Chapter3modelling
Xiang Zhao
2023-05-22
knitr::opts_chunk$set(echo = TRUE)setwd("C:/Users/N11094427/OneDrive - Queensland University of Technology/Documents/ArcGIS/Projects/MaxENT/Data/OutputforR")
library(dplyr)
library(spatialEco)
library(raster)
library(rgdal)
library(MASS)
library(sf)
library(shapefiles)
library(blockCV)
library(gstat)
library(sp)
library(nlme)
library(leaflet)
library(biomod2)
library(ggplot2)
library(AppliedPredictiveModeling)
library(ROSE)
library(caret)
library(tidyverse)
library(ggpubr)
library(terra)
library(corrplot)
library(Hmisc)
library(PerformanceAnalytics)
library(rlang)
library(devtools)
library(plyr)
library(corpcor)
library(stats)
library(MASS)
library(GlmSimulatoR)
library(pscl)
library(AER)
library(MASS)
library(boot)
library(car)
library(mctest)
library(GGally)
library(purrr)
library(ggpmisc)
library(mgcv)
library(cowplot)
library(ggpmisc)
library(ggmap)
library(plotly)
library(mapdeck)
library(rayshader)
library(ROCR)
library(precrec)
library(sp)
library(geoR)
library(ncf)
library(visreg)
library(spdep)
library(ape)
library(leaps)
library(spNetwork)
library(rasterVis)
library(ggplot2)
library(classInt)
library(RStoolbox)
library(gridExtra)
library(openxlsx)
library(readxl)
library(yardstick)#import ice free area boundary
Antarctica <- st_read("ADD_Coastline_low_res_polygon.shp")## Reading layer `ADD_Coastline_low_res_polygon' from data source
## `C:\Users\N11094427\OneDrive - Queensland University of Technology\Documents\ArcGIS\Projects\MaxENT\Data\OutputforR\ADD_Coastline_low_res_polygon.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 592 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -2661733 ymin: -2489515 xmax: 2745916 ymax: 2321777
## Projected CRS: WGS 84 / Antarctic Polar Stereographic#import data frame
bioactivity <- read.csv("Fishnet5km_icefreeareabuffer500m_WGS84SSP_16012023.csv") # import the data table
bioactivity <- bioactivity %>% relocate(Countlandscapetype, .before = DEM_mean)
#add a column with the presence and absence of bioactivity
bioactivity$bioact_PA <- NA # create a new column with NA values
bioactivity$bioact_PA <- ifelse(bioactivity$Bioactivititycount == 0, 0, 1)
bioactivity <- bioactivity %>% relocate(bioact_PA, .before = Bioactivititycount)
#add a column with the presence and absence of non-bioactivity
bioactivity$Nonbioact_PA <- NA # create a new column with NA values
bioactivity$Nonbioact_PA <- ifelse(bioactivity$Nonbioactivitycount == 0, 0, 1)
bioactivity <- bioactivity %>% relocate(Nonbioact_PA, .before = Nonbioactivitycount)
#add a column for sloperecl
bioactivity$sloperecl <- NA # create a new column with NA values
bioactivity <- bioactivity %>% relocate(sloperecl, .after = SlopeMeanpercent)
# reclass slope 0-3%: little or none; 4-9%: Gentle; 10-15%: moderate; 16-30%: Steep; 31-60%: extremely steep; >60%: excessively steep https://sis.agr.gc.ca/cansis/nsdb/slc/v3.2/cmp/slope.html
bioactivity$sloperecl <- with(bioactivity, ifelse(SlopeMeanpercent > 60, 6,
ifelse(SlopeMeanpercent > 30 & SlopeMeanpercent <= 60, 5,
ifelse(SlopeMeanpercent > 15 & SlopeMeanpercent <= 30, 4,
ifelse(SlopeMeanpercent > 9 & SlopeMeanpercent <= 15, 3,
ifelse(SlopeMeanpercent > 3 & SlopeMeanpercent <= 9, 2, 1))))))
# reclass peak population
bioactivity$Peak_Popul_recl <- with(bioactivity, ifelse(Peak_Popul > 150 , 5,
ifelse(Peak_Popul > 50 & Peak_Popul <= 150, 4,
ifelse(Peak_Popul > 20 & Peak_Popul <= 50, 3,
ifelse(Peak_Popul > 10 & Peak_Popul <= 20, 2, 1)))))#modify data
#1 combine categoryof0 2 and 4 together, because they are the ture presence of biodiversity-related activity
# replace 4 with 2
bioactivity$Categoryof0[bioactivity$Categoryof0 == 4] <- 2
class(bioactivity$Categoryof0) = "integer"
bioactivity.60s <- subset(bioactivity, bioactivity$Latitude_WGS84 < -60.0) #only keep points south then 60s
summary(bioactivity.60s )## OID_ OID1 ID Longtitude_WGS84
## Min. : 1 Mode:logical Min. : 1 Min. :-179.940
## 1st Qu.: 5721 NA's:22880 1st Qu.: 5721 1st Qu.: -65.319
## Median :11440 Median :11440 Median : 3.788
## Mean :11440 Mean :11440 Mean : 23.655
## 3rd Qu.:17160 3rd Qu.:17160 3rd Qu.: 160.359
## Max. :22880 Max. :22880 Max. : 179.586
## Latitude_WGS84 POINT_X_SSP POINT_Y_SSP Windspeed
## Min. :-87.45 Min. :-2665635 Min. :-2488784 Min. : 1.230
## 1st Qu.:-78.07 1st Qu.:-1830635 1st Qu.:-1043783 1st Qu.: 4.754
## Median :-72.14 Median : 39366 Median : 521217 Median : 6.842
## Mean :-73.55 Mean : -361131 Mean : 92817 Mean : 7.292
## 3rd Qu.:-69.10 3rd Qu.: 479366 3rd Qu.: 1111218 3rd Qu.: 9.057
## Max. :-60.34 Max. : 2614367 Max. : 2326218 Max. :20.955
## Temp Precip bioact_PA Bioactivititycount
## Min. :-39.961 Min. : 0.791 Min. :0.00000 Min. : 0.000
## 1st Qu.:-24.916 1st Qu.: 129.244 1st Qu.:0.00000 1st Qu.: 0.000
## Median :-19.696 Median : 271.032 Median :0.00000 Median : 0.000
## Mean :-19.160 Mean : 609.732 Mean :0.08387 Mean : 2.974
## 3rd Qu.:-13.679 3rd Qu.: 644.410 3rd Qu.:0.00000 3rd Qu.: 0.000
## Max. : -1.436 Max. :6500.416 Max. :1.00000 Max. :2865.000
## Nonbioact_PA Nonbioactivitycount Categoryof0 Countlandscapetype
## Min. :0.0000 Min. : 0.000 Min. :1.000 Min. :1.00
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:1.000 1st Qu.:2.00
## Median :0.0000 Median : 0.000 Median :3.000 Median :2.00
## Mean :0.3477 Mean : 4.258 Mean :2.345 Mean :2.05
## 3rd Qu.:1.0000 3rd Qu.: 2.000 3rd Qu.:3.000 3rd Qu.:2.00
## Max. :1.0000 Max. :3169.000 Max. :3.000 Max. :5.00
## DEM_mean SlopeMeanpercent sloperecl Edgedensitity_mperkm2
## Min. : -58.05 Min. : 0.00043 Min. :1.000 Min. : 0.001026
## 1st Qu.: 328.51 1st Qu.: 3.93691 1st Qu.:2.000 1st Qu.: 0.122701
## Median : 969.32 Median : 8.74753 Median :2.000 Median : 0.335598
## Mean :1006.16 Mean : 12.45425 Mean :2.711 Mean : 0.584848
## 3rd Qu.:1552.47 3rd Qu.: 18.15785 3rd Qu.:4.000 3rd Qu.: 0.762789
## Max. :4467.39 Max. :134.71022 Max. :6.000 Max. :12.824759
## sum_Area_km2 IcefreePolygon_Count IcefreepolygonMeanSize_km2
## Min. : 0.00000 Min. : 0.00 Min. : 0.00000
## 1st Qu.: 0.01884 1st Qu.: 2.00 1st Qu.: 0.00352
## Median : 0.21415 Median : 7.00 Median : 0.01666
## Mean : 1.38305 Mean : 17.06 Mean : 0.28076
## 3rd Qu.: 1.18745 3rd Qu.: 22.00 3rd Qu.: 0.08441
## Max. :30.31898 Max. :604.00 Max. :26.25244
## TRI_Mean ClaimCountry_Count Country_1 Country_2
## Min. : 0.0037 Min. :0.000 Length:22880 Length:22880
## 1st Qu.: 9.7357 1st Qu.:1.000 Class :character Class :character
## Median : 21.3252 Median :1.000 Mode :character Mode :character
## Mean : 30.6591 Mean :1.597
## 3rd Qu.: 43.8652 3rd Qu.:3.000
## Max. :523.7536 Max. :3.000
## Country_3 GNI_Claim PPP_Claim Feb_Seaice_median_km
## Length:22880 Min. :1.096e+11 Min. : 0.6922 Min. : 0.0
## Class :character 1st Qu.:1.096e+11 1st Qu.: 1.4725 1st Qu.: 0.0
## Mode :character Median :7.876e+11 Median : 1.6579 Median : 0.0
## Mean :6.071e+11 Mean : 46.3605 Mean : 119.4
## 3rd Qu.:8.904e+11 3rd Qu.:104.4277 3rd Qu.: 133.0
## Max. :2.191e+12 Max. :309.9673 Max. :1371.0
## Sept_Seaice_median_km Mainshippiingzone Shiptrafficintensity Penornot
## Min. : 0.0 Min. :0.0000 Min. : 0.00 Min. :0.0000
## 1st Qu.: 610.0 1st Qu.:1.0000 1st Qu.: 0.00 1st Qu.:0.0000
## Median : 890.0 Median :1.0000 Median : 0.00 Median :0.0000
## Mean : 910.9 Mean :0.8211 Mean : 45.89 Mean :0.3104
## 3rd Qu.:1192.0 3rd Qu.:1.0000 3rd Qu.: 75.00 3rd Qu.:1.0000
## Max. :2284.0 Max. :1.0000 Max. :150.00 Max. :1.0000
## Travelspeedkmhour Dist_Claimline_km Dist_Claim_recl Dist_air_km
## Min. : 0.2325 Min. : 0.0 Min. :1.000 Min. : 0.00
## 1st Qu.: 1.1496 1st Qu.: 85.0 1st Qu.:2.000 1st Qu.: 85.59
## Median : 1.3261 Median : 217.3 Median :3.000 Median :191.64
## Mean : 2.9080 Mean : 284.5 Mean :2.545 Mean :218.90
## 3rd Qu.: 1.5078 3rd Qu.: 378.0 3rd Qu.:3.000 3rd Qu.:332.42
## Max. :19.5634 Max. :1781.9 Max. :3.000 Max. :901.14
## Dist_air_recl Dis_traverseskm Dist_travese_recl Dist_port_km
## Min. :1.000 Min. : 0.0 Min. :1.000 Min. : 1.487
## 1st Qu.:1.000 1st Qu.: 330.9 1st Qu.:3.000 1st Qu.: 67.633
## Median :1.000 Median : 731.2 Median :3.000 Median : 228.119
## Mean :1.445 Mean : 999.1 Mean :2.879 Mean : 303.352
## 3rd Qu.:2.000 3rd Qu.:1869.4 3rd Qu.:3.000 3rd Qu.: 491.373
## Max. :3.000 Max. :2459.7 Max. :3.000 Max. :1155.318
## Dist_port_recl Dist_station_km Dist_station_recl GNI_station
## Min. :1.000 Min. : 1.497 Min. :1.000 Min. :3.056e+10
## 1st Qu.:2.000 1st Qu.: 95.565 1st Qu.:2.000 1st Qu.:3.375e+11
## Median :3.000 Median :249.462 Median :3.000 Median :9.663e+11
## Mean :2.478 Mean :278.435 Mean :2.602 Mean :4.188e+12
## 3rd Qu.:3.000 3rd Qu.:431.421 3rd Qu.:3.000 3rd Qu.:1.303e+13
## Max. :3.000 Max. :920.636 Max. :3.000 Max. :1.303e+13
## PPP_station Record_ID_ English_Na Official_N
## Min. : 0.1536 Min. : 1.00 Length:22880 Length:22880
## 1st Qu.: 1.0000 1st Qu.: 15.00 Class :character Class :character
## Median : 1.3900 Median : 45.00 Mode :character Mode :character
## Mean : 11.3670 Mean : 45.52
## 3rd Qu.: 2.6237 3rd Qu.: 71.00
## Max. :771.1880 Max. :236.00
## Operator_1 Operator_2 Type Seasonalit
## Length:22880 Length:22880 Length:22880 Length:22880
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Status Year_Estab Antarctic Latitude_stationWGS84
## Length:22880 Min. : 0 Length:22880 Min. :-90.00
## Class :character 1st Qu.:1956 Class :character 1st Qu.:-77.85
## Mode :character Median :1971 Mode :character Median :-70.77
## Mean :1967 Mean :-72.85
## 3rd Qu.:1987 3rd Qu.:-68.13
## Max. :2012 Max. :-60.71
## Longitude_stationWGS84 Peak_Popul Landtrans Seatrans
## Min. :-149.00 Min. : 0.0 Min. :0.0000 Min. :0.0000
## 1st Qu.: -67.10 1st Qu.: 20.0 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 23.35 Median : 24.0 Median :0.0000 Median :0.0000
## Mean : 30.54 Mean : 169.9 Mean :0.4722 Mean :0.2956
## 3rd Qu.: 159.39 3rd Qu.: 70.0 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. : 166.77 Max. :1200.0 Max. :1.0000 Max. :1.0000
## Airstrap Flights Ships Helipad
## Min. :0.0000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000
## Median :1.0000 Median : 0.000 Median : 1.000 Median :1.0000
## Mean :0.5844 Mean : 2.396 Mean : 3.823 Mean :0.5876
## 3rd Qu.:1.0000 3rd Qu.: 5.000 3rd Qu.: 2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :60.000 Max. :100.000 Max. :1.0000
## ikbioactf1under0 ikbioactf2under0 DistancetoSea Peak_Popul_recl
## Min. :0.0000 Min. :0.00000 Min. : 0 Min. :1.000
## 1st Qu.:0.1176 1st Qu.:0.05577 1st Qu.: 20616 1st Qu.:2.000
## Median :0.2856 Median :0.24743 Median : 85000 Median :3.000
## Mean :0.3434 Mean :0.33683 Mean : 209517 Mean :3.085
## 3rd Qu.:0.5261 3rd Qu.:0.55552 3rd Qu.: 289935 3rd Qu.:4.000
## Max. :1.0000 Max. :1.00000 Max. :1061567 Max. :5.000#convert some categorical variables to factor
#bioactivity.60s$bioact_PA <- factor(bioactivity.60s$bioact_PA)
bioactivity.60s$Nonbioact_PA <- factor(bioactivity.60s$Nonbioact_PA)
bioactivity.60s$Categoryof0 <- factor(bioactivity.60s$Categoryof0)
bioactivity.60s$Countlandscapetype <- factor(bioactivity.60s$Countlandscapetype)
bioactivity.60s$sloperecl <- factor(bioactivity.60s$sloperecl)
bioactivity.60s$ClaimCountry_Count <- factor(bioactivity.60s$ClaimCountry_Count)
bioactivity.60s$Mainshippiingzone <- factor(bioactivity.60s$Mainshippiingzone)
bioactivity.60s$Shiptrafficintensity <- factor(bioactivity.60s$Shiptrafficintensity)
bioactivity.60s$Penornot <- factor(bioactivity.60s$Penornot)
bioactivity.60s$Dist_Claim_recl <- factor(bioactivity.60s$Dist_Claim_recl)
bioactivity.60s$Dist_air_recl <- factor(bioactivity.60s$Dist_air_recl)
bioactivity.60s$Dist_travese_recl <- factor(bioactivity.60s$Dist_travese_recl)
bioactivity.60s$Dist_port_recl <- factor(bioactivity.60s$Dist_port_recl)
bioactivity.60s$Dist_station_recl <- factor(bioactivity.60s$Dist_station_recl)
bioactivity.60s$Peak_Popul_recl <- factor(bioactivity.60s$Peak_Popul_recl)
#data round
bioactivity.60s$DEM_mean <- round(bioactivity.60s$DEM_mean)
bioactivity.60s$SlopeMeanpercent <- round(bioactivity.60s$SlopeMeanpercent)
# data scale
bioactivity.60s$sc.Windspeed <- c(scale(bioactivity.60s$Windspeed))
bioactivity.60s$sc.Temp <- c(scale(bioactivity.60s$Temp))
bioactivity.60s$sc.logPrecip <- c(scale(log(bioactivity.60s$Precip)))
bioactivity.60s$sc.Lon <- c(scale(bioactivity.60s$Longtitude_WGS84))
bioactivity.60s$sc.Lat <- c(scale(bioactivity.60s$Latitude_WGS84))
# bioactivity.60s$sc.Nonbioact_PA <- c(scale(bioactivity.60s$Nonbioact_PA))
bioactivity.60s$sc.log1p.Nonbioactcount <- c(scale(log1p(bioactivity.60s$Nonbioactivitycount)))
bioactivity.60s$sc.DEM.100 <- c(scale((bioactivity.60s$DEM_mean)/100))
bioactivity.60s$sc.SlopeMeanpercent <- c(scale(bioactivity.60s$SlopeMeanpercent))
# bioactivity.60s$sc.sloperecl <- c(scale(bioactivity.60s$sloperecl))
bioactivity.60s$sc.sqrt.Edgedensity <- c(scale(sqrt(bioactivity.60s$Edgedensitity_mperkm2)))
bioactivity.60s$sc.log1p.sumArea <- c(scale(log1p(bioactivity.60s$sum_Area_km2)))
bioactivity.60s$sc.log1p.IcefreePolygon_Count <- c(scale(log1p(bioactivity.60s$IcefreePolygon_Count)))
bioactivity.60s$sc.sqrt.IcefreepolygonMeanSize <- c(scale(sqrt(bioactivity.60s$IcefreepolygonMeanSize_km2)))
# bioactivity.60s$sc.ClaimCountry_Count <- c(scale(bioactivity.60s$ClaimCountry_Count))
bioactivity.60s$sc.log.GNI_Claim <- c(scale(log(bioactivity.60s$GNI_Claim)))
bioactivity.60s$sc.log.PPP_Claim <- c(scale(log(bioactivity.60s$PPP_Claim)))
bioactivity.60s$sc.log1p.Feb_Seaice_median_km <- c(scale(log1p(bioactivity.60s$Feb_Seaice_median_km)))
bioactivity.60s$sc.log1p.Sept_Seaice_median_km <- c(scale(log1p(bioactivity.60s$Sept_Seaice_median_km)))
# bioactivity.60s$sc.Mainshippiingzone <- c(scale((bioactivity.60s$Mainshippiingzone)))
# bioactivity.60s$sc.Shiptrafficintensity <- c(scale((bioactivity.60s$Shiptrafficintensity)))
# bioactivity.60s$sc.Penornot <- c(scale((bioactivity.60s$Penornot)))
bioactivity.60s$sc.log.Travelspeedkmhour <- c(scale(log(bioactivity.60s$Travelspeedkmhour)))
bioactivity.60s$sc.sqrt.Dist_Claimline_km <- c(scale(sqrt(bioactivity.60s$Dist_Claimline_km)))
# bioactivity.60s$sc.Dist_Claim_recl <- c(scale(bioactivity.60s$Dist_Claim_recl))
bioactivity.60s$sc.sqrt.Dist_air_km <- c(scale(sqrt(bioactivity.60s$Dist_air_km)))
# bioactivity.60s$sc.Dist_air_recl <- c(scale(bioactivity.60s$Dist_air_recl))
bioactivity.60s$sc.sqrt.Dist_port_km <- c(scale(sqrt(bioactivity.60s$Dist_port_km)))
# bioactivity.60s$sc.Dist_port_recl <- c(scale(bioactivity.60s$Dist_port_recl))
bioactivity.60s$sc.sqrt.Dis_traverseskm <- c(scale(sqrt(bioactivity.60s$Dis_traverseskm)))
# bioactivity.60s$sc.Dist_travese_recl <- c(scale(bioactivity.60s$Dist_travese_recl))
bioactivity.60s$sc.sqrt.Dist_station_km <- c(scale(sqrt(bioactivity.60s$Dist_station_km)))
# bioactivity.60s$sc.Dist_station_recl <- c(scale(bioactivity.60s$Dist_station_recl))
bioactivity.60s$sc.log.GNI_station <- c(scale(log(bioactivity.60s$GNI_station)))
bioactivity.60s$sc.log.PPP_station <- c(scale(log(bioactivity.60s$PPP_station)))
bioactivity.60s$sc.sqrt.Peak_Popul <- c(scale(sqrt(bioactivity.60s$Peak_Popul)))
bioactivity.60s$sc.sqrt.TRI_Mean <- c(scale(sqrt(bioactivity.60s$TRI_Mean)))
bioactivity.60s$sc.sqrt.DistancetoSea <- scale(sqrt(bioactivity.60s$DistancetoSea))
#Pen or notPen
Pen <- subset(bioactivity.60s, bioactivity.60s$Penornot == 1)
Not_Pen <- subset(bioactivity.60s, bioactivity.60s$Penornot == 0)
#subset1: random zeros and true presence;
#subset2: structural zero
#subset3: true presence
#subset4: random zeros
subset1 <- subset(bioactivity.60s, bioactivity.60s$Categoryof0 == 1 | bioactivity.60s$Categoryof0 == 2)
subset2 <- subset(bioactivity.60s, bioactivity.60s$Categoryof0 == 3)
subset3 <- subset(bioactivity.60s, bioactivity.60s$Categoryof0 == 2)
subset4 <- subset(bioactivity.60s, bioactivity.60s$Categoryof0 == 1)# Mapping our data
bioactivity.60s.forSAC <- bioactivity.60s
names(bioactivity.60s.forSAC)## [1] "OID_" "OID1"
