08953
## 21.75000       5.977392 -284.12641 296.08119 -437.69809 449.65287
## 21.76923       5.977392 -285.14503 297.09982 -439.25594 451.21072
## 21.78846       5.977392 -286.16011 298.11489 -440.80836 452.76314
## 21.80769       5.977392 -287.17166 299.12645 -442.35540 454.31018
## 21.82692       5.977392 -288.17974 300.13453 -443.89712 455.85191
## 21.84615       5.977392 -289.18438 301.13916 -445.43358 457.38836
## 21.86538       5.977392 -290.18560 302.14039 -446.96483 458.91961
## 21.88462       5.977392 -291.18346 303.13824 -448.49091 460.44570
## 21.90385       5.977392 -292.17797 304.13276 -450.01189 461.96667
## 21.92308       5.977392 -293.16918 305.12397 -451.52781 463.48260
## 21.94231       5.977392 -294.15712 306.11190 -453.03873 464.99351
## 21.96154       5.977392 -295.14181 307.09659 -454.54469 466.49947
## 21.98077       5.977392 -296.12330 308.07808 -456.04574 468.00052
## 22.00000       5.977392 -297.10160 309.05638 -457.54193 469.49671
## 22.01923       5.977392 -298.07676 310.03154 -459.03330 470.98809
## 22.03846       5.977392 -299.04880 311.00358 -460.51991 472.47470
## 22.05769       5.977392 -300.01775 311.97254 -462.00180 473.95658
## 22.07692       5.977392 -300.98365 312.93843 -463.47900 475.43379
## 22.09615       5.977392 -301.94651 313.90129 -464.95158 476.90636
## 22.11538       5.977392 -302.90637 314.86116 -466.41956 478.37435
## 22.13462       5.977392 -303.86326 315.81805 -467.88300 479.83778
## 22.15385       5.977392 -304.81721 316.77199 -469.34193 481.29671
## 22.17308       5.977392 -305.76823 317.72301 -470.79639 482.75118
## 22.19231       5.977392 -306.71636 318.67115 -472.24644 484.20122
## 22.21154       5.977392 -307.66163 319.61641 -473.69209 485.64688
## 22.23077       5.977392 -308.60405 320.55884 -475.13341 487.08819
## 22.25000       5.977392 -309.54366 321.49845 -476.57042 488.52520
## 22.26923       5.977392 -310.48048 322.43527 -478.00316 489.95795
## 22.28846       5.977392 -311.41454 323.36932 -479.43168 491.38646
## 22.30769       5.977392 -312.34585 324.30064 -480.85600 492.81078
## 22.32692       5.977392 -313.27445 325.22923 -482.27617 494.23095
## 22.34615       5.977392 -314.20036 326.15514 -483.69222 495.64700
## 22.36538       5.977392 -315.12359 327.07837 -485.10418 497.05897
## 22.38462       5.977392 -316.04418 327.99896 -486.51210 498.46688
## 22.40385       5.977392 -316.96214 328.91692 -487.91600 499.87079
## 22.42308       5.977392 -317.87750 329.83229 -489.31593 501.27071
## 22.44231       5.977392 -318.79028 330.74507 -490.71191 502.66669
## 22.46154       5.977392 -319.70051 331.65529 -492.10397 504.05876
## 22.48077       5.977392 -320.60819 332.56298 -493.49216 505.44694
## 22.50000       5.977392 -321.51336 333.46815 -494.87650 506.83128
## 22.51923       5.977392 -322.41604 334.37082 -496.25702 508.21181
## 22.53846       5.977392 -323.31624 335.27103 -497.63376 509.58855
## 22.55769       5.977392 -324.21399 336.16877 -499.00675 510.96153
## 22.57692       5.977392 -325.10930 337.06409 -500.37601 512.33079
## 22.59615       5.977392 -326.00220 337.95698 -501.74158 513.69636
## 22.61538       5.977392 -326.89270 338.84749 -503.10349 515.05827
## 22.63462       5.977392 -327.78083 339.73561 -504.46176 516.41654
## 22.65385       5.977392 -328.66660 340.62138 -505.81643 517.77121
## 22.67308       5.977392 -329.55003 341.50482 -507.16752 519.12231
## 22.69231       5.977392 -330.43114 342.38593 -508.51507 520.46985
## 22.71154       5.977392 -331.30995 343.26474 -509.85909 521.81387
## 22.73077       5.977392 -332.18648 344.14126 -511.19962 523.15441
## 22.75000       5.977392 -333.06074 345.01552 -512.53669 524.49147
## 22.76923       5.977392 -333.93275 345.88754 -513.87031 525.82510
## 22.78846       5.977392 -334.80253 346.75732 -515.20053 527.15531
## 22.80769       5.977392 -335.67010 347.62488 -516.52736 528.48214
## 22.82692       5.977392 -336.53547 348.49025 -517.85082 529.80561
## 22.84615       5.977392 -337.39865 349.35344 -519.17095 531.12574
## 22.86538       5.977392 -338.25968 350.21446 -520.48777 532.44256
## 22.88462       5.977392 -339.11855 351.07334 -521.80131 533.75609
## 22.90385       5.977392 -339.97530 351.93008 -523.11159 535.06637
## 22.92308       5.977392 -340.82992 352.78470 -524.41862 536.37341
## 22.94231       5.977392 -341.68245 353.63723 -525.72245 537.67723
## 22.96154       5.977392 -342.53289 354.48767 -527.02308 538.97787
## 22.98077       5.977392 -343.38126 355.33604 -528.32055 540.27534
## 23.00000       5.977392 -344.22757 356.18235 -529.61488 541.56966
## 23.01923       5.977392 -345.07184 357.02663 -530.90608 542.86087
## 23.03846       5.977392 -345.91409 357.86888 -532.19419 544.14898
## 23.05769       5.977392 -346.75433 358.70911 -533.47922 545.43401
## 23.07692       5.977392 -347.59257 359.54735 -534.76120 546.71599
## 23.09615       5.977392 -348.42883 360.38361 -536.04015 547.99493
## 23.11538       5.977392 -349.26312 361.21790 -537.31608 549.27087
## 23.13462       5.977392 -350.09545 362.05024 -538.58903 550.54381
## 23.15385       5.977392 -350.92585 362.88063 -539.85901 551.81379
## 23.17308       5.977392 -351.75431 363.70910 -541.12604 553.08082
## 23.19231       5.977392 -352.58086 364.53565 -542.39014 554.34492
## 23.21154       5.977392 -353.40552 365.36030 -543.65133 555.60612
## 23.23077       5.977392 -354.22828 366.18306 -544.90964 556.86443
## 23.25000       5.977392 -355.04917 367.00395 -546.16508 558.11986
## 23.26923       5.977392 -355.86819 367.82298 -547.41767 559.37246
## 23.28846       5.977392 -356.68537 368.64015 -548.66744 560.62222
## 23.30769       5.977392 -357.50071 369.45549 -549.91439 561.86917
## 23.32692       5.977392 -358.31422 370.26900 -551.15855 563.11333
## 23.34615       5.977392 -359.12592 371.08070 -552.39994 564.35472
## 23.36538       5.977392 -359.93582 371.89061 -553.63858 565.59336
## 23.38462       5.977392 -360.74393 372.69872 -554.87448 566.82926
## 23.40385       5.977392 -361.55027 373.50505 -556.10766 568.06244
## 23.42308       5.977392 -362.35484 374.30962 -557.33814 569.29293
## 23.44231       5.977392 -363.15766 375.11244 -558.56595 570.52073
## 23.46154       5.977392 -363.95873 375.91351 -559.79108 571.74587
## 23.48077       5.977392 -364.75807 376.71286 -561.01357 572.96836
## 23.50000       5.977392 -365.55570 377.51048 -562.23343 574.18822
## 23.51923       5.977392 -366.35161 378.30639 -563.45068 575.40546
## 23.53846       5.977392 -367.14583 379.10061 -564.66533 576.62011
## 23.55769       5.977392 -367.93836 379.89314 -565.87740 577.83218
## 23.57692       5.977392 -368.72921 380.68399 -567.08690 579.04169
## 23.59615       5.977392 -369.51840 381.47318 -568.29386 580.24865
## 23.61538       5.977392 -370.30593 382.26072 -569.49829 581.45307
## 23.63462       5.977392 -371.09182 383.04660 -570.70020 582.65499
## 23.65385       5.977392 -371.87607 383.83086 -571.89962 583.85440
## 23.67308       5.977392 -372.65870 384.61349 -573.09654 585.05133
## 23.69231       5.977392 -373.43972 385.39450 -574.29100 586.24579
## 23.71154       5.977392 -374.21913 386.17391 -575.48301 587.43779
## 23.73077       5.977392 -374.99694 386.95173 -576.67257 588.62736
## 23.75000       5.977392 -375.77317 387.72796 -577.85972 589.81450
## 23.76923       5.977392 -376.54783 388.50261 -579.04445 590.99924
## 23.78846       5.977392 -377.32092 389.27570 -580.22679 592.18157
## 23.80769       5.977392 -378.09246 390.04724 -581.40675 593.36153
## 23.82692       5.977392 -378.86244 390.81723 -582.58434 594.53913
## 23.84615       5.977392 -379.63089 391.58568 -583.75959 595.71437
## 23.86538       5.977392 -380.39781 392.35260 -584.93249 596.88728
## 23.88462       5.977392 -381.16322 393.11800 -586.10307 598.05786
## 23.90385       5.977392 -381.92711 393.88189 -587.27135 599.22613
## 23.92308       5.977392 -382.68950 394.64428 -588.43732 600.39211
## 23.94231       5.977392 -383.45040 395.40518 -589.60102 601.55580
## 23.96154       5.977392 -384.20981 396.16459 -590.76244 602.71722
## 23.98077       5.977392 -384.96775 396.92253 -591.92161 603.87639

arima.iq=auto.arima(iq.ts)
arima.iq.fc=forecast(arima.iq,h=156)
autoplot(arima.iq.fc)

checkresiduals(arima.iq.fc)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(2,1,1)(0,0,2)[52]
## Q* = 66.923, df = 99, p-value = 0.9944
## 
## Model df: 5.   Total lags used: 104

summary(arima.iq.fc)