## [3] "ID" "Longtitude_WGS84"
## [5] "Latitude_WGS84" "POINT_X_SSP"
## [7] "POINT_Y_SSP" "Windspeed"
## [9] "Temp" "Precip"
## [11] "bioact_PA" "Bioactivititycount"
## [13] "Nonbioact_PA" "Nonbioactivitycount"
## [15] "Categoryof0" "Countlandscapetype"
## [17] "DEM_mean" "SlopeMeanpercent"
## [19] "sloperecl" "Edgedensitity_mperkm2"
## [21] "sum_Area_km2" "IcefreePolygon_Count"
## [23] "IcefreepolygonMeanSize_km2" "TRI_Mean"
## [25] "ClaimCountry_Count" "Country_1"
## [27] "Country_2" "Country_3"
## [29] "GNI_Claim" "PPP_Claim"
## [31] "Feb_Seaice_median_km" "Sept_Seaice_median_km"
## [33] "Mainshippiingzone" "Shiptrafficintensity"
## [35] "Penornot" "Travelspeedkmhour"
## [37] "Dist_Claimline_km" "Dist_Claim_recl"
## [39] "Dist_air_km" "Dist_air_recl"
## [41] "Dis_traverseskm" "Dist_travese_recl"
## [43] "Dist_port_km" "Dist_port_recl"
## [45] "Dist_station_km" "Dist_station_recl"
## [47] "GNI_station" "PPP_station"
## [49] "Record_ID_" "English_Na"
## [51] "Official_N" "Operator_1"
## [53] "Operator_2" "Type"
## [55] "Seasonalit" "Status"
## [57] "Year_Estab" "Antarctic"
## [59] "Latitude_stationWGS84" "Longitude_stationWGS84"
## [61] "Peak_Popul" "Landtrans"
## [63] "Seatrans" "Airstrap"
## [65] "Flights" "Ships"
## [67] "Helipad" "ikbioactf1under0"
## [69] "ikbioactf2under0" "DistancetoSea"
## [71] "Peak_Popul_recl" "sc.Windspeed"
## [73] "sc.Temp" "sc.logPrecip"
## [75] "sc.Lon" "sc.Lat"
## [77] "sc.log1p.Nonbioactcount" "sc.DEM.100"
## [79] "sc.SlopeMeanpercent" "sc.sqrt.Edgedensity"
## [81] "sc.log1p.sumArea" "sc.log1p.IcefreePolygon_Count"
## [83] "sc.sqrt.IcefreepolygonMeanSize" "sc.log.GNI_Claim"
## [85] "sc.log.PPP_Claim" "sc.log1p.Feb_Seaice_median_km"
## [87] "sc.log1p.Sept_Seaice_median_km" "sc.log.Travelspeedkmhour"
## [89] "sc.sqrt.Dist_Claimline_km" "sc.sqrt.Dist_air_km"
## [91] "sc.sqrt.Dist_port_km" "sc.sqrt.Dis_traverseskm"
## [93] "sc.sqrt.Dist_station_km" "sc.log.GNI_station"
## [95] "sc.log.PPP_station" "sc.sqrt.Peak_Popul"
## [97] "sc.sqrt.TRI_Mean" "sc.sqrt.DistancetoSea"coordinates(bioactivity.60s.forSAC) = ~ POINT_X_SSP + POINT_Y_SSP
crs(bioactivity.60s.forSAC) <- CRS("+proj=stere +lat_0=-90 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs")
bioactivity.60s.forSAC <- st_as_sf(bioactivity.60s.forSAC)
Antarctica <- st_transform(Antarctica, crs=crs(bioactivity.60s.forSAC))Visualize data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Windspeed), size = 0.4)+
labs(color = "Wind speed (km/h)")
ggplot(bioactivity.60s, aes(x = sc.Windspeed)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled wind speed")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Temp), size = 0.4)+
scale_color_gradient(low="blue", high="orange")+
labs(color = "Temperature (Celsius)")
ggplot(bioactivity.60s, aes(x = sc.Temp)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Temperture")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Precip)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Precip (?)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Precip), size = 0.4)+
scale_color_gradient(low="grey", high="green")+
labs(color = "Precipitation (?)")
ggplot(bioactivity.60s, aes(x = sc.logPrecip)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Precipitation")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Bioactivititycount)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Bioactivitity counts")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Nonbioactivitycount)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Non-biodiversity related activity counts")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Nonbioactivitycount), size = 0.4)+
scale_color_gradient(low="grey", high="red")+
labs(color = "Non-biodiversity related activity counts")
ggplot(bioactivity.60s, aes(x = sc.log1p.Nonbioactcount)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Non-biodiversity related activity counts")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Countlandscapetype)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Landscape types")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Countlandscapetype), size = 0.4)+
labs(color = "Landscape types")
ggplot(bioactivity.60s, aes(x = DEM_mean)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "DEM (m)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=DEM_mean), size = 0.4)+
scale_color_gradient(low="black", high="grey")+
labs(color = "DEM (m)")
ggplot(bioactivity.60s, aes(x = sc.DEM.100)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled DEM")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = SlopeMeanpercent)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Slope (%)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=SlopeMeanpercent), size = 0.4)+
scale_color_gradient(low="light green", high="red")+
labs(color = "Slope (%)")
ggplot(bioactivity.60s, aes(x = sc.SlopeMeanpercent)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Slope (%)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = sloperecl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Slope (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=sloperecl), size = 0.4)+
labs(color = "Slope (reclassified)")
ggplot(bioactivity.60s, aes(x = Edgedensitity_mperkm2)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = expression(paste("Edge density ", "(", m/Km^{2},")")))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Edgedensitity_mperkm2), size = 0.4)+
scale_color_gradient(low="light blue", high="black")+
labs(color = expression(paste("Edge density ", "(", m/Km^{2},")")))
ggplot(bioactivity.60s, aes(x = sc.sqrt.Edgedensity)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Edge Density")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = sum_Area_km2)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = expression(paste("Area of ice free", " (", Km^{2},")")))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=sum_Area_km2), size = 0.4)+
scale_color_gradient(low="light blue", high="black")+
labs(color = expression(paste("Area of ice free", " (", Km^{2},")")))
ggplot(bioactivity.60s, aes(x = sc.log1p.sumArea)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled Area (Ice free)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = IcefreePolygon_Count)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Number of ice free patch")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=IcefreePolygon_Count), size = 0.4)+
scale_color_gradient(low="light blue", high="black")+
labs(color = "Number of ice free patch")
ggplot(bioactivity.60s, aes(x = sc.log1p.IcefreePolygon_Count)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled count of ice free patches")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = IcefreepolygonMeanSize_km2)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = expression(paste("Mean size of ice free patch", " (", Km^{2},")")))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=IcefreepolygonMeanSize_km2), size = 0.4)+
scale_color_gradient(low="light blue", high="black")+
labs(color = expression(paste("Mean size of ice free patch", " (", Km^{2},")")))
ggplot(bioactivity.60s, aes(x = sc.sqrt.IcefreepolygonMeanSize)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled mean size of ice free patches")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = TRI_Mean)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Topographic ruggedness index")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=TRI_Mean), size = 0.4)+
scale_color_gradient(low="blue", high="red")+
labs(color = "Topographic ruggedness index")
ggplot(bioactivity.60s, aes(x = sc.sqrt.TRI_Mean)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled topographic ruggedness index")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = ClaimCountry_Count)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Number of Claiming Country")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=ClaimCountry_Count), size = 0.4)+
labs(color = "Number of Claiming Country")
ggplot(bioactivity.60s, aes(x = GNI_Claim)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "GNI of the claiming country")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=GNI_Claim), size = 0.4)+
labs(color = "GNI of the claiming country")
ggplot(bioactivity.60s, aes(x = sc.log.GNI_Claim)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Sclaed GNI of the claiming country")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = PPP_Claim)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "PPP of the claiming country")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=PPP_Claim), size = 0.4)+
labs(color = "PPP of the claiming country")
ggplot(bioactivity.60s, aes(x = sc.log.PPP_Claim)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled PPP of the claiming country")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Feb_Seaice_median_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Extent of sea ice in Feb (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Feb_Seaice_median_km), size = 0.4)+
labs(color = "Extent of sea ice in Feb (Km)")
ggplot(bioactivity.60s, aes(x = sc.log1p.Feb_Seaice_median_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Sclaed extent of sea ice in Fed")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Sept_Seaice_median_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Extent of sea ice in Sept (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Sept_Seaice_median_km), size = 0.4)+
labs(color = "Extent of sea ice in Sept (Km)")
ggplot(bioactivity.60s, aes(x = sc.log1p.Sept_Seaice_median_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Sclaed extent of sea ice in Sept")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Mainshippiingzone)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Mainshippiing zone")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Mainshippiingzone), size = 0.4)+
labs(color = "Mainshippiing zone")
ggplot(bioactivity.60s, aes(x = Shiptrafficintensity)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Ship traffic intensity")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Shiptrafficintensity), size = 0.4)+
labs(color = "Ship traffic intensity")
ggplot(bioactivity.60s, aes(x = Penornot)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Penornot")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Penornot), size = 0.4)+
labs(color = "Pen or not")
ggplot(bioactivity.60s, aes(x = Travelspeedkmhour)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Travel speed (Km/hour)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Travelspeedkmhour), size = 0.4)+
labs(color = "Travel speed (Km/hour)")
ggplot(bioactivity.60s, aes(x = sc.log.Travelspeedkmhour)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scled travel speed")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s, aes(x = Dist_Claimline_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to claiming line (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_Claimline_km), size = 0.4)+
labs(color = "Distance to claiming line (Km)")
ggplot(bioactivity.60s, aes(x = Dist_Claim_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to claiming line (reclassfied)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_Claim_recl), size = 0.4)+
labs(color = "Distance to claiming line (reclassifed)")
ggplot(bioactivity.60s, aes(x = Dist_air_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to airport (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_air_km), size = 0.4)+
labs(color = "Distance to airport (Km)")
ggplot(bioactivity.60s, aes(x = Dist_air_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to airport (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color= Dist_air_recl), size = 0.4)+
labs(color = "Distance to airport (reclassified)")
ggplot(bioactivity.60s, aes(x = Dis_traverseskm)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to traveses (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color= Dis_traverseskm), size = 0.4)+
labs(color = "Distance to traveses (Km)")
ggplot(bioactivity.60s, aes(x = Dist_travese_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to traveses (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color= Dist_travese_recl), size = 0.4)+
labs(color = "Distance to traveses (reclassified)")
ggplot(bioactivity.60s, aes(x = Dist_port_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to port (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color= Dist_port_km), size = 0.4)+
labs(color = "Distance to port (Km)")
ggplot(bioactivity.60s, aes(x = Dist_port_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to port (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_port_recl), size = 0.4)+
labs(color = "Distance to port (reclassified)")
ggplot(bioactivity.60s, aes(x = Dist_station_km)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to station (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_station_km), size = 0.4)+
labs(color = "Distance to station (km)")
ggplot(bioactivity.60s, aes(x = Dist_station_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to station (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Dist_station_recl), size = 0.4)+
labs(color = "Distance to station (reclassified)")
ggplot(bioactivity.60s, aes(x = Peak_Popul)) +
geom_bar(fill = "white", colour = "black")+
xlim(c(0,250))+
ggtitle("Histogram")+
labs(y= "Count", x = "Peak population of the closest station")## Warning: Removed 2505 rows containing non-finite values (`stat_count()`).## Warning: Removed 1 rows containing missing values (`geom_bar()`).
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Peak_Popul), size = 0.4)+
labs(color = "Peak population of the closest station")
ggplot(bioactivity.60s, aes(x = Peak_Popul_recl)) +
geom_bar(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Peak population of the closest station (reclassified)")
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color=Peak_Popul_recl), size = 0.4)+
labs(color = "Peak population of the closest station (reclassified)")
ggplot(bioactivity.60s, aes(x = DistancetoSea)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Distance to sea (Km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(bioactivity.60s.forSAC) +
geom_sf(aes(color= DistancetoSea), size = 0.4)+
labs(color = "Distance to sea (Km)")
ggplot(bioactivity.60s, aes(x =sc.sqrt.DistancetoSea)) +
geom_histogram(fill = "white", colour = "black")+
ggtitle("Histogram")+
labs(y= "Count", x = "Scaled distance to sea")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
correlation test of variables
str(bioactivity.60s)## 'data.frame': 22880 obs. of 98 variables:
## $ OID_ : int 1 2 3 4 5 6 7 8 9 10 ...
## $ OID1 : logi NA NA NA NA NA NA ...
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Longtitude_WGS84 : num 162 162 163 163 163 ...
## $ Latitude_WGS84 : num -66.3 -66.3 -66.5 -66.5 -66.5 ...
## $ POINT_X_SSP : num 789366 794366 769366 764366 769366 ...
## $ POINT_Y_SSP : num -2488784 -2488784 -2473784 -2468784 -2468784 ...
## $ Windspeed : num 7.82 7.85 7.54 7.55 7.53 ...
## $ Temp : num -9.3 -9.38 -9.29 -9.34 -9.38 ...
## $ Precip : num 1081 1109 858 833 842 ...
## $ bioact_PA : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Bioactivititycount : int 0 0 0 0 0 0 0 0 1 0 ...
## $ Nonbioact_PA : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ Nonbioactivitycount : int 0 0 0 0 0 0 2 0 0 0 ...
## $ Categoryof0 : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 1 3 2 3 ...
## $ Countlandscapetype : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ DEM_mean : num 151 597 -47 -42 -45 -48 -37 -46 -48 65 ...
## $ SlopeMeanpercent : num 18 29 0 1 1 0 3 1 0 17 ...
## $ sloperecl : Factor w/ 6 levels "1","2","3","4",..: 4 4 1 1 1 1 1 1 1 4 ...
## $ Edgedensitity_mperkm2 : num 0.0726 0.0766 0.0951 0.1041 0.1345 ...
## $ sum_Area_km2 : num 1.47 0.36 1.564 0.139 0.942 ...
## $ IcefreePolygon_Count : int 1 2 1 1 2 1 1 1 1 1 ...
## $ IcefreepolygonMeanSize_km2 : num 1.47 0.18 1.564 0.139 0.471 ...
## $ TRI_Mean : num 48.368 66.632 0.317 3.452 1.964 ...
## $ ClaimCountry_Count : Factor w/ 4 levels "0","1","2","3": 2 2 2 2 2 2 2 2 2 2 ...
## $ Country_1 : chr "New_Zealand" "New_Zealand" "New_Zealand" "New_Zealand" ...
## $ Country_2 : chr "" "" "" "" ...
## $ Country_3 : chr "" "" "" "" ...
## $ GNI_Claim : num 1.1e+11 1.1e+11 1.1e+11 1.1e+11 1.1e+11 ...
## $ PPP_Claim : num 1.47 1.47 1.47 1.47 1.47 ...
## $ Feb_Seaice_median_km : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Sept_Seaice_median_km : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Mainshippiingzone : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Shiptrafficintensity : Factor w/ 4 levels "0","7","75","150": 1 1 1 1 1 1 1 1 1 1 ...
## $ Penornot : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Travelspeedkmhour : num 14.27 7.93 19.54 19.01 19.18 ...
## $ Dist_Claimline_km : num 108 103 122 125 120 ...
## $ Dist_Claim_recl : Factor w/ 3 levels "1","2","3": 2 2 2 2 2 2 2 2 3 3 ...
## $ Dist_air_km : num 817 816 810 807 805 ...
## $ Dist_air_recl : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
## $ Dis_traverseskm : num 1008 1003 1017 1019 1015 ...
## $ Dist_travese_recl : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
## $ Dist_port_km : num 151 156 126 120 124 ...
## $ Dist_port_recl : Factor w/ 3 levels "1","2","3": 3 3 2 2 2 2 2 2 2 2 ...
## $ Dist_station_km : num 382 382 368 363 363 ...
## $ Dist_station_recl : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
## $ GNI_station : num 9.66e+11 9.66e+11 9.66e+11 9.66e+11 9.66e+11 ...
## $ PPP_station : num 11.1 11.1 11.1 11.1 11.1 ...
## $ Record_ID_ : int 70 70 70 70 70 70 70 70 70 70 ...
## $ English_Na : chr "Leningradskaya" "Leningradskaya" "Leningradskaya" "Leningradskaya" ...
## $ Official_N : chr "Leningradskaya" "Leningradskaya" "Leningradskaya" "Leningradskaya" ...
## $ Operator_1 : chr "Russia" "Russia" "Russia" "Russia" ...
## $ Operator_2 : chr "" "" "" "" ...
## $ Type : chr "Station" "Station" "Station" "Station" ...
## $ Seasonalit : chr "Seasonal" "Seasonal" "Seasonal" "Seasonal" ...
## $ Status : chr "Temporarily Closed" "Temporarily Closed" "Temporarily Closed" "Temporarily Closed" ...
## $ Year_Estab : int 1971 1971 1971 1971 1971 1971 1971 1971 1971 1971 ...
## $ Antarctic : chr "East Antarctica" "East Antarctica" "East Antarctica" "East Antarctica" ...
## $ Latitude_stationWGS84 : num -69.5 -69.5 -69.5 -69.5 -69.5 ...
## $ Longitude_stationWGS84 : num 159 159 159 159 159 ...
## $ Peak_Popul : int 10 10 10 10 10 10 10 10 10 10 ...
## $ Landtrans : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Seatrans : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Airstrap : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Flights : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Ships : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Helipad : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ikbioactf1under0 : num 1 0.87 1 1 1 ...
## $ ikbioactf2under0 : num 0.787 0.787 0.5 0.5 0.5 ...
## $ DistancetoSea : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Peak_Popul_recl : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sc.Windspeed : num 0.1589 0.1676 0.0747 0.0757 0.0711 ...
## $ sc.Temp : num 1.28 1.27 1.28 1.27 1.27 ...
## $ sc.logPrecip : num 1.02 1.039 0.848 0.826 0.834 ...
## $ sc.Lon : num 1.24 1.24 1.24 1.24 1.24 ...
## $ sc.Lat : num 1.12 1.13 1.1 1.09 1.09 ...
## $ sc.log1p.Nonbioactcount : num -0.59 -0.59 -0.59 -0.59 -0.59 ...
## $ sc.DEM.100 : num -1.15 -0.55 -1.42 -1.41 -1.41 ...
## $ sc.SlopeMeanpercent : num 0.486 1.449 -1.091 -1.003 -1.003 ...
## $ sc.sqrt.Edgedensity : num -0.951 -0.933 -0.855 -0.819 -0.709 ...
## $ sc.log1p.sumArea : num 0.552 -0.305 0.605 -0.56 0.206 ...
## $ sc.log1p.IcefreePolygon_Count : num -1.045 -0.738 -1.045 -1.045 -0.738 ...
## $ sc.sqrt.IcefreepolygonMeanSize: num 2.07 0.328 2.154 0.215 0.907 ...
## $ sc.log.GNI_Claim : num -1.37 -1.37 -1.37 -1.37 -1.37 ...
## $ sc.log.PPP_Claim : num -0.916 -0.916 -0.916 -0.916 -0.916 ...
## $ sc.log1p.Feb_Seaice_median_km : num -0.651 -0.651 -0.651 -0.651 -0.651 ...
## $ sc.log1p.Sept_Seaice_median_km: num -3.03 -3.03 -3.03 -3.03 -3.03 ...
## $ sc.log.Travelspeedkmhour : num 2.58 1.88 2.96 2.93 2.94 ...
## $ sc.sqrt.Dist_Claimline_km : num -0.611 -0.64 -0.525 -0.506 -0.534 ...
## $ sc.sqrt.Dist_air_km : num 2.71 2.71 2.69 2.68 2.67 ...
## $ sc.sqrt.Dist_port_km : num -0.364 -0.343 -0.492 -0.529 -0.506 ...
## $ sc.sqrt.Dis_traverseskm : num 0.231 0.226 0.242 0.244 0.239 ...
## $ sc.sqrt.Dist_station_km : num 0.637 0.637 0.582 0.564 0.562 ...
## $ sc.log.GNI_station : num -0.147 -0.147 -0.147 -0.147 -0.147 ...
## $ sc.log.PPP_station : num 1.23 1.23 1.23 1.23 1.23 ...
## $ sc.sqrt.Peak_Popul : num -0.642 -0.642 -0.642 -0.642 -0.642 ...
## $ sc.sqrt.TRI_Mean : num 0.808 1.294 -1.766 -1.244 -1.428 ...
## $ sc.sqrt.DistancetoSea : num [1:22880, 1] -1.3 -1.3 -1.3 -1.3 -1.3 ...
## ..- attr(*, "scaled:center")= num 363
## ..- attr(*, "scaled:scale")= num 279Deal with multicollinearity
### correlation test
M = cor(bioactivity.60s[,c("sc.Windspeed", "sc.Temp", "sc.logPrecip","sc.Lon",
"sc.Lat", "sc.log1p.Nonbioactcount", "sc.DEM.100",
"sc.SlopeMeanpercent","sc.sqrt.TRI_Mean","sc.sqrt.DistancetoSea",
"sc.sqrt.Edgedensity", "sc.log1p.sumArea", "sc.log1p.IcefreePolygon_Count",
"sc.sqrt.IcefreepolygonMeanSize","sc.log1p.Feb_Seaice_median_km",
"sc.log1p.Sept_Seaice_median_km","sc.log.Travelspeedkmhour",
"sc.log.GNI_Claim","sc.log.PPP_Claim", "sc.sqrt.Dist_Claimline_km",
"sc.sqrt.Dist_air_km", "sc.sqrt.Dist_port_km", "sc.sqrt.Dis_traverseskm",
"sc.sqrt.Dist_station_km","sc.log.GNI_station", "sc.log.PPP_station",
"sc.sqrt.Peak_Popul")])
# mat : is a matrix of data
# ... : further arguments to pass to the native R cor.test function
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat<- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
# matrix of the p-value of the correlation
p.mat <- cor.mtest(M)
pdf(file="corrplot_vairables.pdf")
corrplot(M, type="upper", order="hclust",
p.mat = p.mat, sig.level = 0.05, insig = "blank", method = 'number', number.cex = 0.25, tl.cex=0.5)
dev.off()## png
## 2VIF_test <- glm(bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon + sc.Lat +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.TRI_Mean + sc.sqrt.DistancetoSea +
sc.sqrt.Edgedensity + sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station +
sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip +
## sc.Lon + sc.Lat + sc.log1p.Nonbioactcount + sc.DEM.100 +
## sc.SlopeMeanpercent + sc.sqrt.TRI_Mean + sc.sqrt.DistancetoSea +
## sc.sqrt.Edgedensity + sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count +
## sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
## sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
## sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
## sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
## sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station +
## sc.sqrt.Peak_Popul, family = binomial(link = "logit"), data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6677 -0.3552 -0.2092 -0.1147 4.0550
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.41779 0.04683 -72.982 < 2e-16 ***
## sc.Windspeed 0.07381 0.04340 1.701 0.088969 .
## sc.Temp 0.74618 0.11695 6.380 1.77e-10 ***
## sc.logPrecip -0.56419 0.06774 -8.328 < 2e-16 ***
## sc.Lon -0.37267 0.08117 -4.591 4.40e-06 ***
## sc.Lat 0.16657 0.12840 1.297 0.194562
## sc.log1p.Nonbioactcount 0.63711 0.02582 24.679 < 2e-16 ***
## sc.DEM.100 -0.33688 0.07378 -4.566 4.97e-06 ***
## sc.SlopeMeanpercent -0.50700 0.10427 -4.862 1.16e-06 ***
## sc.sqrt.TRI_Mean 0.44241 0.10290 4.299 1.71e-05 ***
## sc.sqrt.DistancetoSea -0.69931 0.09580 -7.300 2.89e-13 ***
## sc.sqrt.Edgedensity -0.26273 0.05464 -4.809 1.52e-06 ***
## sc.log1p.sumArea 0.35206 0.06527 5.394 6.90e-08 ***
## sc.log1p.IcefreePolygon_Count 0.37936 0.04784 7.930 2.20e-15 ***
## sc.sqrt.IcefreepolygonMeanSize 0.03251 0.04371 0.744 0.456932
## sc.log1p.Feb_Seaice_median_km 0.13281 0.04436 2.994 0.002755 **
## sc.log1p.Sept_Seaice_median_km -0.01520 0.03841 -0.396 0.692229
## sc.log.Travelspeedkmhour 0.04805 0.03708 1.296 0.195038
## sc.log.GNI_Claim -0.10379 0.05777 -1.797 0.072415 .
## sc.log.PPP_Claim -0.05471 0.07792 -0.702 0.482611
## sc.sqrt.Dist_Claimline_km 0.06803 0.04532 1.501 0.133346
## sc.sqrt.Dist_air_km -0.41699 0.04774 -8.735 < 2e-16 ***
## sc.sqrt.Dist_port_km 0.04842 0.08054 0.601 0.547708
## sc.sqrt.Dis_traverseskm -0.51492 0.08316 -6.192 5.94e-10 ***
## sc.sqrt.Dist_station_km 0.05281 0.06469 0.816 0.414284
## sc.log.GNI_station 0.10581 0.04948 2.138 0.032487 *
## sc.log.PPP_station 0.09273 0.02666 3.478 0.000504 ***
## sc.sqrt.Peak_Popul -0.33065 0.05621 -5.882 4.05e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13184.7 on 22879 degrees of freedom
## Residual deviance: 9198.2 on 22852 degrees of freedom
## AIC: 9254.2
##
## Number of Fisher Scoring iterations: 7vif(VIF_test)## sc.Windspeed sc.Temp
## 1.869023 15.689961
## sc.logPrecip sc.Lon
## 5.740865 6.970826
## sc.Lat sc.log1p.Nonbioactcount
## 17.101085 1.319437
## sc.DEM.100 sc.SlopeMeanpercent
## 4.077732 11.954405
## sc.sqrt.TRI_Mean sc.sqrt.DistancetoSea
## 14.075598 7.683720
## sc.sqrt.Edgedensity sc.log1p.sumArea
## 4.645284 7.462924
## sc.log1p.IcefreePolygon_Count sc.sqrt.IcefreepolygonMeanSize
## 2.688768 3.304032
## sc.log1p.Feb_Seaice_median_km sc.log1p.Sept_Seaice_median_km
## 1.986716 3.456269
## sc.log.Travelspeedkmhour sc.log.GNI_Claim
## 3.170756 3.719829
## sc.log.PPP_Claim sc.sqrt.Dist_Claimline_km
## 7.967706 2.539591
## sc.sqrt.Dist_air_km sc.sqrt.Dist_port_km
## 3.101619 7.345968
## sc.sqrt.Dis_traverseskm sc.sqrt.Dist_station_km
## 11.013952 5.300229
## sc.log.GNI_station sc.log.PPP_station
## 2.696944 1.216097
## sc.sqrt.Peak_Popul
## 3.410411From the VIF, we can see
Group 1 sc.SlopeMeanpercent, sc.sqrt.TRI_Mean. Group 2. sc.sqrt.Edgedensity, sc.log1p.sumArea, sc.log1p.IcefreePolygon_Count, sc.sqrt.IcefreepolygonMeanSize Group 3. sc.Lat, sc.Temp, sc.logPrecip,
Combing with the corrplot, I decided to delete “sc.sqrt.TRI_Mean” and keep “sc.SlopeMeanpercent”
VIF_test_1 <- glm(bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon + sc.Lat +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.DistancetoSea +
sc.sqrt.Edgedensity + sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test_1)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip +
## sc.Lon + sc.Lat + sc.log1p.Nonbioactcount + sc.DEM.100 +
## sc.SlopeMeanpercent + sc.sqrt.DistancetoSea + sc.sqrt.Edgedensity +
## sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count + sc.sqrt.IcefreepolygonMeanSize +
## sc.log1p.Feb_Seaice_median_km + sc.log1p.Sept_Seaice_median_km +
## sc.log.Travelspeedkmhour + sc.log.GNI_Claim + sc.log.PPP_Claim +
## sc.sqrt.Dist_Claimline_km + sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km +
## sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km + sc.log.GNI_station +
## sc.log.PPP_station + sc.sqrt.Peak_Popul, family = binomial(link = "logit"),
## data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6738 -0.3563 -0.2091 -0.1145 4.0927
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.42094 0.04696 -72.845 < 2e-16 ***
## sc.Windspeed 0.06741 0.04333 1.556 0.119717
## sc.Temp 0.69315 0.11591 5.980 2.23e-09 ***
## sc.logPrecip -0.53020 0.06720 -7.890 3.03e-15 ***
## sc.Lon -0.37599 0.08124 -4.628 3.69e-06 ***
## sc.Lat 0.14200 0.12787 1.110 0.266788
## sc.log1p.Nonbioactcount 0.64000 0.02575 24.853 < 2e-16 ***
## sc.DEM.100 -0.37295 0.07307 -5.104 3.33e-07 ***
## sc.SlopeMeanpercent -0.12053 0.04934 -2.443 0.014583 *
## sc.sqrt.DistancetoSea -0.70287 0.09573 -7.342 2.10e-13 ***
## sc.sqrt.Edgedensity -0.27555 0.05443 -5.063 4.14e-07 ***
## sc.log1p.sumArea 0.37888 0.06477 5.850 4.92e-09 ***
## sc.log1p.IcefreePolygon_Count 0.43039 0.04630 9.296 < 2e-16 ***
## sc.sqrt.IcefreepolygonMeanSize 0.04901 0.04335 1.131 0.258225
## sc.log1p.Feb_Seaice_median_km 0.13159 0.04442 2.963 0.003051 **
## sc.log1p.Sept_Seaice_median_km -0.01739 0.03837 -0.453 0.650305
## sc.log.Travelspeedkmhour -0.00374 0.03511 -0.107 0.915161
## sc.log.GNI_Claim -0.11088 0.05771 -1.921 0.054686 .
## sc.log.PPP_Claim -0.06134 0.07764 -0.790 0.429501
## sc.sqrt.Dist_Claimline_km 0.06994 0.04538 1.541 0.123262
## sc.sqrt.Dist_air_km -0.41874 0.04761 -8.795 < 2e-16 ***
## sc.sqrt.Dist_port_km 0.04669 0.08035 0.581 0.561200
## sc.sqrt.Dis_traverseskm -0.48995 0.08306 -5.899 3.66e-09 ***
## sc.sqrt.Dist_station_km 0.05195 0.06454 0.805 0.420871
## sc.log.GNI_station 0.09976 0.04938 2.020 0.043361 *
## sc.log.PPP_station 0.09494 0.02658 3.571 0.000355 ***
## sc.sqrt.Peak_Popul -0.33899 0.05618 -6.034 1.60e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13185 on 22879 degrees of freedom
## Residual deviance: 9217 on 22853 degrees of freedom
## AIC: 9271
##
## Number of Fisher Scoring iterations: 7vif(VIF_test_1)## sc.Windspeed sc.Temp
## 1.855741 15.491499
## sc.logPrecip sc.Lon
## 5.640990 6.978735
## sc.Lat sc.log1p.Nonbioactcount
## 17.016577 1.320738
## sc.DEM.100 sc.SlopeMeanpercent
## 4.006395 2.884414
## sc.sqrt.DistancetoSea sc.sqrt.Edgedensity
## 7.672212 4.668789
## sc.log1p.sumArea sc.log1p.IcefreePolygon_Count
## 7.407011 2.557321
## sc.sqrt.IcefreepolygonMeanSize sc.log1p.Feb_Seaice_median_km
## 3.273668 1.993267
## sc.log1p.Sept_Seaice_median_km sc.log.Travelspeedkmhour
## 3.470327 2.895737
## sc.log.GNI_Claim sc.log.PPP_Claim
## 3.712740 7.940639
## sc.sqrt.Dist_Claimline_km sc.sqrt.Dist_air_km
## 2.553885 3.090237
## sc.sqrt.Dist_port_km sc.sqrt.Dis_traverseskm
## 7.334166 10.997808
## sc.sqrt.Dist_station_km sc.log.GNI_station
## 5.301867 2.687403
## sc.log.PPP_station sc.sqrt.Peak_Popul
## 1.214267 3.390606Group 2. sc.sqrt.Edgedensity, sc.log1p.sumArea, sc.log1p.IcefreePolygon_Count, sc.sqrt.IcefreepolygonMeanSize I decided to keep sc.log1p.IcefreePolygon_Count, sc.sqrt.IcefreepolygonMeanSize
VIF_test_2 <- glm(bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon + sc.Lat +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.DistancetoSea +
sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test_2)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip +
## sc.Lon + sc.Lat + sc.log1p.Nonbioactcount + sc.DEM.100 +
## sc.SlopeMeanpercent + sc.sqrt.DistancetoSea + sc.log1p.IcefreePolygon_Count +
## sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
## sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
## sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
## sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
## sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station +
## sc.sqrt.Peak_Popul, family = binomial(link = "logit"), data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8146 -0.3566 -0.2098 -0.1157 4.0962
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.410342 0.046789 -72.887 < 2e-16 ***