## 
## Forecast method: ARIMA(2,1,1)(0,0,2)[52]
## 
## Model Information:
## Series: iq.ts 
## ARIMA(2,1,1)(0,0,2)[52] 
## 
## Coefficients:
##           ar1      ar2      ma1    sma1    sma2
##       -0.0275  -0.2503  -0.2229  0.0499  0.1531
## s.e.   0.1422   0.0538   0.1469  0.0417  0.0446
## 
## sigma^2 estimated as 50.67:  log likelihood=-1753.96
## AIC=3519.91   AICc=3520.08   BIC=3545.42
## 
## Error measures:
##                       ME     RMSE      MAE MPE MAPE      MASE         ACF1
## Training set 0.009221677 7.077434 3.853981 NaN  Inf 0.4082533 -0.001646214
## 
## Forecasts:
##          Point Forecast      Lo 80    Hi 80      Lo 95     Hi 95
## 11.00000       3.524880  -5.598001 12.64776  -10.42736  17.47712
## 11.01923       3.224337  -8.177277 14.62595  -14.21293  20.66160
## 11.03846       3.250840  -9.050647 15.55233  -15.56266  22.06434
## 11.05769       3.043892 -10.331443 16.41923  -17.41192  23.49970
## 11.07692       3.813817 -10.760678 18.38831  -18.47595  26.10358
## 11.09615       3.440201 -12.178953 19.05935  -20.44723  27.32764
## 11.11538       3.918348 -12.636320 20.47302  -21.39983  29.23653
## 11.13462       3.951521 -13.503357 21.40640  -22.74341  30.64645
## 11.15385       3.879697 -14.440652 22.20005  -24.13886  31.89825
## 11.17308       4.344389 -14.798694 23.48747  -24.93243  33.62121
## 11.19231       5.399801 -14.530005 25.32961  -25.08021  35.87981
## 11.21154       5.799911 -14.887620 26.48744  -25.83894  37.43876
## 11.23077       6.684705 -14.734222 28.10363  -26.07272  39.44213
## 11.25000       9.674077 -12.451859 31.80001  -24.16462  43.51278
## 11.26923       7.954936 -14.856004 30.76588  -26.93139  42.84126
## 11.28846      12.303409 -11.172612 35.77943  -23.60007  48.20689
## 11.30769       9.620365 -14.502429 33.74316  -27.27227  46.51300
## 11.32692      10.307256 -14.445404 35.05992  -27.54867  48.16318
## 11.34615       8.158274 -17.208612 33.52516  -30.63703  46.95358
## 11.36538       5.832629 -20.133962 31.79922  -33.87984  45.54510
## 11.38462       5.414853 -21.137901 31.96761  -35.19408  46.02379
## 11.40385       4.874630 -22.251625 32.00088  -36.61140  46.36066
## 11.42308       4.333040 -23.354838 32.02092  -38.01191  46.67800
## 11.44231       5.350436 -22.887898 33.58877  -37.83637  48.53724
## 11.46154       3.321026 -25.457236 32.09929  -40.69153  47.33358
## 11.48077       2.874033 -26.434213 32.18228  -41.94906  47.69713
## 11.50000       2.725742 -27.103073 32.55456  -42.89349  48.34498
## 11.51923       3.892464 -26.447990 34.23292  -42.50926  50.29418
## 11.53846       4.624385 -26.219221 35.46799  -42.54684  51.79561
## 11.55769       7.439553 -23.899128 38.77823  -40.48882  55.36793
## 11.57692       8.509613 -23.316443 40.33567  -40.16414  57.18337
## 11.59615       7.361072 -24.945007 39.66715  -42.04681  56.76896
## 11.61538       6.174725 -26.604349 38.95380  -43.95654  56.30599
## 11.63462       6.624544 -26.620795 39.86988  -44.21981  57.46890
## 11.65385       5.009213 -28.695942 38.71437  -46.53837  56.55680
## 11.67308       4.635467 -29.523315 38.79425  -47.60588  56.87682
## 11.69231       5.020754 -29.585709 39.62722  -47.90526  57.94677
## 11.71154       4.969150 -30.079277 40.01758  -48.63279  58.57109
## 11.73077       4.009019 -31.475866 39.49391  -50.26043  58.27847
## 11.75000       4.722760 -31.193282 40.63880  -50.20609  59.65161
## 11.76923       3.697640 -32.644442 40.03972  -51.88278  59.27806
## 11.78846       3.943444 -32.819742 40.70663  -52.28100  60.16789
## 11.80769       3.940194 -33.239327 41.11971  -52.92098  60.80136
## 11.82692       3.707473 -33.883771 41.29872  -53.78337  61.19832
## 11.84615       3.630898 -34.367609 41.62941  -54.48280  61.74460
## 11.86538       3.257732 -35.143720 41.65918  -55.47222  61.98768
## 11.88462       3.152182 -35.648029 41.95239  -56.18762  62.49198
## 11.90385       3.356439 -35.838476 42.55135  -56.58701  63.29989
## 11.92308       3.736588 -35.849094 43.32227  -56.80449  64.27766
## 11.94231       3.431542 -36.541088 43.40417  -57.70132  64.56440
## 11.96154       3.745748 -36.610119 44.10162  -57.97323  65.46472
## 11.98077       3.411661 -37.323839 44.14716  -58.88791  65.71123
## 12.00000       3.237016 -37.938509 44.41254  -59.73552  66.20955
## 12.01923       3.319572 -38.275037 44.91418  -60.29389  66.93304
## 12.03846       3.069148 -38.924976 45.06327  -61.15532  67.29362
## 12.05769       2.941552 -39.452637 45.33574  -61.89476  67.77787
## 12.07692       3.441996 -39.352172 46.23616  -62.00604  68.89003
## 12.09615       3.812550 -39.376727 47.00183  -62.23975  69.86485
## 12.11538       3.812563 -39.767374 47.39250  -62.83720  70.46233
## 12.13462       4.000280 -39.967156 47.96772  -63.24211  71.24267
## 12.15385       3.823840 -40.527914 48.17559  -64.00631  71.65400
## 12.17308       3.208289 -41.524400 47.94098  -65.20446  71.62103
## 12.19231       3.139173 -41.971187 48.24953  -65.85117  72.12951
## 12.21154       3.161498 -42.323417 48.64641  -66.40168  72.72467
## 12.23077       2.875866 -42.980557 48.73229  -67.25548  73.00721
## 12.25000       2.949800 -43.275139 49.17474  -67.74514  73.64474
## 12.26923       2.768319 -43.822219 49.35886  -68.48576  74.02240
## 12.28846       2.670052 -44.283240 49.62334  -69.13881  74.47892
## 12.30769       3.023710 -44.289555 50.33698  -69.33569  75.38310
## 12.32692       2.805883 -44.864637 50.47640  -70.09989  75.71165
## 12.34615       3.071695 -44.953422 51.09681  -70.37638  76.51977
## 12.36538       4.258814 -44.118301 52.63593  -69.72760  78.24523
## 12.38462       3.726538 -45.000032 52.45311  -70.79432  78.24740
## 12.40385       3.595437 -45.478101 52.66897  -71.45606  78.64694
## 12.42308       3.120558 -46.297510 52.53863  -72.45786  78.69897
## 12.44231       2.946511 -46.813703 52.70672  -73.15517  79.04819
## 12.46154       2.625973 -47.474050 52.72600  -73.99540  79.24735
## 12.48077       2.971157 -47.466385 53.40870  -74.16641  80.10872
## 12.50000       2.458595 -48.314223 53.23141  -75.19173  80.10892
## 12.51923       3.341512 -47.764383 54.44741  -74.81821  81.50123
## 12.53846       3.887662 -47.549153 55.32448  -74.77816  82.55348
## 12.55769       4.669838 -47.095780 56.43546  -74.49884  83.83852
## 12.57692       4.591060 -47.501287 56.68341  -75.07731  84.25943
## 12.59615       3.870849 -48.546191 56.28789  -76.29410  84.03579
## 12.61538       4.418965 -48.320769 57.15870  -76.23950  85.07743
## 12.63462       5.282718 -47.777747 58.34318  -75.86626  86.43170
## 12.65385       4.856096 -48.523173 58.23536  -76.78045  86.49264
## 12.67308       4.439014 -49.257165 58.13519  -77.68221  86.56023
## 12.69231       4.692161 -49.319070 58.70339  -77.91089  87.29521
## 12.71154       5.466838 -48.857618 59.79129  -77.61525  88.54892
## 12.73077       4.363088 -50.272797 58.99897  -79.19529  87.92146
## 12.75000       3.569024 -51.376524 58.51457  -80.46294  87.60099
## 12.76923       4.437850 -50.815627 59.69133  -80.06505  88.94075
## 12.78846       4.007605 -51.552093 59.56730  -80.96362  88.97883
## 12.80769       4.261981 -51.602261 60.12622  -81.17500  89.69897
## 12.82692       3.694898 -52.472236 59.86203  -82.20532  89.59512
## 12.84615       3.394041 -53.074359 59.86244  -82.96693  89.75501
## 12.86538       4.211581 -52.556488 60.97965  -82.60769  91.03085
## 12.88462       4.016234 -53.049930 61.08240  -83.25893  91.29140
## 12.90385       3.874493 -53.488217 61.23720  -83.85420  91.60319
## 12.92308       4.312733 -53.344997 61.97046  -83.86716  92.49262
## 12.94231       3.253870 -54.697379 61.20512  -85.37492  91.88266
## 12.96154       3.226903 -55.016386 61.47019  -85.84852  92.30233
## 12.98077       3.689447 -54.844424 62.22332  -85.83038  93.20928
## 13.00000       3.647980 -55.329685 62.62564  -86.55057  93.84653
## 13.01923       3.533359 -55.843310 62.91003  -87.27542  94.34214
## 13.03846       3.546884 -56.188008 63.28178  -87.80975  94.90352
## 13.05769       3.575199 -56.526384 63.67678  -88.34224  95.49264
## 13.07692       3.571036 -56.904100 64.04617  -88.91770  96.05978
## 13.09615       3.564065 -57.279417 64.40755  -89.48801  96.61614
## 13.11538       3.565298 -57.642143 64.77274  -90.04340  97.17400
## 13.13462       3.567009 -58.003016 65.13703  -90.59622  97.73024
## 13.15385       3.566653 -58.364349 65.49765  -91.14864  98.28195
## 13.17308       3.566235 -58.723446 65.85591  -91.69761  98.83008
## 13.19231       3.566335 -59.079848 66.21252  -92.24273  99.37540
## 13.21154       3.566437 -59.434287 66.56716  -92.78486  99.91773
## 13.23077       3.566409 -59.786900 66.91972  -93.32412 100.45693
## 13.25000       3.566384 -60.137544 67.27031  -93.86037 100.99313
## 13.26923       3.566392 -60.486229 67.61901  -94.39364 101.52642
## 13.28846       3.566398 -60.833032 67.96583  -94.92403 102.05683
## 13.30769       3.566396 -61.177987 68.31078  -95.45159 102.58439
## 13.32692       3.566394 -61.521112 68.65390  -95.97636 103.10915
## 13.34615       3.566395 -61.862436 68.99523  -96.49837 103.63116
## 13.36538       3.566395 -62.201988 69.33478  -97.01767 104.15046
## 13.38462       3.566395 -62.539797 69.67259  -97.53430 104.66709
## 13.40385       3.566395 -62.875889 70.00868  -98.04831 105.18110
## 13.42308       3.566395 -63.210288 70.34308  -98.55973 105.69252
## 13.44231       3.566395 -63.543022 70.67581  -99.06860 106.20139
## 13.46154       3.566395 -63.874114 71.00690  -99.57496 106.70775
## 13.48077       3.566395 -64.203588 71.33638 -100.07885 107.21164
## 13.50000       3.566395 -64.531469 71.66426 -100.58030 107.71309
## 13.51923       3.566395 -64.857778 71.99057 -101.07935 108.21214
## 13.53846       3.566395 -65.182538 72.31533 -101.57603 108.70882
## 13.55769       3.566395 -65.505772 72.63856 -102.07037 109.20316
## 13.57692       3.566395 -65.827499 72.96029 -102.56241 109.69520
## 13.59615       3.566395 -66.147743 73.28053 -103.05218 110.18497
## 13.61538       3.566395 -66.466521 73.59931 -103.53971 110.67250
## 13.63462       3.566395 -66.783856 73.91665 -104.02503 111.15782
## 13.65385       3.566395 -67.099765 74.23256 -104.50817 111.64096
## 13.67308       3.566395 -67.414268 74.54706 -104.98916 112.12195
## 13.69231       3.566395 -67.727384 74.86017 -105.46803 112.60082
## 13.71154       3.566395 -68.039131 75.17192 -105.94481 113.07760
## 13.73077       3.566395 -68.349526 75.48232 -106.41952 113.55231
## 13.75000       3.566395 -68.658588 75.79138 -106.89218 114.02497
## 13.76923       3.566395 -68.966332 76.09912 -107.36284 114.49563
## 13.78846       3.566395 -69.272776 76.40557 -107.83150 114.96430
## 13.80769       3.566395 -69.577937 76.71073 -108.29821 115.43100
## 13.82692       3.566395 -69.881830 77.01462 -108.76297 115.89576
## 13.84615       3.566395 -70.184470 77.31726 -109.22582 116.35861
## 13.86538       3.566395 -70.485874 77.61866 -109.68678 116.81957
## 13.88462       3.566395 -70.786055 77.91885 -110.14587 117.27866
## 13.90385       3.566395 -71.085030 78.21782 -110.60311 117.73590
## 13.92308       3.566395 -71.382812 78.51560 -111.05853 118.19132
## 13.94231       3.566395 -71.679416 78.81221 -111.51214 118.64493
## 13.96154       3.566395 -71.974855 79.10765 -111.96398 119.09677
## 13.98077       3.566395 -72.269143 79.40193 -112.41405 119.54684