## sc.Windspeed 0.081062 0.042366 1.913 0.055697 .
## sc.Temp 0.636199 0.115255 5.520 3.39e-08 ***
## sc.logPrecip -0.563909 0.064136 -8.792 < 2e-16 ***
## sc.Lon -0.427171 0.081031 -5.272 1.35e-07 ***
## sc.Lat 0.219903 0.126771 1.735 0.082803 .
## sc.log1p.Nonbioactcount 0.659994 0.025319 26.067 < 2e-16 ***
## sc.DEM.100 -0.424911 0.071942 -5.906 3.50e-09 ***
## sc.SlopeMeanpercent -0.063545 0.046148 -1.377 0.168517
## sc.sqrt.DistancetoSea -0.688864 0.095080 -7.245 4.32e-13 ***
## sc.log1p.IcefreePolygon_Count 0.401800 0.037274 10.780 < 2e-16 ***
## sc.sqrt.IcefreepolygonMeanSize 0.203609 0.032631 6.240 4.39e-10 ***
## sc.log1p.Feb_Seaice_median_km 0.128800 0.044418 2.900 0.003735 **
## sc.log1p.Sept_Seaice_median_km -0.007459 0.038245 -0.195 0.845362
## sc.log.Travelspeedkmhour 0.018255 0.034820 0.524 0.600100
## sc.log.GNI_Claim -0.109698 0.057482 -1.908 0.056340 .
## sc.log.PPP_Claim -0.074780 0.077387 -0.966 0.333890
## sc.sqrt.Dist_Claimline_km 0.053961 0.045365 1.189 0.234253
## sc.sqrt.Dist_air_km -0.401303 0.047331 -8.479 < 2e-16 ***
## sc.sqrt.Dist_port_km 0.056637 0.080359 0.705 0.480937
## sc.sqrt.Dis_traverseskm -0.521172 0.082850 -6.291 3.16e-10 ***
## sc.sqrt.Dist_station_km 0.067723 0.064271 1.054 0.292016
## sc.log.GNI_station 0.082937 0.049249 1.684 0.092176 .
## sc.log.PPP_station 0.093942 0.026506 3.544 0.000394 ***
## sc.sqrt.Peak_Popul -0.295146 0.054592 -5.406 6.43e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13184.7 on 22879 degrees of freedom
## Residual deviance: 9254.5 on 22855 degrees of freedom
## AIC: 9304.5
##
## Number of Fisher Scoring iterations: 7vif(VIF_test_2)## sc.Windspeed sc.Temp
## 1.784341 15.384325
## sc.logPrecip sc.Lon
## 5.155983 7.001984
## sc.Lat sc.log1p.Nonbioactcount
## 16.788551 1.282594
## sc.DEM.100 sc.SlopeMeanpercent
## 3.886623 2.610004
## sc.sqrt.DistancetoSea sc.log1p.IcefreePolygon_Count
## 7.575980 1.677038
## sc.sqrt.IcefreepolygonMeanSize sc.log1p.Feb_Seaice_median_km
## 1.838560 1.994886
## sc.log1p.Sept_Seaice_median_km sc.log.Travelspeedkmhour
## 3.471388 2.861025
## sc.log.GNI_Claim sc.log.PPP_Claim
## 3.718894 7.950074
## sc.sqrt.Dist_Claimline_km sc.sqrt.Dist_air_km
## 2.560744 3.065347
## sc.sqrt.Dist_port_km sc.sqrt.Dis_traverseskm
## 7.350097 10.994678
## sc.sqrt.Dist_station_km sc.log.GNI_station
## 5.265342 2.687516
## sc.log.PPP_station sc.sqrt.Peak_Popul
## 1.212616 3.277778Group 3. I decided to move sc.Lat, because this is coorelated with many other factors such as distance to the sea/port/north pole
VIF_test_3 <- glm(bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.DistancetoSea +
sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test_3)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip +
## sc.Lon + sc.log1p.Nonbioactcount + sc.DEM.100 + sc.SlopeMeanpercent +
## sc.sqrt.DistancetoSea + sc.log1p.IcefreePolygon_Count + sc.sqrt.IcefreepolygonMeanSize +
## sc.log1p.Feb_Seaice_median_km + sc.log1p.Sept_Seaice_median_km +
## sc.log.Travelspeedkmhour + sc.log.GNI_Claim + sc.log.PPP_Claim +
## sc.sqrt.Dist_Claimline_km + sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km +
## sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km + sc.log.GNI_station +
## sc.log.PPP_station + sc.sqrt.Peak_Popul, family = binomial(link = "logit"),
## data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8194 -0.3572 -0.2097 -0.1159 4.0671
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.40239 0.04631 -73.477 < 2e-16 ***
## sc.Windspeed 0.10297 0.04046 2.545 0.010925 *
## sc.Temp 0.70142 0.10888 6.442 1.18e-10 ***
## sc.logPrecip -0.55696 0.06389 -8.718 < 2e-16 ***
## sc.Lon -0.37540 0.07471 -5.025 5.04e-07 ***
## sc.log1p.Nonbioactcount 0.65714 0.02524 26.038 < 2e-16 ***
## sc.DEM.100 -0.39431 0.06962 -5.663 1.48e-08 ***
## sc.SlopeMeanpercent -0.07543 0.04571 -1.650 0.098912 .
## sc.sqrt.DistancetoSea -0.73846 0.09008 -8.198 2.45e-16 ***
## sc.log1p.IcefreePolygon_Count 0.40303 0.03725 10.819 < 2e-16 ***
## sc.sqrt.IcefreepolygonMeanSize 0.20305 0.03248 6.252 4.04e-10 ***
## sc.log1p.Feb_Seaice_median_km 0.13927 0.04402 3.164 0.001558 **
## sc.log1p.Sept_Seaice_median_km -0.04276 0.03242 -1.319 0.187186
## sc.log.Travelspeedkmhour 0.01006 0.03448 0.292 0.770354
## sc.log.GNI_Claim -0.08435 0.05531 -1.525 0.127261
## sc.log.PPP_Claim -0.11361 0.07399 -1.536 0.124652
## sc.sqrt.Dist_Claimline_km 0.09430 0.03914 2.410 0.015973 *
## sc.sqrt.Dist_air_km -0.42218 0.04582 -9.215 < 2e-16 ***
## sc.sqrt.Dist_port_km 0.04820 0.07993 0.603 0.546556
## sc.sqrt.Dis_traverseskm -0.45156 0.07261 -6.219 5.01e-10 ***
## sc.sqrt.Dist_station_km 0.07228 0.06404 1.129 0.259073
## sc.log.GNI_station 0.07184 0.04882 1.472 0.141132
## sc.log.PPP_station 0.09643 0.02651 3.638 0.000275 ***
## sc.sqrt.Peak_Popul -0.31290 0.05340 -5.859 4.64e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13184.7 on 22879 degrees of freedom
## Residual deviance: 9257.6 on 22856 degrees of freedom
## AIC: 9305.6
##
## Number of Fisher Scoring iterations: 7vif(VIF_test_3)## sc.Windspeed sc.Temp
## 1.627876 13.728471
## sc.logPrecip sc.Lon
## 5.150518 5.978725
## sc.log1p.Nonbioactcount sc.DEM.100
## 1.276835 3.638588
## sc.SlopeMeanpercent sc.sqrt.DistancetoSea
## 2.555602 6.859961
## sc.log1p.IcefreePolygon_Count sc.sqrt.IcefreepolygonMeanSize
## 1.675640 1.842583
## sc.log1p.Feb_Seaice_median_km sc.log1p.Sept_Seaice_median_km
## 1.962779 2.500349
## sc.log.Travelspeedkmhour sc.log.GNI_Claim
## 2.804239 3.456081
## sc.log.PPP_Claim sc.sqrt.Dist_Claimline_km
## 7.259886 1.898248
## sc.sqrt.Dist_air_km sc.sqrt.Dist_port_km
## 2.890921 7.328950
## sc.sqrt.Dis_traverseskm sc.sqrt.Dist_station_km
## 8.438584 5.275240
## sc.log.GNI_station sc.log.PPP_station
## 2.648946 1.210408
## sc.sqrt.Peak_Popul
## 3.150969Group 4. I decided to move sc.sqrt.DistancetoSea, because this is correlated with distance to the ports, and I think even some places they are close to sea, they are still very inaccessible.
VIF_test_4 <- glm(bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent +
sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test_4)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.Temp + sc.logPrecip +
## sc.Lon + sc.log1p.Nonbioactcount + sc.DEM.100 + sc.SlopeMeanpercent +
## sc.log1p.IcefreePolygon_Count + sc.sqrt.IcefreepolygonMeanSize +
## sc.log1p.Feb_Seaice_median_km + sc.log1p.Sept_Seaice_median_km +
## sc.log.Travelspeedkmhour + sc.log.GNI_Claim + sc.log.PPP_Claim +
## sc.sqrt.Dist_Claimline_km + sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km +
## sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km + sc.log.GNI_station +
## sc.log.PPP_station + sc.sqrt.Peak_Popul, family = binomial(link = "logit"),
## data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7135 -0.3601 -0.2106 -0.1248 3.9641
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.344780 0.044341 -75.434 < 2e-16 ***
## sc.Windspeed 0.052722 0.040247 1.310 0.190204
## sc.Temp 0.795579 0.105834 7.517 5.59e-14 ***
## sc.logPrecip -0.499057 0.062342 -8.005 1.19e-15 ***
## sc.Lon -0.144034 0.067511 -2.133 0.032884 *
## sc.log1p.Nonbioactcount 0.648650 0.025078 25.865 < 2e-16 ***
## sc.DEM.100 -0.485916 0.068011 -7.145 9.02e-13 ***
## sc.SlopeMeanpercent -0.057783 0.045470 -1.271 0.203800
## sc.log1p.IcefreePolygon_Count 0.391603 0.037067 10.565 < 2e-16 ***
## sc.sqrt.IcefreepolygonMeanSize 0.176936 0.031503 5.616 1.95e-08 ***
## sc.log1p.Feb_Seaice_median_km 0.224462 0.042327 5.303 1.14e-07 ***
## sc.log1p.Sept_Seaice_median_km 0.022227 0.031364 0.709 0.478533
## sc.log.Travelspeedkmhour 0.069538 0.033856 2.054 0.039981 *
## sc.log.GNI_Claim -0.038045 0.053212 -0.715 0.474624
## sc.log.PPP_Claim -0.110820 0.071852 -1.542 0.122990
## sc.sqrt.Dist_Claimline_km 0.096735 0.039139 2.472 0.013453 *
## sc.sqrt.Dist_air_km -0.391284 0.046552 -8.405 < 2e-16 ***
## sc.sqrt.Dist_port_km -0.286912 0.070769 -4.054 5.03e-05 ***
## sc.sqrt.Dis_traverseskm -0.304214 0.068338 -4.452 8.52e-06 ***
## sc.sqrt.Dist_station_km 0.027238 0.066909 0.407 0.683946
## sc.log.GNI_station 0.003293 0.047319 0.070 0.944520
## sc.log.PPP_station 0.092481 0.026639 3.472 0.000517 ***
## sc.sqrt.Peak_Popul -0.238355 0.051105 -4.664 3.10e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13184.7 on 22879 degrees of freedom
## Residual deviance: 9326.3 on 22857 degrees of freedom
## AIC: 9372.3
##
## Number of Fisher Scoring iterations: 7vif(VIF_test_4)## sc.Windspeed sc.Temp
## 1.600405 13.543477
## sc.logPrecip sc.Lon
## 5.099791 4.913435
## sc.log1p.Nonbioactcount sc.DEM.100
## 1.273142 3.470305
## sc.SlopeMeanpercent sc.log1p.IcefreePolygon_Count
## 2.534587 1.676404
## sc.sqrt.IcefreepolygonMeanSize sc.log1p.Feb_Seaice_median_km
## 1.823059 1.827966
## sc.log1p.Sept_Seaice_median_km sc.log.Travelspeedkmhour
## 2.337945 2.699609
## sc.log.GNI_Claim sc.log.PPP_Claim
## 3.242042 6.937203
## sc.sqrt.Dist_Claimline_km sc.sqrt.Dist_air_km
## 1.967607 2.972189
## sc.sqrt.Dist_port_km sc.sqrt.Dis_traverseskm
## 5.948737 7.662423
## sc.sqrt.Dist_station_km sc.log.GNI_station
## 5.817417 2.602498
## sc.log.PPP_station sc.sqrt.Peak_Popul
## 1.206469 3.004187Group 5. I decided to move sc.Temp, because many factors they determine temperatures, such as dem, distance to the port.
VIF_test_5 <- glm(bioact_PA ~ sc.Windspeed + sc.logPrecip + sc.Lon +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent +
sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul, data = bioactivity.60s, family = binomial(link = "logit"))
summary(VIF_test_5)##
## Call:
## glm(formula = bioact_PA ~ sc.Windspeed + sc.logPrecip + sc.Lon +
## sc.log1p.Nonbioactcount + sc.DEM.100 + sc.SlopeMeanpercent +
## sc.log1p.IcefreePolygon_Count + sc.sqrt.IcefreepolygonMeanSize +
## sc.log1p.Feb_Seaice_median_km + sc.log1p.Sept_Seaice_median_km +
## sc.log.Travelspeedkmhour + sc.log.GNI_Claim + sc.log.PPP_Claim +
## sc.sqrt.Dist_Claimline_km + sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km +
## sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km + sc.log.GNI_station +
## sc.log.PPP_station + sc.sqrt.Peak_Popul, family = binomial(link = "logit"),
## data = bioactivity.60s)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7052 -0.3626 -0.2108 -0.1284 4.1076
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.328759 0.043886 -75.851 < 2e-16 ***
## sc.Windspeed 0.010039 0.039250 0.256 0.798128
## sc.logPrecip -0.324723 0.057538 -5.644 1.67e-08 ***
## sc.Lon -0.286435 0.065568 -4.369 1.25e-05 ***
## sc.log1p.Nonbioactcount 0.647666 0.024917 25.993 < 2e-16 ***
## sc.DEM.100 -0.823371 0.051393 -16.021 < 2e-16 ***
## sc.SlopeMeanpercent -0.002308 0.044467 -0.052 0.958599
## sc.log1p.IcefreePolygon_Count 0.394063 0.036985 10.655 < 2e-16 ***
## sc.sqrt.IcefreepolygonMeanSize 0.190132 0.031511 6.034 1.60e-09 ***
## sc.log1p.Feb_Seaice_median_km 0.164637 0.041410 3.976 7.01e-05 ***
## sc.log1p.Sept_Seaice_median_km -0.006042 0.030863 -0.196 0.844787
## sc.log.Travelspeedkmhour 0.138216 0.032335 4.275 1.92e-05 ***
## sc.log.GNI_Claim -0.038093 0.052576 -0.725 0.468740
## sc.log.PPP_Claim -0.150258 0.071217 -2.110 0.034871 *
## sc.sqrt.Dist_Claimline_km 0.142998 0.038593 3.705 0.000211 ***
## sc.sqrt.Dist_air_km -0.344768 0.045845 -7.520 5.47e-14 ***
## sc.sqrt.Dist_port_km -0.353686 0.070212 -5.037 4.72e-07 ***
## sc.sqrt.Dis_traverseskm -0.072060 0.062717 -1.149 0.250567
## sc.sqrt.Dist_station_km -0.078216 0.065075 -1.202 0.229383
## sc.log.GNI_station 0.006581 0.047428 0.139 0.889648
## sc.log.PPP_station 0.099985 0.026709 3.743 0.000182 ***
## sc.sqrt.Peak_Popul -0.194779 0.050594 -3.850 0.000118 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13184.7 on 22879 degrees of freedom
## Residual deviance: 9383.9 on 22858 degrees of freedom
## AIC: 9427.9
##
## Number of Fisher Scoring iterations: 7vif(VIF_test_5)## sc.Windspeed sc.logPrecip
## 1.557376 4.310429
## sc.Lon sc.log1p.Nonbioactcount
## 4.591899 1.273024
## sc.DEM.100 sc.SlopeMeanpercent
## 2.016380 2.441677
## sc.log1p.IcefreePolygon_Count sc.sqrt.IcefreepolygonMeanSize
## 1.676701 1.814235
## sc.log1p.Feb_Seaice_median_km sc.log1p.Sept_Seaice_median_km
## 1.748078 2.291320
## sc.log.Travelspeedkmhour sc.log.GNI_Claim
## 2.467570 3.147719
## sc.log.PPP_Claim sc.sqrt.Dist_Claimline_km
## 6.876311 1.925803
## sc.sqrt.Dist_air_km sc.sqrt.Dist_port_km
## 2.880225 5.849822
## sc.sqrt.Dis_traverseskm sc.sqrt.Dist_station_km
## 6.375155 5.523365
## sc.log.GNI_station sc.log.PPP_station
## 2.616868 1.208849
## sc.sqrt.Peak_Popul
## 2.946629### correlation test
M = cor(bioactivity.60s[,c("sc.Windspeed", "sc.logPrecip","sc.Lon",
"sc.log1p.Nonbioactcount", "sc.DEM.100",
"sc.SlopeMeanpercent",
"sc.log1p.IcefreePolygon_Count",
"sc.sqrt.IcefreepolygonMeanSize","sc.log1p.Feb_Seaice_median_km",
"sc.log1p.Sept_Seaice_median_km","sc.log.Travelspeedkmhour",
"sc.log.GNI_Claim","sc.log.PPP_Claim", "sc.sqrt.Dist_Claimline_km",
"sc.sqrt.Dist_air_km", "sc.sqrt.Dist_port_km", "sc.sqrt.Dis_traverseskm",
"sc.sqrt.Dist_station_km","sc.log.GNI_station", "sc.log.PPP_station",
"sc.sqrt.Peak_Popul")])
# mat : is a matrix of data
# ... : further arguments to pass to the native R cor.test function
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat<- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
# matrix of the p-value of the correlation
p.mat <- cor.mtest(M)
pdf(file="corrplot_vairables_cleaned.pdf")
corrplot(M, type="upper", order="hclust",
p.mat = p.mat, sig.level = 0.05, insig = "blank", method = 'number', number.cex = 0.25, tl.cex=0.5)
dev.off()## png
## 2#Random split into 80-20 without considering spatial autocorrelation
#Random split into 80-20 without considering spatial autocorrelation
mydata <- bioactivity.60s
smp_size <- floor(0.8*nrow(mydata)) #80% for training
set.seed(8792) # to make this random spliting reproducible
train_ind <- sample(seq_len(nrow(mydata)), size = smp_size)
train80 <- mydata[train_ind, ]
test20 <- mydata[-train_ind, ]#Undersample the zeros in train80 to make the training dataset balancing
train80.under0 <- ovun.sample(bioact_PA ~ ID + Longtitude_WGS84 + Latitude_WGS84 + POINT_X_SSP +
POINT_Y_SSP + sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon +
sc.Lat + sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.TRI_Mean + sc.sqrt.DistancetoSea +
sc.sqrt.Edgedensity + sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station +
sc.sqrt.Peak_Popul + Nonbioact_PA + Countlandscapetype +
sloperecl + ClaimCountry_Count + Mainshippiingzone + Shiptrafficintensity +
Penornot + Dist_Claim_recl + Dist_air_recl + Dist_travese_recl +
Dist_port_recl + Dist_station_recl + Peak_Popul_recl,
p=0.5, data=train80, method = "under",
na.action=options("na.action")$na.action, seed = 8470, )$datamodel (no spatial, no structure 0)
# fit.bern_train80under0_all
trainingdata <- train80.under0 # change here when change data for training
testdata <- test20
y_obs <- trainingdata$bioact_PA
intercept_only <- glm(bioact_PA ~ 1, data = trainingdata, family = binomial(link = "logit"))
all_variables <- glm(bioact_PA ~ sc.Windspeed + sc.logPrecip + sc.Lon + sc.log1p.Nonbioactcount +
sc.DEM.100 + sc.SlopeMeanpercent + sc.log1p.IcefreePolygon_Count +
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour + sc.log.GNI_Claim +
sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km + sc.sqrt.Dist_air_km +
sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km +
sc.log.GNI_station + sc.log.PPP_station + Nonbioact_PA + Countlandscapetype +
sloperecl + ClaimCountry_Count + Mainshippiingzone + Shiptrafficintensity +
Penornot + Dist_Claim_recl + Dist_air_recl + Dist_travese_recl +
Dist_port_recl + Dist_station_recl + Peak_Popul_recl,
data = trainingdata, family = binomial(link = "logit"))
with(summary(all_variables), 1 - deviance/null.deviance)## [1] 0.3558881car::vif(all_variables)## GVIF Df GVIF^(1/(2*Df))
## sc.Windspeed 2.555535 1 1.598604
## sc.logPrecip 4.277408 1 2.068189
## sc.Lon 9.138189 1 3.022944
## sc.log1p.Nonbioactcount 3.066218 1 1.751062
## sc.DEM.100 2.282741 1 1.510874
## sc.SlopeMeanpercent 15.201268 1 3.898880
## sc.log1p.IcefreePolygon_Count 1.886280 1 1.373419
## sc.sqrt.IcefreepolygonMeanSize 2.026678 1 1.423615
## sc.log1p.Feb_Seaice_median_km 3.039806 1 1.743504
## sc.log1p.Sept_Seaice_median_km 3.982995 1 1.995744
## sc.log.Travelspeedkmhour 3.089417 1 1.757674
## sc.log.GNI_Claim 8.609049 1 2.934118
## sc.log.PPP_Claim 19.091169 1 4.369344
## sc.sqrt.Dist_Claimline_km 5.037704 1 2.244483
## sc.sqrt.Dist_air_km 6.221762 1 2.494346
## sc.sqrt.Dist_port_km 13.856489 1 3.722431
## sc.sqrt.Dis_traverseskm 17.538348 1 4.187881
## sc.sqrt.Dist_station_km 11.299433 1 3.361463
## sc.log.GNI_station 3.126174 1 1.768099
## sc.log.PPP_station 1.610178 1 1.268928
## Nonbioact_PA 2.892811 1 1.700827
## Countlandscapetype 1.731287 4 1.071016
## sloperecl 25.328871 5 1.381534
## ClaimCountry_Count 355.010167 3 2.660971
## Mainshippiingzone 3.598621 1 1.897003
## Shiptrafficintensity 72.305275 3 2.041088
## Penornot 31.875949 1 5.645879
## Dist_Claim_recl 4.576297 2 1.462610
## Dist_air_recl 8.693814 2 1.717128
## Dist_travese_recl 3.647794 2 1.381999
## Dist_port_recl 15.829775 2 1.994659
## Dist_station_recl 11.345838 2 1.835309
## Peak_Popul_recl 29.091550 4 1.523950both <- step(intercept_only, direction='both', scope=formula(all_variables), trace=0)
summary(both)##
## Call:
## glm(formula = bioact_PA ~ sc.log1p.Nonbioactcount + sc.DEM.100 +
## sc.sqrt.Dist_station_km + sc.log1p.IcefreePolygon_Count +
## Shiptrafficintensity + sc.Lon + sc.log.Travelspeedkmhour +
## sc.sqrt.IcefreepolygonMeanSize + Nonbioact_PA + Peak_Popul_recl +
## Countlandscapetype + sc.logPrecip + sc.sqrt.Dist_port_km +
## Dist_station_recl + Dist_air_recl + sc.log1p.Feb_Seaice_median_km,
## family = binomial(link = "logit"), data = trainingdata)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1114 -0.6880 0.0982 0.7186 3.3178
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.14801 0.33449 -0.443 0.658127
## sc.log1p.Nonbioactcount 0.43877 0.07472 5.872 4.31e-09 ***
## sc.DEM.100 -0.60190 0.07207 -8.351 < 2e-16 ***
## sc.sqrt.Dist_station_km -0.18165 0.12783 -1.421 0.155307
## sc.log1p.IcefreePolygon_Count 0.33142 0.05507 6.019 1.76e-09 ***
## Shiptrafficintensity7 0.67555 0.18840 3.586 0.000336 ***
## Shiptrafficintensity75 0.46679 0.20257 2.304 0.021200 *
## Shiptrafficintensity150 -0.85165 0.19384 -4.394 1.11e-05 ***
## sc.Lon -0.48284 0.09433 -5.118 3.08e-07 ***
## sc.log.Travelspeedkmhour 0.24032 0.05593 4.297 1.73e-05 ***
## sc.sqrt.IcefreepolygonMeanSize 0.27683 0.06396 4.328 1.51e-05 ***
## Nonbioact_PA1 0.57452 0.15934 3.606 0.000311 ***
## Peak_Popul_recl2 -0.64689 0.20066 -3.224 0.001265 **
## Peak_Popul_recl3 -0.27561 0.20709 -1.331 0.183238
## Peak_Popul_recl4 -0.40070 0.21531 -1.861 0.062740 .
## Peak_Popul_recl5 -1.15962 0.26697 -4.344 1.40e-05 ***
## Countlandscapetype2 0.34934 0.15202 2.298 0.021565 *
## Countlandscapetype3 0.56603 0.16804 3.369 0.000756 ***
## Countlandscapetype4 0.79217 0.31496 2.515 0.011900 *
## Countlandscapetype5 12.74340 277.45392 0.046 0.963366
## sc.logPrecip -0.34668 0.08541 -4.059 4.93e-05 ***
## sc.sqrt.Dist_port_km -0.30477 0.11145 -2.735 0.006247 **
## Dist_station_recl2 -0.63784 0.17604 -3.623 0.000291 ***
## Dist_station_recl3 -0.54409 0.25417 -2.141 0.032299 *
## Dist_air_recl2 -0.69698 0.16118 -4.324 1.53e-05 ***
## Dist_air_recl3 -0.75965 0.24137 -3.147 0.001648 **
## sc.log1p.Feb_Seaice_median_km -0.16755 0.06949 -2.411 0.015901 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4208.7 on 3035 degrees of freedom
## Residual deviance: 2746.1 on 3009 degrees of freedom
## AIC: 2800.1
##
## Number of Fisher Scoring iterations: 12performance of the model (no spatial, no structure 0)
best_model <- glm(bioact_PA ~ sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.sqrt.Dist_station_km + sc.log1p.IcefreePolygon_Count +
Shiptrafficintensity + sc.Lon + sc.log.Travelspeedkmhour +
sc.sqrt.IcefreepolygonMeanSize + Nonbioact_PA + Peak_Popul_recl +
Countlandscapetype + sc.logPrecip + sc.sqrt.Dist_port_km +
Dist_station_recl + Dist_air_recl + sc.log1p.Feb_Seaice_median_km,
family = binomial(link = "logit"), data = trainingdata)
Dev_res_best_model <- residuals(best_model) # Deviance residuals
##### Prediction
predict_20 <- predict(best_model, newdata = test20, type = "response")
predicted_20 <- ifelse(predict_20 > 0.5, 1, 0)
tab <- table(Predicted = predicted_20, Reference = test20$bioact_PA)
aucs.test <- as.data.frame(evalmod(scores = predicted_20, labels = test20$bioact_PA, mode = 'aucroc'))
### Moran's I
trainingdata_Dev <- cbind(trainingdata, Dev_res_best_model)
coordinates(trainingdata_Dev) = ~POINT_X_SSP+POINT_Y_SSP
W <- 1/as.matrix(dist(coordinates(trainingdata_Dev)))
diag(W) <- 0
moran.i <- Moran.I(trainingdata_Dev$Dev_res_best_model, W) # this one much quicker
moran.i## $observed
## [1] 0.04367041
##
## $expected
## [1] -0.0003294893
##
## $sd
## [1] 0.001874366
##
## $p.value
## [1] 0#Random split into 80-20 without considering spatial autocorrelation for 50 times and store them
train_ind <- c()
train80 <- list()
test20 <- list()
train80.under0 <- list()
smp_size <- floor(0.8*nrow(mydata)) #80% for training
for (i in 1:50) {
train_ind <- sample(seq_len(nrow(mydata)), size = smp_size)
train80[[i]] <- mydata[train_ind, ]
test20[[i]] <- mydata[-train_ind, ]
train80.under0[[i]] <- ovun.sample(bioact_PA ~ ID + Longtitude_WGS84 + Latitude_WGS84 +
POINT_X_SSP + POINT_Y_SSP +
sc.Windspeed + sc.Temp + sc.logPrecip + sc.Lon + sc.Lat +
sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.SlopeMeanpercent + sc.sqrt.TRI_Mean + sc.sqrt.DistancetoSea +
sc.sqrt.Edgedensity + sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km + sc.sqrt.Dis_traverseskm +
sc.sqrt.Dist_station_km + sc.log.GNI_station + sc.log.PPP_station +
sc.sqrt.Peak_Popul + Nonbioact_PA + Countlandscapetype +
sloperecl + ClaimCountry_Count + Mainshippiingzone + Shiptrafficintensity +
Penornot + Dist_Claim_recl + Dist_air_recl + Dist_travese_recl + Dist_port_recl +
Dist_station_recl + Peak_Popul_recl, p=0.5, data= train80[[i]], method = "under",
na.action=options("na.action")$na.action, seed = 8472,)$data
}
model_glm_8020 <- list()
pseduoR2_glm_8020 <- c()
mymodel_glm_8020 <- list()
moran.i_8020 <- list()
moran.i_8020.ob <- c()
moran.i_8020.p <- c()
predict_8020 <- list()
predicted_8020 <- list()
tab_8020 <- list()
aucs.df_8020 <- list()
AUC_8020 <- c()
for(i in 1: 50){
#fit the traning data with a glm model to get the p_hat
model_glm_8020[[i]] <- glm(bioact_PA ~ sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.sqrt.Dist_station_km + sc.log1p.IcefreePolygon_Count +
Shiptrafficintensity + sc.Lon + sc.log.Travelspeedkmhour +
sc.sqrt.IcefreepolygonMeanSize + Nonbioact_PA + Peak_Popul_recl +
Countlandscapetype + sc.logPrecip + sc.sqrt.Dist_port_km +
Dist_station_recl + Dist_air_recl + sc.log1p.Feb_Seaice_median_km,
family = binomial(link = "logit"), data = train80.under0[[i]])
pseduoR2_glm_8020[i] <- (model_glm_8020[[i]]$null.deviance - model_glm_8020[[i]]$deviance) / model_glm_8020[[i]]$null.deviance
p_hat_glm_8020 <- predict(model_glm_8020[[i]], type = "response")
Dev_res_glm_8020 <- residuals(model_glm_8020[[i]]) # Deviance residuals. Deviance Residuals measure how much probabilities estimated from our model differ from the observed prostationions of successes.
mymodel_glm_8020[[i]] <- cbind(train80.under0[[i]], p_hat_glm_8020, Dev_res_glm_8020)
coordinates(mymodel_glm_8020[[i]]) = ~POINT_X_SSP+POINT_Y_SSP
w_8020 <- 1/as.matrix(dist(coordinates(mymodel_glm_8020[[i]])))
diag(w_8020) <- 0
moran.i_8020[[i]] <- Moran.I(mymodel_glm_8020[[i]]$Dev_res_glm_8020, w_8020) # this one much quicker
moran.i_8020.ob[i] <- moran.i_8020[[i]]$observed
moran.i_8020.p[i] <- moran.i_8020[[i]]$p.value
###### Prediction
predict_8020[[i]] <- predict(model_glm_8020[[i]], newdata = test20[[i]], type = "response") # type = "response" is equal to inv.logit
predicted_8020[[i]] <- ifelse(predict_8020[[i]] > 0.5,1,0)
tab_8020[[i]] <- table(Predicted = predict_8020[[i]], Reference = test20[[i]]$bioact_PA)
aucs.df_8020[[i]] <- as.data.frame(evalmod(scores = predict_8020[[i]], labels = test20[[i]]$bioact_PA, mode = 'aucroc'))
AUC_8020[i] <- aucs.df_8020[[i]]$aucs
}
mean(AUC_8020)## [1] 0.8774526mean(pseduoR2_glm_8020)## [1] 0.3488343mean(moran.i_8020.ob)## [1] 0.04543055mean(moran.i_8020.p)## [1] 0##### prediction accuracy test on all unused data (test data and data deleted by undersampling)
mydata_notusedinmodel <- list()
predict_notusedinmodel <- list()
predicted_notusedinmodel <- list()
tab_notusedinmodel <- list()
aucs.df_notusedinmodel <- list()
AUC_notusedinmodel <- c()
for(i in 1: 50){
mydata_notusedinmodel[[i]] <- subset(mydata, !(ID %in% train80.under0[[i]]$ID))
predict_notusedinmodel[[i]] <- predict(model_glm_8020[[i]], newdata = mydata_notusedinmodel[[i]], type = "response") # type = "response" is equal to inv.logit
predicted_notusedinmodel[[i]] <- ifelse(predict_notusedinmodel[[i]] > 0.5, 1, 0)
tab_notusedinmodel[[i]] <- table(Predicted = predict_notusedinmodel[[i]], Reference = mydata_notusedinmodel[[i]]$bioact_PA)
aucs.df_notusedinmodel[[i]] <- as.data.frame(evalmod(scores = predict_notusedinmodel[[i]], labels = mydata_notusedinmodel[[i]]$bioact_PA, mode = 'aucroc'))
AUC_notusedinmodel[i] <- aucs.df_notusedinmodel[[i]]$aucs
}
mean(AUC_notusedinmodel)## [1] 0.8758157the result of 22880 without taking sturctured zero is better than the models that taking out sturctured zero
let’s prove this by using subset 1
#Random split into 80-20 without considering spatial autocorrelation for 50 times and store them
smp_size <- floor(0.8*nrow(subset1)) #80% for training
train_ind <- c()
train80 <- list()
test20 <- list()
train80.under0 <- list()
for (i in 1:50) {
train_ind <- sample(seq_len(nrow(subset1)), size = smp_size)
train80[[i]] <- subset1[train_ind, ]
test20[[i]] <- subset1[-train_ind, ]
train80.under0[[i]] <- ovun.sample(bioact_PA ~ ID + Longtitude_WGS84 + Latitude_WGS84 +
POINT_X_SSP + POINT_Y_SSP + sc.Windspeed +
sc.Temp + sc.logPrecip + sc.Lon + sc.Lat +
sc.log1p.Nonbioactcount + sc.DEM.100 + sc.SlopeMeanpercent +
sc.sqrt.TRI_Mean + sc.sqrt.DistancetoSea + sc.sqrt.Edgedensity +
sc.log1p.sumArea + sc.log1p.IcefreePolygon_Count+
sc.sqrt.IcefreepolygonMeanSize + sc.log1p.Feb_Seaice_median_km +
sc.log1p.Sept_Seaice_median_km + sc.log.Travelspeedkmhour +
sc.log.GNI_Claim + sc.log.PPP_Claim + sc.sqrt.Dist_Claimline_km +
sc.sqrt.Dist_air_km + sc.sqrt.Dist_port_km +
sc.sqrt.Dis_traverseskm + sc.sqrt.Dist_station_km +
sc.log.GNI_station + sc.log.PPP_station + sc.sqrt.Peak_Popul +
Nonbioact_PA + Countlandscapetype + sloperecl +
ClaimCountry_Count + Mainshippiingzone + Shiptrafficintensity +
Penornot + Dist_Claim_recl + Dist_air_recl + Dist_travese_recl +
Dist_port_recl + Dist_station_recl + Peak_Popul_recl,
p=0.5, data= train80[[i]], method = "under",
na.action=options("na.action")$na.action, seed = 8472,)$data
}
###### modells building glm 8020 randomly select
# test20[[i]]
# train80.under0[[i]]
model_glm_8020 <- list()
pseduoR2_glm_8020 <- c()
mymodel_glm_8020 <- list()
moran.i_8020 <- list()
moran.i_8020.ob <- c()
moran.i_8020.p <- c()
predict_8020 <- list()
predicted_8020 <- list()
tab_8020 <- list()
aucs.df_8020 <- list()
AUC_8020 <- c()
for(i in 1: 50){
#fit the traning data with a glm model to get the p_hat
model_glm_8020[[i]] <- glm(bioact_PA ~ sc.log1p.Nonbioactcount + sc.DEM.100 +
sc.sqrt.Dist_station_km + sc.log1p.IcefreePolygon_Count +
Shiptrafficintensity + sc.Lon + sc.log.Travelspeedkmhour +
sc.sqrt.IcefreepolygonMeanSize + Nonbioact_PA + Peak_Popul_recl +
Countlandscapetype + sc.logPrecip + sc.sqrt.Dist_port_km +
Dist_station_recl + Dist_air_recl + sc.log1p.Feb_Seaice_median_km,
family = binomial(link = "logit"), data = train80.under0[[i]])
pseduoR2_glm_8020[i] <- (model_glm_8020[[i]]$null.deviance - model_glm_8020[[i]]$deviance) / model_glm_8020[[i]]$null.deviance
p_hat_glm_8020 <- predict(model_glm_8020[[i]], type = "response")
Dev_res_glm_8020 <- residuals(model_glm_8020[[i]]) # Deviance residuals. Deviance Residuals measure how much probabilities estimated from our model differ from the observed prostationions of successes.
mymodel_glm_8020[[i]] <- cbind(train80.under0[[i]], p_hat_glm_8020, Dev_res_glm_8020)
coordinates(mymodel_glm_8020[[i]]) = ~POINT_X_SSP+POINT_Y_SSP
w_8020 <- 1/as.matrix(dist(coordinates(mymodel_glm_8020[[i]])))
diag(w_8020) <- 0
moran.i_8020[[i]] <- Moran.I(mymodel_glm_8020[[i]]$Dev_res_glm_8020, w_8020) # this one much quicker
moran.i_8020.ob[i] <- moran.i_8020[[i]]$observed
moran.i_8020.p[i] <- moran.i_8020[[i]]$p.value
###### Prediction
predict_8020[[i]] <- predict(model_glm_8020[[i]], newdata = test20[[i]], type = "response") # type = "response" is equal to inv.logit
predicted_8020[[i]] <- ifelse(predict_8020[[i]] > 0.5,1,0)
tab_8020[[i]] <- table(Predicted = predict_8020[[i]], Reference = test20[[i]]$bioact_PA)
aucs.df_8020[[i]] <- as.data.frame(evalmod(scores = predict_8020[[i]], labels = test20[[i]]$bioact_PA, mode = 'aucroc'))
AUC_8020[i] <- aucs.df_8020[[i]]$aucs
}## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredmean(AUC_8020)## [1] 0.8824448mean(pseduoR2_glm_8020)## [1] 0.3593347mean(moran.i_8020.ob)## [1] 0.03424289mean(moran.i_8020.p)## [1] 0##### prediction accuracy test on all unused data (test data and data deleted by undersampling)
mydata_notusedinmodel <- list()
predict_notusedinmodel <- list()
predicted_notusedinmodel <- list()
tab_notusedinmodel <- list()
aucs.df_notusedinmodel <- list()
AUC_notusedinmodel <- c()
for(i in 1: 50){
mydata_notusedinmodel[[i]] <- subset(mydata, !(ID %in% train80.under0[[i]]$ID))
predict_notusedinmodel[[i]] <- predict(model_glm_8020[[i]], newdata = mydata_notusedinmodel[[i]], type = "response") # type = "response" is equal to inv.logit
predicted_notusedinmodel[[i]] <- ifelse(predict_notusedinmodel[[i]] > 0.5, 1, 0)
tab_notusedinmodel[[i]] <- table(Predicted = predict_notusedinmodel[[i]], Reference = mydata_notusedinmodel[[i]]$bioact_PA)
aucs.df_notusedinmodel[[i]] <- as.data.frame(evalmod(scores = predict_notusedinmodel[[i]], labels = mydata_notusedinmodel[[i]]$bioact_PA, mode = 'aucroc'))
AUC_notusedinmodel[i] <- aucs.df_notusedinmodel[[i]]$aucs
}
mean(AUC_notusedinmodel)## [1] 0.3936575Alternatively, to support a first choice of block size, prior to any model fitting, package blockCV allows the user to look at the existing autocorrelation in the predictors, as an indication of landscape spatial structure. The function works by automatically fitting variogram models to each continuous raster and finding the effective range of spatial autocorrelation.