ets.sj=ets(sj.ts,model="ZZZ")

## Warning in ets(sj.ts, model = "ZZZ"): I can't handle data with frequency greater
## than 24. Seasonality will be ignored. Try stlf() if you need seasonal forecasts.

ets.sj.fc=forecast(ets.sj,h=260)
autoplot(ets.sj.fc)

checkresiduals(ets.sj.fc)

## 
##  Ljung-Box test
## 
## data:  Residuals from ETS(A,Ad,N)
## Q* = 93.059, df = 99, p-value = 0.6492
## 
## Model df: 5.   Total lags used: 104

summary(ets.sj.fc)

## 
## Forecast method: ETS(A,Ad,N)
## 
## Model Information:
## ETS(A,Ad,N) 
## 
## Call:
##  ets(y = sj.ts, model = "ZZZ") 
## 
##   Smoothing parameters:
##     alpha = 0.9999 
##     beta  = 0.1355 
##     phi   = 0.8 
## 
##   Initial states:
##     l = 3.0767 
##     b = 0.2624 
## 
##   sigma:  13.4753
## 
##      AIC     AICc      BIC 
## 11279.55 11279.64 11308.60 
## 
## Error measures:
##                      ME     RMSE      MAE MPE MAPE      MASE       ACF1
## Training set 0.00139911 13.43929 8.026055 NaN  Inf 0.2196945 0.01271293
## 
## Forecasts:
##          Point Forecast      Lo 80     Hi 80      Lo 95     Hi 95
## 19.00000       5.228984  -12.04034  22.49831  -21.18217  31.64014
## 19.01923       5.412350  -20.36663  31.19133  -34.01320  44.83790
## 19.03846       5.559044  -27.46309  38.58118  -44.94396  56.06204
## 19.05769       5.676398  -33.91225  45.26505  -54.86921  66.22201
## 19.07692       5.770282  -39.91170  51.45226  -64.09428  75.63484
## 19.09615       5.845390  -45.55476  57.24554  -72.76435  84.45513
## 19.11538       5.905476  -50.89588  62.70683  -80.96470  92.77565
## 19.13462       5.953544  -55.97209  67.87917  -88.75353 100.66062
## 19.15385       5.992000  -60.81143  72.79542  -96.17502 108.15902
## 19.17308       6.022764  -65.43668  77.48221 -103.26503 115.31056
## 19.19231       6.047375  -69.86720  81.96195 -110.05395 122.14870
## 19.21154       6.067064  -74.11982  86.25395 -116.56819 128.70232
## 19.23077       6.082816  -78.20943  90.37506 -122.83105 134.99668
## 19.25000       6.095417  -82.14931  94.34014 -128.86325 141.05408
## 19.26923       6.105498  -85.95137  98.16237 -134.68334 146.89433
## 19.28846       6.113562  -89.62634 101.85346 -140.30798 152.53510
## 19.30769       6.120014  -93.18385 105.42388 -145.75214 157.99217
## 19.32692       6.125176  -96.63264 108.88299 -151.02935 163.27970
## 19.34615       6.129305  -99.98059 112.23920 -156.15178 168.41039
## 19.36538       6.132608 -103.23483 115.50005 -161.13046 173.39568
## 19.38462       6.135251 -106.40183 118.67233 -165.97536 178.24587
## 19.40385       6.137365 -109.48745 121.76218 -170.69553 182.97026
## 19.42308       6.139056 -112.49702 124.77513 -175.29916 187.57727
## 19.44231       6.140409 -115.43538 127.71620 -179.79371 192.07453
## 19.46154       6.141492 -118.30695 130.58993 -184.18597 196.46895
## 19.48077       6.142358 -121.11575 133.40046 -188.48212 200.76684
## 19.50000       6.143051 -123.86546 136.15156 -192.68781 204.97391
## 19.51923       6.143605 -126.55946 138.84667 -196.80822 209.09543
## 19.53846       6.144048 -129.20082 141.48891 -200.84806 213.13616
## 19.55769       6.144403 -131.79238 144.08118 -204.81170 217.10051
## 19.57692       6.144687 -134.33675 146.62612 -208.70312 220.99250
## 19.59615       6.144914 -136.83632 149.12615 -212.52601 224.81584
## 19.61538       6.145095 -139.29332 151.58351 -216.28377 228.57396
## 19.63462       6.145241 -141.70979 154.00027 -219.97952 232.27000
## 19.65385       6.145357 -144.08764 156.37835 -223.61618 235.90689
## 19.67308       6.145450 -146.42861 158.71951 -227.19644 239.48734
## 19.69231       6.145524 -148.73435 161.02540 -230.72281 243.01386
## 19.71154       6.145584 -151.00639 163.29755 -234.19761 246.48878
## 19.73077       6.145631 -153.24613 165.53739 -237.62302 249.91429
## 19.75000       6.145669 -155.45490 167.74624 -241.00107 253.29241
## 19.76923       6.145700 -157.63394 169.92534 -244.33365 256.62505
## 19.78846       6.145724 -159.78442 172.07587 -247.62253 259.91398
## 19.80769       6.145744 -161.90742 174.19890 -250.86938 263.16087
## 19.82692       6.145759 -164.00395 176.29547 -254.07577 266.36729
## 19.84615       6.145772 -166.07499 178.36654 -257.24316 269.53470
## 19.86538       6.145782 -168.12144 180.41300 -260.37293 272.66450
## 19.88462       6.145790 -170.14415 182.43572 -263.46640 275.75798
## 19.90385       6.145796 -172.14392 184.43551 -266.52479 278.81638
## 19.92308       6.145801 -174.12151 186.41312 -269.54926 281.84087
## 19.94231       6.145805 -176.07766 188.36927 -272.54093 284.83254
## 19.96154       6.145809 -178.01303 190.30464 -275.50083 287.79244
## 19.98077       6.145811 -179.92827 192.21990 -278.42995 290.72157
## 20.00000       6.145813 -181.82401 194.11564 -281.32923 293.62085
## 20.01923       6.145815 -183.70082 195.99245 -284.19956 296.49119
## 20.03846       6.145816 -185.55926 197.85089 -287.04180 299.33343
## 20.05769       6.145817 -187.39986 199.69149 -289.85675 302.14839
## 20.07692       6.145818 -189.22312 201.51475 -292.64519 304.93682
## 20.09615       6.145819 -191.02952 203.32116 -295.40784 307.69948
## 20.11538       6.145820 -192.81952 205.11116 -298.14541 310.43705
## 20.13462       6.145820 -194.59356 206.88520 -300.85857 313.15021
## 20.15385       6.145820 -196.35207 208.64371 -303.54797 315.83961
## 20.17308       6.145821 -198.09543 210.38707 -306.21421 318.50585
## 20.19231       6.145821 -199.82403 212.11568 -308.85789 321.14953
## 20.21154       6.145821 -201.53825 213.82990 -311.47956 323.77120
## 20.23077       6.145821 -203.23844 215.53008 -314.07977 326.37141
## 20.25000       6.145821 -204.92493 217.21657 -316.65904 328.95068
## 20.26923       6.145821 -206.59805 218.88970 -319.21786 331.50950
## 20.28846       6.145821 -208.25812 220.54976 -321.75671 334.04836
## 20.30769       6.145821 -209.90543 222.19708 -324.27606 336.56770
## 20.32692       6.145822 -211.54028 223.83192 -326.77634 339.06798
## 20.34615       6.145822 -213.16294 225.45458 -329.25799 341.54963
## 20.36538       6.145822 -214.77368 227.06533 -331.72140 344.01305
## 20.38462       6.145822 -216.37276 228.66441 -334.16699 346.45863
## 20.40385       6.145822 -217.96044 230.25208 -336.59513 348.88677
## 20.42308       6.145822 -219.53694 231.82859 -339.00618 351.29782
## 20.44231       6.145822 -221.10251 233.39415 -341.40051 353.69215
## 20.46154       6.145822 -222.65736 234.94901 -343.77846 356.07010
## 20.48077       6.145822 -224.20173 236.49337 -346.14035 358.43199
## 20.50000       6.145822 -225.73580 238.02744 -348.48652 360.77816
## 20.51923       6.145822 -227.25979 239.55144 -350.81726 363.10890
## 20.53846       6.145822 -228.77390 241.06554 -353.13289 365.42453
## 20.55769       6.145822 -230.27831 242.56995 -355.43368 367.72533
## 20.57692       6.145822 -231.77321 244.06485 -357.71993 370.01157
## 20.59615       6.145822 -233.25877 245.55041 -359.99190 372.28355
## 20.61538       6.145822 -234.73517 247.02681 -362.24986 374.54151
## 20.63462       6.145822 -236.20258 248.49422 -364.49407 376.78571
## 20.65385       6.145822 -237.66115 249.95279 -366.72477 379.01641
## 20.67308       6.145822 -239.11105 251.40270 -368.94220 381.23384
## 20.69231       6.145822 -240.55243 252.84408 -371.14660 383.43824
## 20.71154       6.145822 -241.98544 254.27708 -373.33819 385.62984
## 20.73077       6.145822 -243.41022 255.70186 -375.51721 387.80885
## 20.75000       6.145822 -244.82691 257.11855 -377.68385 389.97549
## 20.76923       6.145822 -246.23565 258.52729 -379.83833 392.12997
## 20.78846       6.145822 -247.63656 259.92821 -381.98084 394.27249
## 20.80769       6.145822 -249.02979 261.32144 -384.11160 396.40325
## 20.82692       6.145822 -250.41545 262.70710 -386.23079 398.52243
## 20.84615       6.145822 -251.79367 264.08532 -388.33859 400.63023
## 20.86538       6.145822 -253.16456 265.45621 -390.43519 402.72683
## 20.88462       6.145822 -254.52825 266.81989 -392.52077 404.81241
## 20.90385       6.145822 -255.88483 268.17648 -394.59549 406.88713
## 20.92308       6.145822 -257.23443 269.52608 -396.65952 408.95116
## 20.94231       6.145822 -258.57715 270.86880 -398.71303 411.00467
## 20.96154       6.145822 -259.91310 272.20474 -400.75618 413.04782
## 20.98077       6.145822 -261.24236 273.53401 -402.78912 415.08076
## 21.00000       6.145822 -262.56506 274.85670 -404.81200 417.10365
## 21.01923       6.145822 -263.88127 276.17291 -406.82498 419.11662
## 21.03846       6.145822 -265.19110 277.48274 -408.82819 421.11983
## 21.05769       6.145822 -266.49463 278.78628 -410.82177 423.11342
## 21.07692       6.145822 -267.79197 280.08361 -412.80587 425.09752
## 21.09615       6.145822 -269.08319 281.37483 -414.78062 427.07227
## 21.11538       6.145822 -270.36838 282.66002 -416.74615 429.03779
## 21.13462       6.145822 -271.64762 283.93926 -418.70258 430.99423
## 21.15385       6.145822 -272.92100 285.21264 -420.65005 432.94169
## 21.17308       6.145822 -274.18859 286.48024 -422.58867 434.88031
## 21.19231       6.145822 -275.45048 287.74213 -424.51856 436.81020
## 21.21154       6.145822 -276.70674 288.99838 -426.43984 438.73149
## 21.23077       6.145822 -277.95745 290.24909 -428.35263 440.64427
## 21.25000       6.145822 -279.20267 291.49431 -430.25703 442.54868
## 21.26923       6.145822 -280.44248 292.73412 -432.15316 444.44481
## 21.28846       6.145822 -281.67695 293.96860 -434.04113 446.33277
## 21.30769       6.145822 -282.90615 295.19780 -435.92102 448.21267
## 21.32692       6.145822 -284.13015 296.42179 -437.79296 450.08461
## 21.34615       6.145822 -285.34900 297.64064 -439.65704 451.94868
## 21.36538       6.145822 -286.56278 298.85442 -441.51336 453.80500
## 21.38462       6.145822 -287.77155 300.06319 -443.36201 455.65365
## 21.40385       6.145822 -288.97536 301.26701 -445.20308 457.49473
## 21.42308       6.145822 -290.17429 302.46593 -447.03668 459.32833
## 21.44231       6.145822 -291.36838 303.66003 -448.86289 461.15454
## 21.46154       6.145822 -292.55771 304.84935 -450.68180 462.97344
## 21.48077       6.145822 -293.74231 306.03395 -452.49350 464.78514
## 21.50000       6.145822 -294.92225 307.21390 -454.29806 466.58971
## 21.51923       6.145822 -296.09759 308.38923 -456.09559 468.38723
## 21.53846       6.145822 -297.26837 309.56002 -457.88615 470.17779
## 21.55769       6.145822 -298.43466 310.72630 -459.66982 471.96147
## 21.57692       6.145822 -299.59649 311.88814 -461.44670 473.73834
## 21.59615       6.145822 -300.75393 313.04557 -463.21684 475.50849
## 21.61538       6.145822 -301.90702 314.19866 -464.98034 477.27198
## 21.63462       6.145822 -303.05580 315.34745 -466.73726 479.02890
## 21.65385       6.145822 -304.20034 316.49198 -468.48767 480.77932
## 21.67308       6.145822 -305.34067 317.63231 -470.23166 482.52330
## 21.69231       6.145822 -306.47684 318.76848 -471.96928 484.26092
## 21.71154       6.145822 -307.60889 319.90054 -473.70061 485.99225
## 21.73077       6.145822 -308.73688 321.02852 -475.42571 487.71736
## 21.75000       6.145822 -309.86084 322.15248 -477.14466 489.43631
## 21.76923       6.145822 -310.98082 323.27246 -478.85752 491.14916
## 21.78846       6.145822 -312.09685 324.38849 -480.56435 492.85599
## 21.80769       6.145822 -313.20899 325.50063 -482.26521 494.55685
## 21.82692       6.145822 -314.31726 326.60890 -483.96017 496.25181
## 21.84615       6.145822 -315.42172 327.71336 -485.64929 497.94093
## 21.86538       6.145822 -316.52239 328.81403 -487.33263 499.62427
## 21.88462       6.145822 -317.61932 329.91097 -489.01024 501.30188
## 21.90385       6.145822 -318.71255 331.00420 -490.68219 502.97383
## 21.92308       6.145822 -319.80212 332.09376 -492.34853 504.64018
## 21.94231       6.145822 -320.88805 333.17969 -494.00932 506.30097
## 21.96154       6.145822 -321.97039 334.26203 -495.66462 507.95626
## 21.98077       6.145822 -323.04917 335.34081 -497.31447 509.60611
## 22.00000       6.145822 -324.12442 336.41607 -498.95893 511.25057
## 22.01923       6.145822 -325.19619 337.48783 -500.59806 512.88970
## 22.03846       6.145822 -326.26450 338.55615 -502.23190 514.52354
## 22.05769       6.145822 -327.32939 339.62103 -503.86051 516.15215
## 22.07692       6.145822 -328.39089 340.68253 -505.48393 517.77557
## 22.09615       6.145822 -329.44903 341.74068 -507.10222 519.39386
## 22.11538       6.145822 -330.50385 342.79549 -508.71542 521.00706
## 22.13462       6.145822 -331.55537 343.84701 -510.32358 522.61522
## 22.15385       6.145822 -332.60363 344.89527 -511.92675 524.21840
## 22.17308       6.145822 -333.64865 345.94029 -513.52498 525.81662
## 22.19231       6.145822 -334.69047 346.98211 -515.11830 527.40995
## 22.21154       6.145822 -335.72911 348.02076 -516.70677 528.99842
## 22.23077       6.145822 -336.76461 349.05626 -518.29043 530.58207
## 22.25000       6.145822 -337.79699 350.08864 -519.86932 532.16096
## 22.26923       6.145822 -338.82628 351.11793 -521.44349 533.73513
## 22.28846       6.145822 -339.85251 352.14416 -523.01297 535.30461
## 22.30769       6.145822 -340.87571 353.16735 -524.57781 536.86945
## 22.32692       6.145822 -341.89590 354.18754 -526.13805 538.42969
## 22.34615       6.145822 -342.91310 355.20474 -527.69373 539.98537
## 22.36538       6.145822 -343.92735 356.21899 -529.24489 541.53654
## 22.38462       6.145822 -344.93867 357.23031 -530.79157 543.08321
## 22.40385       6.145822 -345.94708 358.23873 -532.33381 544.62545
## 22.42308       6.145822 -346.95262 359.24426 -533.87164 546.16328
## 22.44231       6.145822 -347.95530 360.24694 -535.40511 547.69675
## 22.46154       6.145822 -348.95514 361.24679 -536.93424 549.22588
## 22.48077       6.145822 -349.95218 362.24383 -538.45908 550.75073
## 22.50000       6.145822 -350.94644 363.23809 -539.97967 552.27131
## 22.51923       6.145822 -351.93794 364.22958 -541.49603 553.78767
## 22.53846       6.145822 -352.92670 365.21834 -543.00820 555.29985
## 22.55769       6.145822 -353.91274 366.20438 -544.51623 556.80787
## 22.57692       6.145822 -354.89609 367.18773 -546.02013 558.31178
## 22.59615       6.145822 -355.87677 368.16841 -547.51995 559.81159
## 22.61538       6.145822 -356.85480 369.14644 -549.01572 561.30736
## 22.63462       6.145822 -357.83020 370.12184 -550.50747 562.79911
## 22.65385       6.145822 -358.80299 371.09464 -551.99523 564.28687
## 22.67308       6.145822 -359.77320 372.06485 -553.47903 565.77068
## 22.69231       6.145822 -360.74085 373.03249 -554.95892 567.25056
## 22.71154       6.145822 -361.70594 373.99759 -556.43490 568.72655
## 22.73077       6.145822 -362.66852 374.96016 -557.90703 570.19868
## 22.75000       6.145822 -363.62858 375.92023 -559.37533 571.66697
## 22.76923       6.145822 -364.58616 376.87781 -560.83982 573.13146
## 22.78846       6.145822 -365.54127 377.83292 -562.30054 574.59218
## 22.80769       6.145822 -366.49394 378.78558 -563.75751 576.04916
## 22.82692       6.145822 -367.44418 379.73582 -565.21077 577.50242
## 22.84615       6.145822 -368.39200 380.68364 -566.66035 578.95199
## 22.86538       6.145822 -369.33743 381.62908 -568.10626 580.39790
## 22.88462       6.145822 -370.28049 382.57213 -569.54854 581.84019
## 22.90385       6.145822 -371.22119 383.51284 -570.98722 583.27887
## 22.92308       6.145822 -372.15955 384.45120 -572.42232 584.71397
## 22.94231       6.145822 -373.09559 385.38724 -573.85387 586.14552
## 22.96154       6.145822 -374.02933 386.32097 -575.28190 587.57354
## 22.98077       6.145822 -374.96078 387.25242 -576.70642 588.99807
## 23.00000       6.145822 -375.88995 388.18160 -578.12748 590.41912
## 23.01923       6.145822 -376.81688 389.10852 -579.54508 591.83673
## 23.03846       6.145822 -377.74156 390.03320 -580.95927 593.25091
## 23.05769       6.145822 -378.66402 390.95567 -582.37005 594.66169
## 23.07692       6.145822 -379.58428 391.87592 -583.77746 596.06910
## 23.09615       6.145822 -380.50235 392.79399 -585.18152 597.47316
## 23.11538       6.145822 -381.41824 393.70988 -586.58226 598.87390
## 23.13462       6.145822 -382.33197 394.62361 -587.97969 600.27133
## 23.15385       6.145822 -383.24356 395.53520 -589.37384 601.66549
## 23.17308       6.145822 -384.15302 396.44466 -590.76474 603.05638
## 23.19231       6.145822 -385.06036 397.35200 -592.15240 604.44405
## 23.21154       6.145822 -385.96560 398.25725 -593.53685 605.82850
## 23.23077       6.145822 -386.86876 399.16041 -594.91812 607.20976
## 23.25000       6.145822 -387.76985 400.06150 -596.29622 608.58786
## 23.26923       6.145822 -388.66889 400.96053 -597.67117 609.96281
## 23.28846       6.145822 -389.56588 401.85752 -599.04299 611.33464
## 23.30769       6.145822 -390.46084 402.75248 -600.41172 612.70336
## 23.32692       6.145822 -391.35378 403.64543 -601.77736 614.06901
## 23.34615       6.145822 -392.24473 404.53637 -603.13995 615.43159
## 23.36538       6.145822 -393.13368 405.42533 -604.49949 616.79113
## 23.38462       6.145822 -394.02067 406.31231 -605.85601 618.14765
## 23.40385       6.145822 -394.90569 407.19733 -607.20953 619.50117
## 23.42308       6.145822 -395.78876 408.08040 -608.56007 620.85172
## 23.44231       6.145822 -396.66989 408.96154 -609.90765 622.19930
## 23.46154       6.145822 -397.54911 409.84075 -611.25229 623.54394
## 23.48077       6.145822 -398.42641 410.71805 -612.59401 624.88565
## 23.50000       6.145822 -399.30181 411.59346 -613.93282 626.22447
## 23.51923       6.145822 -400.17533 412.46697 -615.26875 627.56040
## 23.53846       6.145822 -401.04697 413.33862 -616.60182 628.89346
## 23.55769       6.145822 -401.91675 414.20840 -617.93203 630.22368
## 23.57692       6.145822 -402.78469 415.07633 -619.25942 631.55106
## 23.59615       6.145822 -403.65078 415.94242 -620.58400 632.87564
## 23.61538       6.145822 -404.51505 416.80669 -621.90578 634.19742
## 23.63462       6.145822 -405.37750 417.66914 -623.22479 635.51643
## 23.65385       6.145822 -406.23815 418.52979 -624.54103 636.83268
## 23.67308       6.145822 -407.09700 419.38865 -625.85454 638.14618
## 23.69231       6.145822 -407.95408 420.24572 -627.16532 639.45696
## 23.71154       6.145822 -408.80938 421.10102 -628.47340 640.76504
## 23.73077       6.145822 -409.66293 421.95457 -629.77878 642.07042
## 23.75000       6.145822 -410.51472 422.80637 -631.08149 643.37313
## 23.76923       6.145822 -411.36478 423.65642 -632.38154 644.67319
## 23.78846       6.145822 -412.21311 424.50475 -633.67895 645.97060
## 23.80769       6.145822 -413.05973 425.35137 -634.97374 647.26538
## 23.82692       6.145822 -413.90463 426.19628 -636.26591 648.55756
## 23.84615       6.145822 -414.74785 427.03949 -637.55549 649.84714
## 23.86538       6.145822 -415.58937 427.88102 -638.84250 651.13414
## 23.88462       6.145822 -416.42922 428.72087 -640.12694 652.41858
## 23.90385       6.145822 -417.26741 429.55905 -641.40883 653.70047
## 23.92308       6.145822 -418.10394 430.39558 -642.68819 654.97983
## 23.94231       6.145822 -418.93882 431.23046 -643.96503 656.25668
## 23.96154       6.145822 -419.77206 432.06371 -645.23937 657.53102
## 23.98077       6.145822 -420.60368 432.89533 -646.51122 658.80287