The purpose of blockcv, is to find an indication of landscape spatial structure
##### test the spatial autocorrelation of each varibale
# Very plain plot using the spline.correlog function
bioactivity.60s.forSAC <- bioactivity.60s
coordinates(bioactivity.60s.forSAC) = ~ POINT_X_SSP + POINT_Y_SSP
crs(bioactivity.60s.forSAC) <- CRS("+proj=stere +lat_0=-90 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs")
bioactivity.60s.forSAC <- st_as_sf(bioactivity.60s.forSAC)
spa_test <- cv_spatial_autocor(x=bioactivity.60s.forSAC, column=
c("bioact_PA","sc.log1p.Nonbioactcount","sc.DEM.100",
"sc.sqrt.Dist_station_km","sc.log1p.IcefreePolygon_Count",
"Shiptrafficintensity","sc.Lon","sc.log.Travelspeedkmhour",
"sc.sqrt.IcefreepolygonMeanSize","Nonbioact_PA","Peak_Popul_recl",
"Countlandscapetype","sc.logPrecip","sc.sqrt.Dist_port_km",
"Dist_station_recl","Dist_air_recl","sc.log1p.Feb_Seaice_median_km"))##
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#mydata
#spatial mappping and blocking
### import data
Antarctica <- st_read("ADD_Coastline_high_res_line_Sliced.shp")## Reading layer `ADD_Coastline_high_res_line_Sliced' from data source
## `C:\Users\N11094427\OneDrive - Queensland University of Technology\Documents\ArcGIS\Projects\MaxENT\Data\OutputforR\ADD_Coastline_high_res_line_Sliced.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 29563 features and 10 fields
## Geometry type: MULTILINESTRING
## Dimension: XY
## Bounding box: xmin: -2661818 ymin: -2491520 xmax: 2746700 ymax: 2322031
## Projected CRS: WGS 84 / Antarctic Polar Stereographic###data a preparation for spatial block
proj4string <-("+proj=stere +lat_0=-90 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs") # the code of WGS84 Stereographic South Pole
my.projection <- st_crs(proj4string) # get the crs of WGS 1984 Stereographic South Pole
dem <- raster("ADD_Coastline_high_res_Me.tif") # get the dem raster
dem_WG84SSP <- projectRaster(dem, crs = crs(proj4string)) # reproject the dem raster to let it have the same crs code with our target data
sb_bioactivity.60s <- sf::st_as_sf(bioactivity.60s, coords = c("POINT_X_SSP", "POINT_Y_SSP"), crs = my.projection) # turn csv to sf with the crs
sb_bioactivity.60s_bioact <- sb_bioactivity.60s [, c("ID","bioact_PA","Bioactivititycount","Nonbioact_PA")]
#spatial block
sb_mydata <- spatialBlock(
speciesData = sb_bioactivity.60s,
species = "bioact_PA",
rasterLayer = dem_WG84SSP,
theRange = 300000, # block range is 300km
k = 96,
iteration = 100,
numLimit = 0L,
border = NULL,
biomod2Format = TRUE,
xOffset = 0,
yOffset = 0,
seed = 9897
)## This function is deprecated! Please use 'cv_spatial' instead.##
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## train_0 train_1 test_0 test_1
## 1 20944 1916 17 3
## 2 20948 1918 13 1
## 3 20779 1879 182 40
## 4 20849 1913 112 6
## 5 20949 1898 12 21
## 6 20895 1919 66 0
## 7 20861 1919 100 0
## 8 20863 1918 98 1
## 9 20916 1914 45 5
## 10 20602 1910 359 9
## 11 20958 1919 3 0
## 12 20943 1917 18 2
## 13 20955 1919 6 0
## 14 20922 1918 39 1
## 15 20021 1721 940 198
## 16 20880 1916 81 3
## 17 20959 1917 2 2
## 18 20885 1912 76 7
## 19 20594 1910 367 9
## 20 20961 1918 0 1
## 21 20961 1915 0 4
## 22 20851 1919 110 0
## 23 20213 1916 748 3
## 24 20947 1918 14 1
## 25 20943 1919 18 0
## 26 20787 1918 174 1
## 27 20953 1888 8 31
## 28 20861 1874 100 45
## 29 20904 1909 57 10
## 30 20789 1842 172 77
## 31 20634 1844 327 75
## 32 20932 1919 29 0
## 33 19612 1851 1349 68
## 34 20511 1899 450 20
## 35 20942 1917 19 2
## 36 20959 1919 2 0
## 37 20169 1911 792 8
## 38 20659 1801 302 118
## 39 20910 1874 51 45
## 40 20477 1918 484 1
## 41 20797 1909 164 10
## 42 20952 1919 9 0
## 43 20813 1859 148 60
## 44 20890 1916 71 3
## 45 20951 1914 10 5
## 46 20676 1904 285 15
## 47 20505 1897 456 22
## 48 20926 1917 35 2
## 49 20325 1895 636 24
## 50 20836 1906 125 13
## 51 20916 1917 45 2
## 52 20793 1864 168 55
## 53 19816 1754 1145 165
## 54 20876 1847 85 72
## 55 20960 1919 1 0
## 56 20877 1870 84 49
## 57 20530 1889 431 30
## 58 20939 1914 22 5
## 59 20110 1773 851 146
## 60 20829 1878 132 41
## 61 20890 1919 71 0
## 62 20496 1907 465 12
## 63 20959 1919 2 0
## 64 20892 1919 69 0
## 65 20954 1917 7 2
## 66 20906 1914 55 5
## 67 20960 1919 1 0
## 68 19777 1834 1184 85
## 69 20774 1877 187 42
## 70 20951 1913 10 6
## 71 20013 1860 948 59
## 72 20891 1913 70 6
## 73 20959 1919 2 0
## 74 20580 1919 381 0
## 75 20930 1904 31 15
## 76 20928 1906 33 13
## 77 20943 1917 18 2
## 78 20959 1919 2 0
## 79 20950 1919 11 0
## 80 20759 1918 202 1
## 81 20038 1903 923 16
## 82 20889 1899 72 20
## 83 19787 1912 1174 7
## 84 20940 1919 21 0
## 85 20862 1912 99 7
## 86 20780 1908 181 11
## 87 20630 1907 331 12
## 88 20923 1919 38 0
## 89 20679 1911 282 8
## 90 20938 1919 23 0
## 91 20912 1917 49 2
## 92 20961 1917 0 2
## 93 20949 1909 12 10
## 94 20956 1919 5 0
## 95 19918 1902 1043 17
## 96 20647 1897 314 22## Warning in cv_spatial(x = speciesData, column = species, r = rasterLayer, :
## Folds 6, 7, 11, 13, 20, 21, 22, 25, 32, 36, 42, 55, 61, 63, 64, 67, 73, 74, 78,
## 79, 84, 88, 90, 92, 94 have class(es) with zero records
sb_mydata$blocks_sf <- st_as_sf(sb_mydata$blocks)
mydata.blocked <- st_join(sb_bioactivity.60s, sb_mydata$blocks_sf, join = st_intersects)
mydata.blocked$folds <- factor(mydata.blocked$folds)
sb_mydata$plots
###adding points on spatialBlock Plot
sb_mydata$plots + geom_sf(data = sb_bioactivity.60s, alpha = 0.1, col = "blue")
# drop the geometry to make data as dataframe for resampling purpose
mydata.blocked.dropgeo <- st_drop_geometry(mydata.blocked)
# Add geometry back to data, but the data is still dataframe class
geometry <- bioactivity.60s[c("ID", "POINT_X_SSP", "POINT_Y_SSP")]
mydata.blocked <- merge(mydata.blocked.dropgeo, geometry, by=c("ID"))
mydata.blocked.copy <- mydata.blocked
mydata.blocked$folds## [1] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [25] 68 68 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
## [49] 46 46 71 71 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 71 71 46 46
## [73] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 71 46 46 46 46 46 46 46 46 46
## [97] 46 46 46 46 46 46 46 46 46 71 46 46 46 46 46 46 46 46 46 46 46 46 71 71
## [121] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 71 71 71 71 71 71 46 46 46
## [145] 46 46 46 46 46 46 46 46 46 46 46 71 71 71 71 71 71 46 46 46 46 46 46 46
## [169] 46 46 46 46 46 46 46 46 71 71 71 71 71 71 71 71 46 46 46 46 46 46 46 46
## [193] 46 46 46 46 46 46 46 66 66 66 66 71 71 71 71 71 46 46 46 46 46 46 46 46
## [217] 46 46 46 46 46 46 46 66 66 66 66 66 66 66 71 71 71 71 71 71 71 71 46 46
## [241] 46 46 46 46 46 46 46 66 66 66 66 66 66 66 66 66 71 71 71 71 71 71 71 71
## [265] 71 71 71 71 46 46 46 46 46 46 46 49 49 49 49 66 66 66 66 66 66 66 66 66
## [289] 66 66 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 46 46 48 48 48
## [313] 49 49 49 49 49 49 66 66 66 66 66 66 66 66 66 66 71 71 71 71 71 71 71 71
## [337] 71 71 71 71 71 71 71 71 46 46 48 48 48 49 49 49 49 49 49 49 49 66 66 66
## [361] 66 66 66 66 66 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [385] 71 71 71 71 71 46 46 48 48 48 48 48 48 48 48 48 49 49 49 49 33 33 33 33
## [409] 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [433] 83 83 83 83 83 83 83 78 78 77 77 77 77 77 77 76 33 33 33 33 33 33 33 33
## [457] 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [481] 83 83 83 83 83 77 77 77 77 77 76 33 33 33 33 33 33 33 33 33 33 83 83 83
## [505] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [529] 83 83 83 83 83 83 77 77 77 77 77 77 76 33 33 33 33 33 33 33 33 33 33 33
## [553] 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [577] 83 83 83 83 83 83 83 83 83 83 77 77 76 33 33 33 33 33 33 33 33 33 33 33
## [601] 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [625] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 77 76 76 76 33 33 33 33 33 33
## [649] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83
## [673] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76 76 33 33 33 33 33
## [697] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83
## [721] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76 33 33 33
## [745] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83
## [769] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76 33 33 33 33 33 33
## [793] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83
## [817] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 33 33
## [841] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83
## [865] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [889] 83 83 76 76 76 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [913] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83
## [937] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76 76 33
## [961] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [985] 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1009] 83 83 83 83 83 83 83 83 83 83 83 83 76 33 33 33 33 33 33 33 33 33 33 33
## [1033] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83
## [1057] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 33
## [1081] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1105] 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1129] 83 83 83 83 83 83 83 83 76 76 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1153] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83
## [1177] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76
## [1201] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1225] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1249] 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 76 33 33 33 33 33 33 33 33
## [1273] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1297] 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1321] 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1345] 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83
## [1369] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 76 76 33 33 33 33 33 33
## [1393] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1417] 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1441] 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1465] 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83
## [1489] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33
## [1513] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83
## [1537] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1561] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83
## [1585] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33
## [1609] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1633] 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1657] 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1681] 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1705] 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1729] 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83
## [1753] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33
## [1777] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83
## [1801] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1825] 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1849] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1873] 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33
## [1897] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83
## [1921] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33
## [1945] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [1969] 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [1993] 83 83 60 60 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2017] 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [2041] 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33
## [2065] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83
## [2089] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 60 33 33 33
## [2113] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2137] 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [2161] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2185] 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [2209] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2233] 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [2257] 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2281] 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33
## [2305] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2329] 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33
## [2353] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83
## [2377] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33
## [2401] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83
## [2425] 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2449] 33 33 33 33 33 83 83 83 83 83 83 60 33 33 33 33 33 33 33 33 33 33 33 33
## [2473] 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 60 60 33 33 33 33
## [2497] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 60 60
## [2521] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83
## [2545] 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83
## [2569] 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2593] 33 33 33 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2617] 33 33 33 33 33 33 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2641] 33 33 33 33 33 33 33 33 83 83 83 83 83 60 33 33 33 33 33 33 33 33 33 33
## [2665] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 33 33 33
## [2689] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83
## [2713] 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2737] 33 33 33 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 83 83
## [2761] 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 83 83
## [2785] 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2809] 33 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33
## [2833] 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83
## [2857] 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83
## [2881] 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2905] 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33
## [2929] 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33
## [2953] 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83
## [2977] 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83
## [3001] 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33 33 33 33 33 33 33 33 33 33
## [3025] 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 33 33 33 33 33
## [3049] 33 33 33 33 33 33 33 33 33 33 33 83 83 83 83 83 83 83 83 83 83 83 83 31
## [3073] 31 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 19 19 19 19
## [3097] 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19
## [3121] 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19
## [3145] 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 19
## [3169] 19 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 31 31 31 31 31 31 31 31
## [3193] 31 31 31 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 31
## [3217] 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19
## [3241] 19 31 31 31 31 31 31 31 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31
## [3265] 31 31 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 19 19 19
## [3289] 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19
## [3313] 19 19 19 19 19 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 19 31
## [3337] 31 31 31 31 31 31 19 19 19 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31
## [3361] 31 31 31 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31
## [3385] 31 31 31 31 19 19 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 19
## [3409] 19 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 19 19 19 19
## [3433] 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 31
## [3457] 31 31 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31
## [3481] 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31
## [3505] 31 31 31 31 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 31 31
## [3529] 31 19 19 19 19 19 19 19 19 19 31 31 31 31 31 31 31 31 31 31 31 31 31 19
## [3553] 19 19 19 19 19 31 31 31 31 31 31 31 31 19 19 19 19 19 19 19 19 31 31 31
## [3577] 31 31 31 31 19 19 19 19 31 31 31 31 31 31 19 19 19 19 19 19 19 31 31 31
## [3601] 31 31 31 31 31 19 19 19 19 19 19 31 31 31 31 31 31 31 19 19 19 19 19 19
## [3625] 31 31 31 31 31 19 19 19 19 31 31 31 31 19 31 31 31 31 31 19 19 31 31 31
## [3649] 19 19 19 19 31 19 19 19 19 31 31 31 19 19 19 31 31 31 19 19 31 31 31 19
## [3673] 19 19 19 31 31 31 19 19 19 31 31 31 19 19 19 19 31 31 31 19 19 19 19 19
## [3697] 31 19 19 19 19 31 31 19 19 19 19 19 31 31 31 31 19 31 31 31 31 31 31 31
## [3721] 31 31 31 31 31 31 31 31 31 19 19 31 31 31 31 31 19 19 19 19 19 19 31 31
## [3745] 31 31 31 19 19 19 19 19 19 19 19 31 31 31 31 31 19 19 19 19 19 19 31 31
## [3769] 19 19 19 19 19 19 19 19 19 19 31 31 31 31 19 19 19 19 19 19 19 19 19 19
## [3793] 31 31 31 31 31 19 19 19 19 19 19 19 19 31 31 31 19 19 19 19 31 31 31 31
## [3817] 31 19 19 19 19 19 19 31 31 31 31 31 19 19 19 19 31 31 31 31 31 31 31 31
## [3841] 19 19 19 19 19 19 19 19 19 15 15 15 15 15 15 15 15 64 64 64 64 64 64 64
## [3865] 64 64 64 64 15 15 15 15 15 15 15 15 15 64 64 64 64 64 64 64 64 64 64 64
## [3889] 64 15 15 15 15 15 15 15 15 64 64 64 64 64 64 64 64 15 15 15 15 15 15 15
## [3913] 15 64 64 64 64 64 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [3937] 15 15 64 64 64 15 15 15 15 15 15 15 15 15 15 64 64 64 64 15 15 15 15 15
## [3961] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [3985] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4009] 15 15 64 64 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 64 64 64 64 15
## [4033] 15 15 15 15 15 15 15 15 15 15 15 15 15 64 64 64 64 64 15 15 15 15 15 15
## [4057] 15 15 15 15 15 15 15 15 15 64 64 64 15 15 15 15 15 15 15 15 15 15 15 15
## [4081] 15 15 15 15 15 64 64 64 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4105] 15 15 15 15 64 64 64 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4129] 15 15 15 15 15 15 15 15 15 64 64 64 15 15 15 15 15 15 15 15 15 15 15 15
## [4153] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4177] 15 15 15 15 15 15 15 15 15 15 15 15 15 64 64 15 15 15 15 15 15 15 15 15
## [4201] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 64 15 15 15
## [4225] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4249] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4273] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4297] 15 15 15 15 17 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4321] 15 15 15 15 15 15 15 15 15 17 3 93 15 15 15 15 15 15 15 15 15 15 15 15
## [4345] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 93 93 15 15 15 15 15 15
## [4369] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4393] 15 15 15 15 15 15 15 15 15 15 17 93 15 15 15 15 15 15 15 15 15 15 15 15
## [4417] 15 15 15 15 15 15 15 15 17 93 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4441] 15 15 15 15 15 15 15 15 15 15 44 44 15 15 15 15 15 15 15 15 15 15 15 15
## [4465] 15 15 15 15 15 15 15 15 15 15 44 3 15 15 15 15 15 15 15 15 15 15 15 15
## [4489] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4513] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4537] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4561] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4585] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4609] 15 15 15 15 15 15 15 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4633] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 3 3 15 15 15 15
## [4657] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4681] 15 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4705] 15 15 15 15 15 15 15 15 15 15 15 15 3 3 3 3 3 15 15 15 15 15 15 15
## [4729] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 3 3 3
## [4753] 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4777] 15 15 15 15 15 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15
## [4801] 15 15 15 15 15 15 15 15 15 15 15 15 15 44 3 3 3 3 3 3 3 3 93 15
## [4825] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4849] 15 3 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15
## [4873] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 44 44 44 44 44 44 44 44 3 3
## [4897] 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4921] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 44 44 44 44 44 44 44 44 44 44
## [4945] 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [4969] 15 15 15 15 15 15 44 44 44 44 44 44 44 44 3 3 3 3 3 3 3 3 3 3
## [4993] 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 44
## [5017] 44 44 44 44 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 93 93 15 15
## [5041] 15 15 15 15 15 15 15 15 15 15 15 15 44 44 44 44 3 3 3 3 3 3 3 3
## [5065] 3 3 3 3 3 3 3 3 93 93 15 15 15 15 15 15 15 15 15 15 15 15 15 44
## [5089] 44 44 44 3 3 3 3 3 3 3 3 3 3 3 93 93 15 15 15 15 15 15 15 15
## [5113] 15 15 44 44 44 44 44 3 3 3 3 3 3 3 93 93 93 15 15 15 15 15 15 15
## [5137] 15 15 44 44 44 3 3 3 3 3 3 93 93 93 93 93 15 15 15 15 15 15 15 15
## [5161] 15 15 15 15 15 15 15 44 44 3 3 3 3 3 3 3 93 93 15 15 15 15 15 15
## [5185] 15 15 15 15 15 15 15 15 15 15 3 3 3 3 3 3 3 3 3 15 15 15 15 15
## [5209] 15 15 15 15 15 15 15 15 15 15 15 15 3 3 3 3 3 3 3 3 3 3 3 3
## [5233] 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 44 44 44 44
## [5257] 44 44 44 3 3 3 3 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15
## [5281] 15 15 15 15 15 15 15 15 15 15 15 44 44 44 44 44 44 44 44 3 3 3 3 3
## [5305] 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 44 44
## [5329] 44 3 3 3 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15 15 15
## [5353] 15 15 44 44 44 3 3 3 3 3 3 3 3 3 3 3 15 15 15 15 15 15 15 15
## [5377] 15 15 61 61 61 18 18 18 18 18 18 18 18 18 18 18 18 18 37 37 37 37 37 37
## [5401] 37 37 37 37 61 61 61 61 18 18 18 18 18 18 18 18 18 18 18 37 37 37 37 37
## [5425] 37 37 61 61 61 18 18 18 18 18 18 18 18 18 18 37 37 37 37 37 61 18 18 18
## [5449] 18 92 92 61 61 18 18 18 18 18 18 18 18 37 37 37 61 61 61 18 18 18 18 18
## [5473] 18 18 18 37 37 37 37 37 37 1 1 61 61 18 18 18 18 18 18 18 37 37 37 37
## [5497] 37 37 37 61 61 61 61 18 18 37 37 37 37 37 37 37 1 61 61 18 18 18 18 37
## [5521] 37 37 37 37 37 37 37 37 37 37 1 1 1 1 61 61 18 18 18 18 18 37 37 37
## [5545] 37 37 37 37 37 37 37 37 37 37 1 1 1 1 1 1 18 18 37 37 37 37 37 37
## [5569] 37 37 37 37 37 1 1 1 18 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5593] 37 37 37 37 1 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 1 18 37
## [5617] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5641] 37 61 61 18 18 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 61
## [5665] 61 18 18 18 18 37 37 37 37 37 37 37 37 37 61 61 61 61 18 37 37 37 37 37
## [5689] 37 37 37 37 37 37 37 37 37 61 61 61 37 37 37 37 37 37 37 37 37 37 37 37
## [5713] 37 37 37 61 61 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61
## [5737] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5761] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5785] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5809] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5833] 37 37 37 37 37 37 37 37 37 1 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5857] 37 37 37 37 37 37 37 1 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5881] 37 37 37 37 37 37 37 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5905] 37 37 37 37 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61 37 37
## [5929] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [5953] 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61 61 37 37 37 37 37 37 37 37
## [5977] 37 37 37 37 61 61 61 37 37 37 37 37 37 37 37 37 37 37 61 37 37 37 37 37
## [6001] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6025] 37 5 5 37 37 37 37 37 37 37 37 37 37 37 37 5 5 37 37 37 37 37 37 37
## [6049] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6073] 37 37 37 37 37 37 37 37 5 37 37 37 37 37 37 37 37 37 37 37 37 37 5 5
## [6097] 5 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 5 5 5 5 5 5 5 37
## [6121] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 5 5 5 5 5 5 5 5 37 37
## [6145] 37 37 37 37 37 37 37 37 37 37 37 37 5 5 5 5 5 37 37 37 37 37 37 37
## [6169] 37 37 37 37 37 37 37 5 5 5 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6193] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6217] 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6241] 37 37 37 37 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61 37 37 37
## [6265] 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61 37 37 37 37 37 37 37 37 37
## [6289] 37 37 37 37 37 37 37 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6313] 37 5 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6337] 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 61 61
## [6361] 61 61 61 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [6385] 37 37 5 90 90 90 90 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6409] 23 23 23 23 23 45 90 90 90 90 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6433] 23 23 23 23 23 23 23 45 90 90 90 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6457] 23 23 23 23 23 90 90 90 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6481] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6505] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 45 45 45 45 45 23 23 23 23 23
## [6529] 23 23 23 23 23 23 23 23 23 23 23 23 23 45 45 45 45 23 23 23 23 23 23 23
## [6553] 23 23 23 23 23 23 23 23 23 45 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6577] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6601] 23 23 23 23 23 23 23 23 23 23 23 45 23 23 23 23 23 23 23 23 23 23 23 23
## [6625] 23 23 23 23 23 23 23 23 23 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6649] 23 23 23 23 23 23 23 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6673] 23 23 23 23 23 45 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6697] 23 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6721] 23 23 23 23 23 23 45 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6745] 23 23 23 23 23 23 23 23 23 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23
## [6769] 23 23 23 23 23 23 23 23 23 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23
## [6793] 23 23 23 23 23 23 23 23 23 23 23 81 81 81 81 81 81 81 81 23 23 23 23 23
## [6817] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 8 81 81 81 81 81 81 81 81 23
## [6841] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 8 81 81 81 81 81
## [6865] 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 81 81
## [6889] 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6913] 23 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6937] 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [6961] 23 23 23 23 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23
## [6985] 23 23 23 23 23 23 23 23 23 23 23 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7009] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 81 81 81 81 81
## [7033] 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [7057] 23 23 23 23 23 23 23 23 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7081] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 81 81 81 81
## [7105] 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23
## [7129] 23 23 23 23 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23
## [7153] 23 23 23 23 23 23 23 8 8 90 90 81 81 81 81 81 81 81 81 81 81 23 23 23
## [7177] 23 23 23 23 23 23 23 23 23 23 23 23 23 23 8 8 8 8 8 90 81 81 81 81
## [7201] 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 23 23 8 8 8
## [7225] 8 8 8 8 90 90 90 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23
## [7249] 23 23 23 23 23 23 23 23 8 8 8 8 90 90 81 81 81 81 81 81 81 81 81 81
## [7273] 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 23 8 90 81 81 81 81 81 81
## [7297] 81 81 81 81 23 23 23 23 23 23 23 23 23 23 8 8 8 8 8 8 8 81 81 81
## [7321] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 23 23
## [7345] 23 23 8 8 8 8 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7369] 81 81 81 81 23 23 23 23 23 23 23 23 23 23 23 8 8 8 8 81 81 81 81 81
## [7393] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 81 81 81 81
## [7417] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 8 81 81
## [7441] 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23 23 8 81 81 81
## [7465] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 23
## [7489] 8 8 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7513] 81 81 23 23 23 23 23 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7537] 81 81 81 81 81 81 23 23 23 23 23 8 8 8 81 81 81 81 81 81 81 81 81 81
## [7561] 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 23 81 81 81 81 81 81
## [7585] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23
## [7609] 23 23 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7633] 81 81 81 81 81 23 23 23 23 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7657] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 81 81
## [7681] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7705] 81 81 81 81 81 81 81 81 23 23 23 67 81 81 81 81 81 81 81 81 81 81 81 81
## [7729] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7753] 23 23 23 23 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7777] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 8
## [7801] 8 8 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7825] 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 23 23 8 8 8 8 8 8 8
## [7849] 8 8 8 8 8 8 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7873] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 23 23 8 8 8 8 8 8 8 8
## [7897] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7921] 81 81 81 81 81 81 81 81 81 81 81 81 81 8 8 8 8 8 8 8 79 81 81 81
## [7945] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [7969] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 8 8 8 8 8 8 8 8 8
## [7993] 8 79 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8017] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 8
## [8041] 8 8 8 79 79 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8065] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 8 8
## [8089] 8 8 79 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8113] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 8 8 79 79 79
## [8137] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8161] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 11 11 79
## [8185] 79 79 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
## [8209] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 11 95 95 95
## [8233] 95 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8257] 74 74 74 74 74 74 74 74 74 74 74 28 2 95 95 95 95 95 95 74 74 74 74 74
## [8281] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8305] 74 74 74 74 74 28 2 2 95 95 95 95 95 95 95 74 74 74 74 74 74 74 74 74
## [8329] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 28 2 2 2 95 95
## [8353] 95 95 95 95 95 95 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8377] 74 74 74 74 74 74 74 74 74 74 74 28 28 2 95 95 95 95 95 95 95 74 74 74
## [8401] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8425] 74 74 74 74 28 28 28 28 95 95 95 95 95 95 95 74 74 74 74 74 74 74 74 74
## [8449] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 28 28 28 28 28 2 95 95 95
## [8473] 95 95 95 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8497] 74 28 28 28 28 28 28 2 95 95 95 95 95 74 74 74 74 74 74 74 74 74 74 74
## [8521] 74 74 74 74 74 74 74 74 74 74 74 74 28 28 28 28 28 28 28 2 95 95 95 95
## [8545] 95 95 95 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8569] 74 74 74 28 28 28 28 28 28 28 28 2 95 95 95 95 95 95 95 95 95 74 74 74
## [8593] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 28 28 28 28
## [8617] 28 28 28 28 2 2 85 95 95 95 95 95 95 95 95 95 74 74 74 74 74 74 74 74
## [8641] 74 74 74 74 74 74 74 74 74 74 74 74 74 28 28 28 28 28 28 28 28 2 85 85
## [8665] 85 95 95 95 95 95 95 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
## [8689] 74 28 28 28 28 28 28 28 28 28 85 85 95 74 74 74 74 74 74 74 74 74 74 74
## [8713] 74 28 28 28 28 28 28 28 28 85 85 95 95 74 74 74 74 74 74 28 28 28 28 28
## [8737] 28 28 85 85 95 95 74 74 74 74 74 74 74 74 74 28 28 28 85 85 95 95 95 95
## [8761] 95 95 95 95 95 74 74 74 74 74 74 74 74 74 74 85 85 95 95 95 95 95 95 95
## [8785] 95 74 74 74 74 74 74 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 74 74
## [8809] 74 74 74 74 28 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8833] 74 74 74 74 28 28 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8857] 95 95 95 95 74 74 74 28 28 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8881] 95 95 95 95 74 74 74 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8905] 95 95 74 74 28 28 28 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8929] 95 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 28 95
## [8953] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [8977] 28 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9001] 95 95 95 95 42 85 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9025] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 85 85 85 85 85 85 95 95 95 95
## [9049] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9073] 95 28 28 28 28 28 28 85 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9097] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 28 28 28 28 28 28
## [9121] 28 28 85 85 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9145] 95 95 95 95 95 95 95 95 28 28 28 28 28 28 95 95 95 95 95 95 95 95 95 95
## [9169] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 28 28 28 28 42
## [9193] 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9217] 95 95 95 95 95 95 95 28 28 28 28 28 42 85 85 95 95 95 95 95 95 95 95 95
## [9241] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 28 28
## [9265] 28 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9289] 95 95 95 95 95 95 95 95 74 74 28 28 85 85 85 95 95 95 95 95 95 95 95 95
## [9313] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 74
## [9337] 74 28 28 42 85 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9361] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 74 74 74 42 85 85 36 95
## [9385] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9409] 95 95 95 95 95 95 95 74 74 74 28 36 95 95 95 95 95 95 95 95 95 95 95 95
## [9433] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 28 42 95 95 95
## [9457] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9481] 28 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9505] 95 95 28 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9529] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9553] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9577] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 85 95
## [9601] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9625] 95 95 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9649] 95 95 95 95 95 95 95 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9673] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 85 85 85 85 85 85 85 14
## [9697] 14 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9721] 95 95 95 85 14 14 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9745] 95 95 95 95 95 95 95 95 95 95 95 85 85 14 14 14 95 95 95 95 95 95 95 95
## [9769] 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 28 85 85 85 85 85 14 14
## [9793] 14 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9817] 95 95 95 42 42 85 85 14 14 14 95 95 95 95 95 95 95 95 95 95 95 95 95 95
## [9841] 95 95 95 95 95 95 95 95 85 85 85 85 85 85 14 14 14 14 95 95 95 95 95 95
## [9865] 95 95 95 95 95 95 95 95 95 85 85 85 85 85 85 14 14 95 95 95 95 95 95 95
## [9889] 95 95 95 95 95 95 95 85 85 14 95 95 95 95 95 95 95 95 95 95 95 95 95 85
## [9913] 85 85 85 95 95 95 95 95 95 95 95 95 95 95 95 95 85 85 85 85 85 85 14 14
## [9937] 14 14 14 14 95 95 95 95 95 95 95 95 95 95 95 42 85 85 85 85 14 14 14 14
## [9961] 14 14 14 14 95 95 95 95 95 95 95 95 95 85 85 14 14 14 14 14 14 95 95 95
## [9985] 95 95 58 72 72 72 72 25 25 25 25 25 25 58 72 72 72 72 25 25 25 58 72 72
## [10009] 72 72 25 25 25 58 24 72 72 72 72 25 76 76 24 72 72 72 25 25 76 76 72 25
## [10033] 25 76 76 25 24 24 76 76 24 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58
## [10057] 21 21 58 58 58 58 58 58 21 58 58 72 72 72 72 72 72 65 65 65 65 65 65 65
## [10081] 72 72 65 65 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 24
## [10105] 72 72 72 72 24 24 72 72 72 72 72 24 24 24 72 72 72 72 72 72 72 72 72 72
## [10129] 24 24 72 72 72 72 72 24 24 72 72 21 72 72 72 63 63 22 22 22 22 62 22 22
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## [10225] 62 62 66 66 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 62 62 62 62
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## [10273] 62 62 62 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 62 62 62 62 62 62
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## [10321] 62 62 62 22 22 22 22 22 22 22 22 22 22 22 22 22 22 62 62 62 62 62 62 62
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## [10369] 22 22 22 22 22 22 22 22 22 22 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10393] 62 62 62 62 62 62 62 62 62 62 62 62 22 22 22 22 22 22 22 22 22 22 22 22
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## [10441] 62 62 62 62 62 62 62 62 62 62 62 22 22 22 22 22 22 22 62 62 62 62 62 62
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## [10489] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10513] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10537] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
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## [10585] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10609] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10633] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10657] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10681] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62
## [10705] 62 62 62 62 62 62 62 62 62 62 62 26 26 26 62 62 62 62 62 62 62 62 62 62
## [10729] 26 26 62 62 62 62 62 62 62 62 62 62 62 62 26 26 62 20 62 26 26 26 26 26
## [10753] 26 26 94 94 26 26 26 26 26 94 94 26 26 26 26 26 26 26 26 26 26 26 26 26
## [10777] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [10801] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [10825] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [10849] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
## [10873] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 94 26 26 26 26
## [10897] 22 22 26 26 26 26 26 26 26 26 26 26 26 26 22 22 26 26 26 26 26 26 26 26
## [10921] 26 26 26 26 51 51 89 89 89 89 89 89 89 89 89 89 51 51 89 89 89 89 89 89
## [10945] 89 89 89 51 89 89 89 89 89 89 89 89 89 51 51 51 51 89 89 89 89 89 89 89
## [10969] 89 89 89 89 51 51 51 51 89 89 89 89 89 89 89 89 89 89 89 89 89 89 51 51
## [10993] 51 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [11017] 89 89 89 89 51 51 51 69 69 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [11041] 89 51 51 51 51 69 69 89 89 89 89 89 89 89 89 89 51 51 51 89 89 89 89 89
## [11065] 89 89 89 89 89 51 51 51 51 51 89 89 89 89 89 89 89 89 89 89 89 89 89 89
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## [11113] 89 89 89 69 89 89 89 89 89 89 69 69 89 89 89 89 89 89 69 69 69 69 69 89
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## [11161] 89 69 69 69 69 69 69 69 69 89 89 89 89 89 89 89 89 89 89 89 27 27 27 27
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## [11209] 27 69 69 69 69 69 69 89 89 6 6 6 6 6 6 60 27 27 27 27 27 27 27 69
## [11233] 69 69 69 69 69 69 69 69 89 89 89 89 89 6 6 6 6 6 6 60 60 60 60 60
## [11257] 60 60 60 60 27 27 27 27 27 27 27 69 69 69 69 69 69 69 69 69 69 89 89 89
## [11281] 89 89 89 6 6 6 6 6 6 6 60 60 60 60 60 60 60 60 27 27 27 27 27 69
## [11305] 69 69 69 69 69 69 89 89 89 89 89 89 89 6 6 6 60 60 60 60 60 60 60 27
## [11329] 27 27 27 69 69 69 69 69 69 69 89 89 89 89 6 6 6 6 6 6 60 60 60 60
## [11353] 60 60 27 27 27 69 69 69 69 69 69 69 89 89 89 89 6 6 6 60 60 60 60 60
## [11377] 51 69 69 69 69 69 69 69 89 89 89 89 6 6 6 60 60 60 51 69 69 69 69 69
## [11401] 89 89 89 89 89 89 6 6 6 6 6 6 6 60 60 51 51 69 69 69 69 69 69 69
## [11425] 69 69 89 89 89 89 89 89 6 6 6 60 60 60 60 51 69 69 69 69 69 89 89 89
## [11449] 89 6 60 60 60 60 60 60 60 60 69 69 69 69 69 89 89 89 89 60 60 60 60 60
## [11473] 60 60 60 60 60 69 69 69 69 69 69 89 89 6 60 60 60 69 69 89 6 60 60 60
## [11497] 60 69 69 69 69 89 60 60 60 60 60 69 69 69 69 89 60 60 60 60 60 60 60 60
## [11521] 35 35 69 69 69 69 69 69 69 69 60 60 60 60 60 60 60 60 60 35 69 69 69 69
## [11545] 69 69 69 69 60 60 60 60 60 60 60 69 69 69 69 69 69 69 69 69 60 60 60 60
## [11569] 60 60 60 35 51 69 69 69 69 69 69 69 60 60 60 60 51 69 69 69 69 69 69 60
## [11593] 60 69 69 69 69 69 69 69 69 35 35 35 35 35 35 35 35 69 69 69 69 69 69 69
## [11617] 69 89 89 89 35 35 69 69 69 69 69 69 89 89 60 60 69 69 69 89 89 89 60 60
## [11641] 60 60 35 35 69 69 69 69 89 89 89 60 60 35 35 69 89 89 89 60 60 69 60 60
## [11665] 35 35 69 69 69 60 60 35 69 69 69 69 60 60 69 69 69 69 69 60 60 51 51 69
## [11689] 69 69 69 89 89 89 60 60 60 51 51 69 69 69 69 89 89 89 60 69 69 69 89 89
## [11713] 60 60 60 60 60 60 51 69 69 69 89 89 6 60 60 60 60 60 60 60 69 69 89 89
## [11737] 89 48 48 48 48 48 6 6 6 6 60 60 60 60 60 60 60 60 60 69 69 69 89 89
## [11761] 48 48 48 48 48 48 48 48 6 6 6 6 60 60 60 60 51 51 51 51 69 69 69 48
## [11785] 48 48 48 48 48 48 48 48 6 6 6 6 6 6 60 60 60 60 68 68 71 71 71 71
## [11809] 71 71 71 71 71 40 40 40 80 80 80 80 80 80 49 49 49 49 49 49 91 91 91 91
## [11833] 91 68 68 71 71 71 71 71 71 71 40 40 40 40 40 40 80 80 80 80 80 80 49 49
## [11857] 49 49 49 49 91 91 91 91 68 68 71 71 71 40 40 40 40 40 40 40 40 40 40 40
## [11881] 80 80 80 80 80 80 49 49 49 49 49 49 49 49 91 91 91 91 68 68 68 68 68 71
## [11905] 71 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 49 49 49 49 49 49
## [11929] 49 49 49 49 49 49 91 91 91 91 91 68 68 68 68 68 68 68 68 68 71 71 71 71
## [11953] 71 71 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 49 49 49 49 49 49
## [11977] 49 49 49 49 49 49 49 49 49 91 91 91 68 68 68 68 68 68 68 68 68 68 71 71
## [12001] 71 71 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 49 49 49 49 49 49 49
## [12025] 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71
## [12049] 71 71 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 80 80 49 49
## [12073] 49 49 49 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 71
## [12097] 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 80 80 80 80 49 49
## [12121] 49 49 49 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68
## [12145] 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 40 40 40 40
## [12169] 40 40 40 40 80 80 80 80 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68
## [12193] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71
## [12217] 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [12241] 80 80 80 80 80 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68
## [12265] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71
## [12289] 71 71 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 49 49 49 49 68 68 68
## [12313] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12337] 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40
## [12361] 40 40 40 40 40 40 40 40 40 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12385] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12409] 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40
## [12433] 40 40 40 40 40 40 40 40 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12457] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71