ets.iq=ets(iq.ts,model="ZZZ")

## Warning in ets(iq.ts, model = "ZZZ"): I can't handle data with frequency greater
## than 24. Seasonality will be ignored. Try stlf() if you need seasonal forecasts.

ets.iq.fc=forecast(ets.iq,h=156)
autoplot(ets.iq.fc)

checkresiduals(ets.iq.fc)

## 
##  Ljung-Box test
## 
## data:  Residuals from ETS(A,N,N)
## Q* = 121.31, df = 102, p-value = 0.09321
## 
## Model df: 2.   Total lags used: 104

summary(ets.iq.fc)

## 
## Forecast method: ETS(A,N,N)
## 
## Model Information:
## ETS(A,N,N) 
## 
## Call:
##  ets(y = iq.ts, model = "ZZZ") 
## 
##   Smoothing parameters:
##     alpha = 0.6543 
## 
##   Initial states:
##     l = 0.0744 
## 
##   sigma:  7.4063
## 
##      AIC     AICc      BIC 
## 5338.417 5338.463 5351.178 
## 
## Error measures:
##                       ME     RMSE      MAE  MPE MAPE      MASE      ACF1
## Training set 0.009226816 7.392083 3.699635 -Inf  Inf 0.3919034 0.0713476
## 
## Forecasts:
##          Point Forecast      Lo 80    Hi 80      Lo 95     Hi 95
## 11.00000       3.213589  -6.278017 12.70520  -11.30257  17.72975
## 11.01923       3.213589  -8.129126 14.55630  -14.13360  20.56078
## 11.03846       3.213589  -9.717915 16.14509  -16.56344  22.99062
## 11.05769       3.213589 -11.131807 17.55899  -18.72580  25.15298
## 11.07692       3.213589 -12.418334 18.84551  -20.69337  27.12055
## 11.09615       3.213589 -13.606744 20.03392  -22.51089  28.93807
## 11.11538       3.213589 -14.716559 21.14374  -24.20821  30.63539
## 11.13462       3.213589 -15.761575 22.18875  -25.80642  32.23360
## 11.15385       3.213589 -16.751967 23.17915  -27.32109  33.74827
## 11.17308       3.213589 -17.695501 24.12268  -28.76411  35.19128
## 11.19231       3.213589 -18.598258 25.02544  -30.14475  36.57193
## 11.21154       3.213589 -19.465107 25.89229  -31.47048  37.89766
## 11.23077       3.213589 -20.300021 26.72720  -32.74738  39.17455
## 11.25000       3.213589 -21.106289 27.53347  -33.98046  40.40763
## 11.26923       3.213589 -21.886672 28.31385  -35.17395  41.60113
## 11.28846       3.213589 -22.643513 29.07069  -36.33144  42.75861
## 11.30769       3.213589 -23.378822 29.80600  -37.45599  43.88317
## 11.32692       3.213589 -24.094339 30.52152  -38.55028  44.97746
## 11.34615       3.213589 -24.791581 31.21876  -39.61662  46.04380
## 11.36538       3.213589 -25.471880 31.89906  -40.65705  47.08423
## 11.38462       3.213589 -26.136415 32.56359  -41.67337  48.10055
## 11.40385       3.213589 -26.786234 33.21341  -42.66718  49.09436
## 11.42308       3.213589 -27.422272 33.84945  -43.63992  50.06710
## 11.44231       3.213589 -28.045371 34.47255  -44.59286  51.02004
## 11.46154       3.213589 -28.656290 35.08347  -45.52718  51.95436
## 11.48077       3.213589 -29.255716 35.68290  -46.44393  52.87111
## 11.50000       3.213589 -29.844275 36.27145  -47.34405  53.77123
## 11.51923       3.213589 -30.422537 36.84972  -48.22843  54.65561
## 11.53846       3.213589 -30.991025 37.41820  -49.09785  55.52503
## 11.55769       3.213589 -31.550217 37.97740  -49.95306  56.38024
## 11.57692       3.213589 -32.100556 38.52773  -50.79474  57.22191
## 11.59615       3.213589 -32.642449 39.06963  -51.62349  58.05067
## 11.61538       3.213589 -33.176273 39.60345  -52.43990  58.86708
## 11.63462       3.213589 -33.702378 40.12956  -53.24451  59.67169
## 11.65385       3.213589 -34.221091 40.64827  -54.03781  60.46499
## 11.67308       3.213589 -34.732714 41.15989  -54.82027  61.24745
## 11.69231       3.213589 -35.237529 41.66471  -55.59232  62.01950
## 11.71154       3.213589 -35.735802 42.16298  -56.35437  62.78154
## 11.73077       3.213589 -36.227781 42.65496  -57.10678  63.53396
## 11.75000       3.213589 -36.713699 43.14088  -57.84993  64.27711
## 11.76923       3.213589 -37.193773 43.62095  -58.58414  65.01132
## 11.78846       3.213589 -37.668210 44.09539  -59.30973  65.73691
## 11.80769       3.213589 -38.137205 44.56438  -60.02699  66.45417
## 11.82692       3.213589 -38.600939 45.02812  -60.73621  67.16339
## 11.84615       3.213589 -39.059586 45.48677  -61.43765  67.86483
## 11.86538       3.213589 -39.513311 45.94049  -62.13157  68.55874
## 11.88462       3.213589 -39.962267 46.38945  -62.81819  69.24536
## 11.90385       3.213589 -40.406603 46.83378  -63.49774  69.92492
## 11.92308       3.213589 -40.846459 47.27364  -64.17044  70.59762
## 11.94231       3.213589 -41.281966 47.70914  -64.83649  71.26367
## 11.96154       3.213589 -41.713252 48.14043  -65.49608  71.92326
## 11.98077       3.213589 -42.140436 48.56762  -66.14941  72.57659
## 12.00000       3.213589 -42.563635 48.99081  -66.79663  73.22381
## 12.01923       3.213589 -42.982957 49.41014  -67.43793  73.86511
## 12.03846       3.213589 -43.398506 49.82569  -68.07346  74.50064
## 12.05769       3.213589 -43.810384 50.23756  -68.70337  75.13055
## 12.07692       3.213589 -44.218685 50.64586  -69.32782  75.75499
## 12.09615       3.213589 -44.623502 51.05068  -69.94693  76.37411
## 12.11538       3.213589 -45.024921 51.45210  -70.56085  76.98803
## 12.13462       3.213589 -45.423028 51.85021  -71.16970  77.59688
## 12.15385       3.213589 -45.817902 52.24508  -71.77361  78.20078
## 12.17308       3.213589 -46.209621 52.63680  -72.37269  78.79987
## 12.19231       3.213589 -46.598260 53.02544  -72.96706  79.39424
## 12.21154       3.213589 -46.983890 53.41107  -73.55683  79.98401
## 12.23077       3.213589 -47.366580 53.79376  -74.14210  80.56928
## 12.25000       3.213589 -47.746397 54.17358  -74.72298  81.15016
## 12.26923       3.213589 -48.123403 54.55058  -75.29957  81.72674
## 12.28846       3.213589 -48.497661 54.92484  -75.87194  82.29912
## 12.30769       3.213589 -48.869229 55.29641  -76.44021  82.86739
## 12.32692       3.213589 -49.238166 55.66534  -77.00445  83.43163
## 12.34615       3.213589 -49.604525 56.03170  -77.56475  83.99193
## 12.36538       3.213589 -49.968361 56.39554  -78.12118  84.54836
## 12.38462       3.213589 -50.329724 56.75690  -78.67384  85.10102
## 12.40385       3.213589 -50.688665 57.11584  -79.22280  85.64997
## 12.42308       3.213589 -51.045232 57.47241  -79.76812  86.19530
## 12.44231       3.213589 -51.399470 57.82665  -80.30988  86.73706
## 12.46154       3.213589 -51.751426 58.17860  -80.84815  87.27533
## 12.48077       3.213589 -52.101142 58.52832  -81.38299  87.81017
## 12.50000       3.213589 -52.448661 58.87584  -81.91448  88.34166
## 12.51923       3.213589 -52.794024 59.22120  -82.44266  88.86984
## 12.53846       3.213589 -53.137270 59.56445  -82.96761  89.39479
## 12.55769       3.213589 -53.478438 59.90562  -83.48938  89.91656
## 12.57692       3.213589 -53.817565 60.24474  -84.00803  90.43521
## 12.59615       3.213589 -54.154687 60.58187  -84.52362  90.95080
## 12.61538       3.213589 -54.489840 60.91702  -85.03619  91.46337
## 12.63462       3.213589 -54.823057 61.25024  -85.54580  91.97298
## 12.65385       3.213589 -55.154372 61.58155  -86.05251  92.47968
## 12.67308       3.213589 -55.483818 61.91100  -86.55635  92.98353
## 12.69231       3.213589 -55.811424 62.23860  -87.05738  93.48456
## 12.71154       3.213589 -56.137222 62.56440  -87.55564  93.98282
## 12.73077       3.213589 -56.461241 62.88842  -88.05119  94.47837
## 12.75000       3.213589 -56.783511 63.21069  -88.54406  94.97124
## 12.76923       3.213589 -57.104058 63.53124  -89.03429  95.46147
## 12.78846       3.213589 -57.422911 63.85009  -89.52194  95.94912
## 12.80769       3.213589 -57.740097 64.16728  -90.00703  96.43421
## 12.82692       3.213589 -58.055640 64.48282  -90.48961  96.91679
## 12.84615       3.213589 -58.369566 64.79675  -90.96972  97.39690
## 12.86538       3.213589 -58.681901 65.10908  -91.44739  97.87457
## 12.88462       3.213589 -58.992667 65.41985  -91.92267  98.34985
## 12.90385       3.213589 -59.301888 65.72907  -92.39558  98.82276
## 12.92308       3.213589 -59.609587 66.03677  -92.86617  99.29335
## 12.94231       3.213589 -59.915787 66.34297  -93.33446  99.76164
## 12.96154       3.213589 -60.220508 66.64769  -93.80049 100.22767
## 12.98077       3.213589 -60.523773 66.95095  -94.26429 100.69147
## 13.00000       3.213589 -60.825601 67.25278  -94.72590 101.15308
## 13.01923       3.213589 -61.126014 67.55319  -95.18534 101.61252
## 13.03846       3.213589 -61.425030 67.85221  -95.64265 102.06983
## 13.05769       3.213589 -61.722670 68.14985  -96.09785 102.52503
## 13.07692       3.213589 -62.018951 68.44613  -96.55097 102.97815
## 13.09615       3.213589 -62.313893 68.74107  -97.00205 103.42923
## 13.11538       3.213589 -62.607514 69.03469  -97.45110 103.87828
## 13.13462       3.213589 -62.899830 69.32701  -97.89816 104.32534
## 13.15385       3.213589 -63.190859 69.61804  -98.34325 104.77043
## 13.17308       3.213589 -63.480619 69.90780  -98.78640 105.21358
## 13.19231       3.213589 -63.769125 70.19630  -99.22763 105.65481
## 13.21154       3.213589 -64.056394 70.48357  -99.66697 106.09415
## 13.23077       3.213589 -64.342441 70.76962 -100.10444 106.53162
## 13.25000       3.213589 -64.627282 71.05446 -100.54007 106.96725
## 13.26923       3.213589 -64.910932 71.33811 -100.97388 107.40105
## 13.28846       3.213589 -65.193406 71.62059 -101.40588 107.83306
## 13.30769       3.213589 -65.474719 71.90190 -101.83611 108.26329
## 13.32692       3.213589 -65.754884 72.18206 -102.26459 108.69177
## 13.34615       3.213589 -66.033915 72.46109 -102.69133 109.11851
## 13.36538       3.213589 -66.311827 72.73901 -103.11636 109.54354
## 13.38462       3.213589 -66.588632 73.01581 -103.53970 109.96688
## 13.40385       3.213589 -66.864344 73.29152 -103.96136 110.38854
## 13.42308       3.213589 -67.138975 73.56615 -104.38137 110.80855
## 13.44231       3.213589 -67.412539 73.83972 -104.79975 111.22693
## 13.46154       3.213589 -67.685046 74.11223 -105.21652 111.64370
## 13.48077       3.213589 -67.956511 74.38369 -105.63169 112.05887
## 13.50000       3.213589 -68.226944 74.65412 -106.04528 112.47246
## 13.51923       3.213589 -68.496357 74.92354 -106.45731 112.88449
## 13.53846       3.213589 -68.764762 75.19194 -106.86780 113.29498
## 13.55769       3.213589 -69.032169 75.45935 -107.27676 113.70394
## 13.57692       3.213589 -69.298590 75.72577 -107.68422 114.11140
## 13.59615       3.213589 -69.564036 75.99122 -108.09018 114.51736
## 13.61538       3.213589 -69.828518 76.25570 -108.49467 114.92185
## 13.63462       3.213589 -70.092045 76.51922 -108.89770 115.32488
## 13.65385       3.213589 -70.354628 76.78181 -109.29929 115.72647
## 13.67308       3.213589 -70.616278 77.04346 -109.69945 116.12663
## 13.69231       3.213589 -70.877003 77.30418 -110.09819 116.52537
## 13.71154       3.213589 -71.136814 77.56399 -110.49554 116.92272
## 13.73077       3.213589 -71.395720 77.82290 -110.89150 117.31868
## 13.75000       3.213589 -71.653731 78.08091 -111.28610 117.71328
## 13.76923       3.213589 -71.910856 78.33804 -111.67934 118.10651
## 13.78846       3.213589 -72.167104 78.59428 -112.07123 118.49841
## 13.80769       3.213589 -72.422484 78.84966 -112.46180 118.88898
## 13.82692       3.213589 -72.677004 79.10418 -112.85106 119.27824
## 13.84615       3.213589 -72.930673 79.35785 -113.23901 119.66619
## 13.86538       3.213589 -73.183501 79.61068 -113.62568 120.05286
## 13.88462       3.213589 -73.435494 79.86267 -114.01107 120.43825
## 13.90385       3.213589 -73.686661 80.11384 -114.39520 120.82237
## 13.92308       3.213589 -73.937011 80.36419 -114.77807 121.20525
## 13.94231       3.213589 -74.186551 80.61373 -115.15971 121.58689
## 13.96154       3.213589 -74.435290 80.86247 -115.54012 121.96730
## 13.98077       3.213589 -74.683233 81.11041 -115.91932 122.34650

nn.sj=nnetar(sj.ts)
nn.sj.fc=forecast(nn.sj,n=260)
autoplot(nn.sj.fc)

checkresiduals(nn.sj.fc)

## Warning in modeldf.default(object): Could not find appropriate degrees of
## freedom for this model.

summary(nn.sj.fc)

## 
## Forecast method: NNAR(14,1,8)[52]
## 
## Model Information:
## 
## Average of 20 networks, each of which is
## a 15-8-1 network with 137 weights
## options were - linear output units 
## 
## Error measures:
##                       ME     RMSE      MAE  MPE MAPE      MASE        ACF1
## Training set -0.02916514 7.021991 5.081972 -Inf  Inf 0.1391071 -0.01325086
## 
## Forecasts:
##        Point Forecast
## 19.000       5.636054
## 19.019       5.481144
## 19.038       6.587799
## 19.058       7.893551
## 19.077       9.098632
## 19.096      10.047051
## 19.115      10.972826
## 19.135      12.226914
## 19.154      13.734772
## 19.173      14.857715
## 19.192      15.717831
## 19.212      16.951791
## 19.231      18.257685
## 19.250      19.425901
## 19.269      20.350479
## 19.288      21.333160
## 19.308      22.312748
## 19.327      23.173866
## 19.346      24.162363
## 19.365      24.676934
## 19.385      25.313397
## 19.404      26.013609
## 19.423      25.853803
## 19.442      26.097070
## 19.462      26.674429
## 19.481      27.395998
## 19.500      28.185928
## 19.519      28.789048
## 19.538      29.531838
## 19.558      30.268589
## 19.577      31.117487
## 19.596      32.133863
## 19.615      33.232231
## 19.635      34.595599
## 19.654      36.077810
## 19.673      37.646087
## 19.692      39.440548
## 19.712      41.426356
## 19.731      43.920015
## 19.750      46.794865
## 19.769      50.510984
## 19.788      54.584619
## 19.808      59.299671
## 19.827      65.785479
## 19.846      74.773417
## 19.865      86.039247
## 19.885      99.752117
## 19.904     115.641071
## 19.923     133.910542
## 19.942     154.677391
## 19.962     180.357549
## 19.981     215.796986
## 20.000     264.860850
## 20.019     318.911058
## 20.038     363.098967
## 20.058     393.115811
## 20.077     410.006571
## 20.096     420.652047
## 20.115     424.286053
## 20.135     420.884599
## 20.154     418.657236
## 20.173     417.595582
## 20.192     389.691186
## 20.212     338.130200
## 20.231     259.842459
## 20.250     178.994082
## 20.269     141.315097
## 20.288     141.074214
## 20.308     126.892590
## 20.327      88.930609
## 20.346      66.379349
## 20.365      51.128456
## 20.385      37.188452
## 20.404      30.606656
## 20.423      19.245871
## 20.442      13.975828
## 20.462      10.058445
## 20.481      10.123173
## 20.500      12.229238
## 20.519       8.518315
## 20.538       7.560043
## 20.558       6.308464
## 20.577       5.077719
## 20.596       4.577098
## 20.615       4.438929
## 20.635       4.047917
## 20.654       4.402446
## 20.673       4.452874
## 20.692       5.026385
## 20.712       5.446411
## 20.731       6.245654
## 20.750       7.325255
## 20.769       7.946419
## 20.788       9.044674
## 20.808      10.096176
## 20.827      11.137957
## 20.846      12.259916
## 20.865      13.414459
## 20.885      14.504755
## 20.904      15.704603
## 20.923      16.862360
## 20.942      18.009336
## 20.962      19.368308
## 20.981      21.739064

nn.iq=nnetar(iq.ts)
nn.iq.fc=forecast(nn.iq,n=156)
autoplot(nn.iq.fc)

checkresiduals(nn.iq.fc)

## Warning in modeldf.default(object): Could not find appropriate degrees of
## freedom for this model.

summary(nn.iq.fc)

## 
## Forecast method: NNAR(5,1,4)[52]
## 
## Model Information:
## 
## Average of 20 networks, each of which is
## a 6-4-1 network with 33 weights
## options were - linear output units 
## 
## Error measures:
##                       ME     RMSE      MAE  MPE MAPE      MASE       ACF1
## Training set -0.00372408 4.902024 3.230232 -Inf  Inf 0.3421794 -0.0109473
## 
## Forecasts:
##        Point Forecast
## 11.000       3.179567
## 11.019       3.599909
## 11.038       4.134918
## 11.058       4.259176
## 11.077       4.276526
## 11.096       4.336583
## 11.115       4.332559
## 11.135       4.272942
## 11.154       4.273593
## 11.173       4.336713
## 11.192       4.361805
## 11.212       4.395136
## 11.231       4.504078
## 11.250       4.494154
## 11.269       4.554716
## 11.288       4.591500
## 11.308       4.583940
## 11.327       4.590418
## 11.346       4.559163
## 11.365       4.407235
## 11.385       4.391956
## 11.404       4.340081
## 11.423       4.325099
## 11.442       4.308486
## 11.462       4.397355
## 11.481       4.480902
## 11.500       4.550891
## 11.519       4.380260
## 11.538       4.320281
## 11.558       4.213387
## 11.577       4.112986
## 11.596       4.039953
## 11.615       3.977682
## 11.635       3.983444
## 11.654       3.920986
## 11.673       3.903571
## 11.692       3.886314
## 11.712       3.874947
## 11.731       3.889114
## 11.750       3.975537
## 11.769       3.948728
## 11.788       3.999365
## 11.808       3.994948
## 11.827       4.067616
## 11.846       4.155332
## 11.865       4.113750
## 11.885       4.132617
## 11.904       4.152840
## 11.923       4.107991
## 11.942       4.248878
## 11.962       4.312154
## 11.981       4.291229
## 12.000       4.329927
## 12.019       4.334642
## 12.038       4.322136
## 12.058       4.313349
## 12.077       4.305046
## 12.096       4.293419
## 12.115       4.284675
## 12.135       4.278853
## 12.154       4.273410
## 12.173       4.267729
## 12.192       4.263267
## 12.212       4.258934
## 12.231       4.253012
## 12.250       4.249188
## 12.269       4.244352
## 12.288       4.239950
## 12.308       4.236713
## 12.327       4.233841
## 12.346       4.232289
## 12.365       4.234481
## 12.385       4.235757
## 12.404       4.238458
## 12.423       4.240957
## 12.442       4.243510
## 12.462       4.243425
## 12.481       4.242112
## 12.500       4.239682
## 12.519       4.241799
## 12.538       4.243426
## 12.558       4.247327
## 12.577       4.252581
## 12.596       4.258396
## 12.615       4.264530
## 12.635       4.269331
## 12.654       4.275099
## 12.673       4.279796
## 12.692       4.284063
## 12.712       4.287756
## 12.731       4.290346
## 12.750       4.290304
## 12.769       4.291506
## 12.788       4.290627
## 12.808       4.290296
## 12.827       4.287967
## 12.846       4.284356
## 12.865       4.282793
## 12.885       4.280409
## 12.904       4.278079
## 12.923       4.277478
## 12.942       4.273142
## 12.962       4.269181
## 12.981       4.266601

library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

## 
## Attaching package: 'GGally'

## The following object is masked from 'package:dplyr':
## 
##     nasa

## The following object is masked from 'package:fma':
## 
##     pigs

sj.ts %>%
  as.data.frame() %>%
  GGally::ggpairs()

## Don't know how to automatically pick scale for object of type ts. Defaulting to continuous.

train.sj.1<-train.sj
train.sj.1$year<-NULL
train.sj.1$weekofyear<-NULL
train.sj.1 %>%
as.data.frame() %>%
GGally::ggpairs()

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 191 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 205 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 195 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 195 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 194 rows containing missing values

## Warning: Removed 191 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 191 rows containing missing values

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 205 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 55 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 55 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 52 rows containing missing values

## Warning: Removed 49 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 49 rows containing missing values

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 25 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 25 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning: Removed 19 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 25 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 25 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values

## Warning: Removed 19 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 19 rows containing missing values

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 195 rows containing missing values (geom_point).

## Warning: Removed 55 rows containing missing values (geom_point).

## Warning: Removed 25 rows containing missing values (geom_point).

## Warning: Removed 25 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 195 rows containing missing values (geom_point).

## Warning: Removed 55 rows containing missing values (geom_point).

## Warning: Removed 25 rows containing missing values (geom_point).

## Warning: Removed 25 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 9 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 194 rows containing missing values (geom_point).

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing non-finite values (stat_density).

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 6 rows containing missing values

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 191 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 49 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 19 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 19 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 9 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 9 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 6 rows containing non-finite values (stat_bin).

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

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## Warning: Removed 6 rows containing missing values (geom_point).

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## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 191 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 19 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

train.iq.1<-train.iq
train.iq.1$year<-NULL
train.iq.1$weekofyear<-NULL

count.sj<-ggplot(data=train.sj.1,aes(x=train.sj.1$total_cases))
count.sj<-count.sj+geom_bar(stat="count")+xlab("Total Dengue Cases")
count.sj

## Warning: Use of `train.sj.1$total_cases` is discouraged. Use `total_cases`
## instead.

count.iq<-ggplot(data=train.iq.1,aes(x=train.iq.1$total_cases))
count.iq<-count.iq+geom_bar(stat="count")+xlab("Total Dengue Cases")
count.iq

## Warning: Use of `train.iq.1$total_cases` is discouraged. Use `total_cases`
## instead.

train.sj.2<-ts(train.sj.1,frequency=52)

## Warning in data.matrix(data): NAs introduced by coercion

tslm.SJ.1 <- tslm(total_cases~weekofyear+reanalysis_dew_point_temp_k+reanalysis_max_air_temp_k, data=train.sj.2)
coefficients(tslm.SJ.1)

##                 (Intercept)                  weekofyear 
##                -715.9606828                   0.8971327 
## reanalysis_dew_point_temp_k   reanalysis_max_air_temp_k 
##                   0.3493944                   2.0676910

accuracy(tslm.SJ.1)

##                        ME     RMSE      MAE  MPE MAPE      MASE     ACF1
## Training set 9.731015e-16 49.15302 27.11494 -Inf  Inf 0.7395291 0.958288

autoplot(train.sj.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.SJ.1), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))

train.iq.2<-ts(train.iq.1,frequency=52)

## Warning in data.matrix(data): NAs introduced by coercion

## Warning in data.matrix(data): NAs introduced by coercion

tslm.IQ.1 <- tslm(total_cases~weekofyear+reanalysis_dew_point_temp_k+reanalysis_max_air_temp_k, data=train.iq.2)
coefficients(tslm.IQ.1)

##                 (Intercept)                  weekofyear 
##               -5.089092e+02                7.999541e-03 
## reanalysis_dew_point_temp_k   reanalysis_max_air_temp_k 
##                1.758915e+00               -1.124429e-02

accuracy(tslm.IQ.1)

##                         ME     RMSE      MAE MPE MAPE      MASE      ACF1
## Training set -3.444989e-16 10.49556 6.422777 NaN  Inf 0.6794027 0.7102048

autoplot(train.iq.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.IQ.1), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))

tslm.sj.2<-tslm(total_cases~weekofyear+reanalysis_dew_point_temp_k+reanalysis_max_air_temp_k+station_avg_temp_c+station_precip_mm+year,data=train.sj.2)
coefficients(tslm.sj.2)

##                 (Intercept)                  weekofyear 
##               2162.99189108                  0.73714678 
## reanalysis_dew_point_temp_k   reanalysis_max_air_temp_k 
##                 -0.82416595                 10.95041794 
##          station_avg_temp_c           station_precip_mm 
##                 -5.04667903                  0.01906517 
##                        year 
##                 -2.53640747

autoplot(train.sj.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.sj.2), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))

tslm.iq.2<-tslm(total_cases~weekofyear+reanalysis_dew_point_temp_k+reanalysis_max_air_temp_k+station_avg_temp_c+station_precip_mm+year,data=train.iq.2)
coefficients(tslm.iq.2)

##                 (Intercept)                  weekofyear 
##               -1.394915e+03                9.959076e-03 
## reanalysis_dew_point_temp_k   reanalysis_max_air_temp_k 
##                1.227983e+00                7.960885e-03 
##          station_avg_temp_c           station_precip_mm 
##                4.424241e-01                6.979751e-03 
##                        year 
##                5.107205e-01

accuracy(tslm.iq.2)

##                        ME    RMSE      MAE MPE MAPE      MASE      ACF1
## Training set 1.338396e-16 9.08503 5.921679 NaN  Inf 0.6417092 0.6174369

autoplot(train.iq.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.iq.2), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))

tslm.sj.3<-tslm(total_cases~reanalysis_specific_humidity_g_per_kg+reanalysis_dew_point_temp_k+station_avg_temp_c+reanalysis_max_air_temp_k+weekofyear+year,data = train.sj.2)
coefficients(tslm.sj.3)

##                           (Intercept) reanalysis_specific_humidity_g_per_kg 
##                          8549.0911435                            23.2904557 
##           reanalysis_dew_point_temp_k                    station_avg_temp_c 
##                           -22.9561337                            -5.2926474 
##             reanalysis_max_air_temp_k                            weekofyear 
##                            10.1158117                             0.7135182 
##                                  year 
##                            -2.5268664

accuracy(tslm.sj.3)

##                         ME     RMSE      MAE  MPE MAPE      MASE      ACF1
## Training set -1.482868e-15 47.78175 26.06667 -Inf  Inf 0.7109388 0.9512268

autoplot(train.sj.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.iq.2), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))

tslm.iq.3<-tslm(total_cases~reanalysis_specific_humidity_g_per_kg+reanalysis_dew_point_temp_k+station_avg_temp_c+reanalysis_max_air_temp_k+weekofyear+year,data = train.iq.2)
coefficients(tslm.iq.3)

##                           (Intercept) reanalysis_specific_humidity_g_per_kg 
##                          1.362886e+03                          9.254170e+00 
##           reanalysis_dew_point_temp_k                    station_avg_temp_c 
##                         -8.121326e+00                          2.306411e-01 
##             reanalysis_max_air_temp_k                            weekofyear 
##                         -7.220113e-02                         -9.525546e-04 
##                                  year 
##                          4.497959e-01

accuracy(tslm.iq.3)

##                         ME     RMSE      MAE  MPE MAPE      MASE      ACF1
## Training set -1.097635e-16 9.038929 5.837813 -Inf  Inf 0.6357542 0.6264961

autoplot(train.iq.2[,'total_cases'], series="Data") +
autolayer(fitted(tslm.iq.2), series="Fitted") +
xlab("Year") + ylab("") +
ggtitle("Cases of Dengue") +
guides(colour=guide_legend(title=" "))