## [12481] 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
## [12505] 30 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12529] 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 40
## [12553] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 49 49 49 49
## [12577] 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12601] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71
## [12625] 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 86 86 80 80
## [12649] 80 80 80 80 80 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12673] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12697] 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40
## [12721] 40 40 40 40 40 40 40 86 86 86 80 80 80 80 80 80 80 80 49 49 49 68 68 68
## [12745] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12769] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71
## [12793] 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 40 86 80 80 80 80 80 80
## [12817] 80 80 80 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12841] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12865] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71
## [12889] 40 40 40 40 40 40 40 40 40 40 86 80 80 80 80 80 80 49 49 68 68 68 68 68
## [12913] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [12937] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 40
## [12961] 40 40 40 40 40 40 40 80 80 80 80 80 80 80 80 49 49 49 49 49 49 49 49 49
## [12985] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13009] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13033] 68 68 68 68 68 68 71 71 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 80
## [13057] 80 49 49 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68
## [13081] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13105] 68 68 68 68 68 68 68 68 68 68 68 68 68 71 40 40 40 40 40 40 40 40 40 40
## [13129] 40 40 40 40 40 80 80 80 80 80 80 80 80 49 49 49 49 49 49 49 49 49 49 49
## [13153] 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13177] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71
## [13201] 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80
## [13225] 80 80 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49
## [13249] 91 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13273] 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 40
## [13297] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 86 80 80 80 80 49 49 49 49 49
## [13321] 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 91 91 91 91 68 68 68 68
## [13345] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71
## [13369] 71 71 71 40 40 40 40 40 40 40 40 40 40 86 80 80 80 80 49 49 49 49 49 49
## [13393] 49 49 49 49 49 49 49 49 49 49 49 49 49 49 91 91 91 91 68 68 68 68 68 71
## [13417] 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40
## [13441] 40 40 80 80 80 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 91 68
## [13465] 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40
## [13489] 40 80 80 80 80 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 68
## [13513] 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40
## [13537] 40 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 91 91 68 68 68
## [13561] 68 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 49 49 49
## [13585] 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68
## [13609] 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40
## [13633] 40 40 40 40 40 40 40 86 86 86 80 49 49 49 49 49 49 49 49 49 49 49 49 49
## [13657] 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13681] 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 40 86
## [13705] 86 86 80 80 80 49 49 49 49 49 49 49 49 49 49 49 49 49 91 91 68 68 68 68
## [13729] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71
## [13753] 71 40 40 40 40 40 40 40 40 40 40 40 86 86 80 80 49 49 49 49 49 49 49 49
## [13777] 49 49 49 49 49 49 49 49 49 49 49 49 49 91 91 68 68 68 68 68 68 68 68 68
## [13801] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71
## [13825] 71 40 40 40 40 40 40 40 40 40 40 40 80 49 49 49 49 49 49 49 49 49 49 49
## [13849] 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13873] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71
## [13897] 71 71 71 40 40 40 40 40 40 40 40 40 80 80 80 80 49 49 49 49 49 49 49 49
## [13921] 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [13945] 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [13969] 71 40 40 40 40 40 40 40 40 40 40 40 86 86 80 80 80 80 80 80 49 49 49 49
## [13993] 49 49 49 49 49 49 49 49 49 49 49 91 68 68 68 68 68 68 68 68 68 68 68 68
## [14017] 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71
## [14041] 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 80 49 49
## [14065] 49 49 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68
## [14089] 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14113] 71 71 71 71 71 71 40 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 80 49
## [14137] 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [14161] 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14185] 71 40 40 40 40 40 40 40 40 40 40 40 80 80 80 80 49 49 49 49 49 49 49 49
## [14209] 49 49 49 49 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71
## [14233] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40
## [14257] 40 40 40 40 40 80 80 80 80 49 49 49 49 49 49 49 49 49 49 68 68 68 68 68
## [14281] 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14305] 71 71 71 71 71 40 40 40 40 40 80 80 80 49 49 49 49 49 49 49 49 49 49 30
## [14329] 30 30 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71
## [14353] 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 80 80 80 49 49 49 49 49
## [14377] 49 49 49 49 49 49 49 30 30 30 30 30 30 68 68 68 68 68 68 68 71 71 71 71
## [14401] 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 40 40 80 80 80 80 80 80
## [14425] 80 49 49 49 49 49 49 49 49 49 49 49 49 49 30 30 30 68 68 68 68 68 68 68
## [14449] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40 40 40 86
## [14473] 86 86 80 80 80 49 49 49 49 49 49 49 49 49 49 30 30 30 30 30 68 68 68 68
## [14497] 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 40 40
## [14521] 40 86 86 86 86 86 80 80 80 49 49 49 49 49 49 49 49 49 49 49 49 49 30 30
## [14545] 30 30 30 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14569] 71 71 71 71 40 40 40 86 86 86 86 86 49 49 49 49 49 49 49 49 49 49 49 30
## [14593] 30 30 30 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14617] 71 71 71 71 71 71 71 71 40 40 40 86 86 86 86 86 86 86 86 49 49 49 49 49
## [14641] 49 49 49 49 49 49 30 30 30 30 30 68 68 68 68 68 68 71 71 71 71 71 71 71
## [14665] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86
## [14689] 86 86 86 86 86 86 86 86 86 80 49 49 49 49 49 49 49 49 49 49 49 30 30 30
## [14713] 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71
## [14737] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86
## [14761] 86 86 86 80 49 49 49 49 49 49 49 49 49 49 49 30 30 30 30 30 30 30 30 30
## [14785] 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71
## [14809] 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86 86 86 80 80 49 49 49
## [14833] 49 49 49 49 49 49 30 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68
## [14857] 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14881] 71 71 71 71 71 86 86 86 86 86 86 86 86 86 86 80 49 49 49 49 49 49 49 49
## [14905] 49 49 49 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 68 68
## [14929] 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [14953] 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86 86 86 86 49 49 49 49
## [14977] 49 49 49 49 49 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68
## [15001] 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71
## [15025] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86
## [15049] 86 86 86 86 49 49 49 49 49 49 49 49 49 49 49 49 30 30 30 30 30 30 68 68
## [15073] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [15097] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [15121] 71 71 71 71 71 71 40 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
## [15145] 84 49 49 49 49 49 49 49 49 49 49 49 49 91 91 30 30 30 30 30 30 30 30 30
## [15169] 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [15193] 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [15217] 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86 86 86 86 86
## [15241] 86 86 86 84 84 49 49 49 49 49 49 49 49 30 30 30 30 30 30 30 30 30 30 30
## [15265] 30 30 30 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 68 68
## [15289] 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71
## [15313] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86 86
## [15337] 86 86 84 84 84 84 49 49 49 49 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [15361] 30 30 30 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 68 68
## [15385] 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [15409] 71 71 71 71 71 71 86 86 86 86 86 86 86 86 86 84 84 84 49 49 49 49 49 91
## [15433] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 68
## [15457] 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71
## [15481] 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86 49 49 49 49 49
## [15505] 91 91 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [15529] 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
## [15553] 68 68 68 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
## [15577] 71 71 71 86 86 86 86 86 49 49 49 49 91 91 91 30 30 30 30 30 30 30 30 30
## [15601] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 68 68 68 68 68
## [15625] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71 71 71 71
## [15649] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86
## [15673] 86 86 86 86 84 84 84 84 49 91 91 30 30 30 30 30 30 30 30 30 30 30 30 30
## [15697] 30 30 30 30 30 30 30 30 30 30 30 30 30 68 68 68 68 68 68 68 68 68 68 68
## [15721] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 71 71 71 71 71
## [15745] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 86 86 86 86 86 86
## [15769] 86 86 84 84 84 84 84 84 84 49 91 91 91 53 53 53 53 53 53 53 53 53 53 53
## [15793] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57 57 57 57 57 57 57
## [15817] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41
## [15841] 41 41 41 41 41 41 41 41 41 41 41 41 41 9 9 9 9 9 9 9 12 12 12 12
## [15865] 12 12 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [15889] 53 53 53 53 53 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [15913] 57 57 57 57 57 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [15937] 41 41 9 9 9 9 9 12 12 12 12 12 43 43 53 53 53 53 53 53 53 53 53 53
## [15961] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57 57 57 57 57 57 57 57 57
## [15985] 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41 41 41 41 41
## [16009] 41 41 41 41 41 41 41 41 41 41 41 9 9 12 12 12 12 12 12 12 53 53 53 53
## [16033] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57
## [16057] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16081] 57 57 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [16105] 41 41 12 12 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16129] 53 53 53 53 53 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16153] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41
## [16177] 41 41 41 41 41 41 41 41 41 43 43 43 43 43 43 53 53 53 53 53 53 53 53 53
## [16201] 53 53 53 53 53 53 53 53 53 53 53 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16225] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16249] 57 57 57 57 57 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
## [16273] 43 43 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57 57 57 57
## [16297] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16321] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41 41 41
## [16345] 41 41 41 41 41 41 41 41 41 41 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16369] 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16393] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41
## [16417] 41 41 41 41 41 41 41 41 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16441] 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16465] 57 57 57 57 57 57 57 57 57 57 57 57 57 41 41 41 41 41 41 41 41 41 53 53
## [16489] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57 57
## [16513] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 41
## [16537] 41 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16561] 53 53 53 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
## [16585] 57 57 57 57 57 57 57 57 57 41 41 43 53 53 53 53 53 53 53 53 53 53 53 53
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## [16633] 57 57 57 57 57 57 57 57 57 41 41 41 53 53 53 53 53 53 53 53 53 53 53 53
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## [16681] 57 57 57 57 57 57 57 57 41 41 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16705] 53 53 53 53 53 53 53 53 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
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## [16801] 9 9 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57
## [16825] 57 57 57 57 57 9 9 53 53 53 53 53 53 53 53 53 53 53 53 53 53 57 57 57
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## [16897] 53 53 9 9 9 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 9
## [16921] 9 9 9 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16945] 53 53 53 9 9 9 9 9 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [16969] 53 53 53 53 53 53 53 53 9 9 9 43 43 53 53 53 53 53 53 53 53 53 53 53
## [16993] 53 53 53 53 53 53 53 53 53 53 53 9 9 43 43 53 53 53 53 53 53 53 53 53
## [17017] 53 53 53 53 53 53 53 53 53 53 53 53 53 57 13 9 43 43 53 53 53 53 53 53
## [17041] 53 53 53 53 53 53 53 53 53 53 53 13 9 9 43 43 43 43 53 53 53 53 53 53
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## [17113] 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 53 53 53 53 53 53 53
## [17137] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 43 43
## [17161] 43 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17185] 53 53 53 53 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 53 53 53
## [17209] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43
## [17233] 43 43 43 43 43 43 43 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17257] 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 43 43 43 53
## [17281] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 29 29
## [17305] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 13 13 43 43 43 43 43 43
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## [17377] 53 53 53 53 53 43 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17401] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17425] 53 53 53 53 53 53 53 53 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 43
## [17449] 43 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 53 53 53 53 53 53 53
## [17473] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 43
## [17497] 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17521] 53 53 53 53 53 43 43 43 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17545] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 29 29 53 53 53
## [17569] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17593] 53 53 53 53 53 43 43 43 43 29 29 29 53 53 53 53 53 53 53 53 53 53 53 53
## [17617] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 29 29 29 53 53
## [17641] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17665] 53 53 53 53 43 43 43 43 29 29 29 29 53 53 53 53 53 53 53 53 53 53 53 53
## [17689] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 43 29
## [17713] 29 29 29 29 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17737] 53 53 53 53 53 53 53 53 53 53 53 43 43 43 43 43 43 43 29 29 29 29 29 29
## [17761] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17785] 53 53 43 43 43 43 43 43 43 29 29 29 29 29 29 53 53 53 53 53 53 53 53 53
## [17809] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 29 29 29
## [17833] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17857] 53 53 53 53 53 53 29 29 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17881] 53 53 53 53 53 53 53 53 53 53 29 29 29 53 53 53 53 53 53 53 53 53 53 53
## [17905] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 29 29 29 29
## [17929] 29 29 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [17953] 53 53 53 53 43 43 43 29 29 29 29 29 53 53 53 53 53 53 53 53 53 53 53 53
## [17977] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43 29 29 29 29 29
## [18001] 29 29 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
## [18025] 53 53 53 53 43 43 43 43 43 43 43 43 29 29 29 29 29 29 29 29 29 53 53 53
## [18049] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 43 43 43
## [18073] 43 43 43 43 43 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59
## [18097] 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 10 10 10 10 10 38 38
## [18121] 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [18145] 59 59 10 10 10 10 10 10 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59
## [18169] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 38 38 38 38 38
## [18193] 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [18217] 59 59 59 59 59 10 10 10 10 10 38 38 38 38 59 59 59 59 59 59 59 59 59 59
## [18241] 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 38 38 38 38 38 38
## [18265] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [18289] 10 10 10 10 10 10 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59
## [18313] 59 59 59 59 59 59 59 59 10 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59
## [18337] 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 38 38 38 59 59 59 59 59
## [18361] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 38 38 38
## [18385] 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 38 38
## [18409] 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 38
## [18433] 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10
## [18457] 10 10 10 10 10 38 38 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59
## [18481] 59 59 59 59 59 59 59 59 10 10 10 10 38 38 38 38 38 38 38 38 38 38 38 59
## [18505] 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 38 38 38 38 38 38 38 38 38
## [18529] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 38 38 38 38 38
## [18553] 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 38
## [18577] 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 10 10 10 10 10 10
## [18601] 10 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 38
## [18625] 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 38
## [18649] 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 38 38 38 38
## [18673] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 10 10 10 10 10 10
## [18697] 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 10 10
## [18721] 10 10 10 38 38 38 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59
## [18745] 59 59 59 59 59 59 59 59 59 7 7 10 10 10 10 10 38 38 38 38 38 38 38 38
## [18769] 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [18793] 59 59 59 10 10 10 10 10 38 38 38 38 38 38 38 38 38 38 38 38 38 38 59 59
## [18817] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 10 10 10 38 38 38 38 38
## [18841] 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 10 10
## [18865] 10 10 10 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [18889] 59 59 59 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 38 38 38 38 38
## [18913] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 10 10
## [18937] 10 10 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 7 10 10 10 10 10
## [18961] 10 10 10 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 7 10 10 10
## [18985] 10 10 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 7 10 10 10 10
## [19009] 10 10 10 10 10 10 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59
## [19033] 7 10 10 10 10 10 10 10 10 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59
## [19057] 59 59 59 59 59 7 7 10 10 10 10 10 10 10 38 38 38 59 59 59 59 59 59 59
## [19081] 59 59 59 59 59 59 59 59 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59
## [19105] 59 10 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 7 7 10 10 10 10
## [19129] 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 10 10 10
## [19153] 10 10 10 10 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7
## [19177] 7 7 10 10 10 10 10 10 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59
## [19201] 59 59 59 59 59 59 59 59 7 7 10 10 10 10 38 38 38 38 38 38 38 59 59 59
## [19225] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 38 38 38
## [19249] 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19273] 10 10 10 10 10 38 38 38 38 38 38 38 38 38 38 38 38 38 38 59 59 59 59 59
## [19297] 59 59 59 59 59 59 59 10 10 10 10 38 38 38 38 38 38 38 38 38 38 38 38 59
## [19321] 59 59 59 59 59 59 59 59 59 59 59 7 10 10 38 38 38 38 38 38 38 59 59 59
## [19345] 59 59 59 59 59 59 59 59 59 59 7 10 10 10 10 38 38 38 38 38 38 59 59 59
## [19369] 59 59 59 59 59 10 38 38 38 38 38 38 59 59 59 59 59 59 59 7 7 10 10 38
## [19393] 38 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 7 10
## [19417] 10 10 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19441] 59 59 59 59 7 7 7 10 10 10 10 10 38 38 38 38 38 38 38 38 59 59 59 59
## [19465] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 10 10 10 10 10
## [19489] 38 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19513] 59 59 59 10 10 10 10 10 10 38 38 38 38 38 38 38 38 38 59 59 59 59 59 59
## [19537] 59 59 59 59 59 59 59 59 59 59 59 10 10 10 10 10 10 10 10 38 38 38 38 38
## [19561] 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 10 10 10
## [19585] 10 10 10 10 10 38 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59
## [19609] 59 59 59 59 59 59 10 10 10 10 10 10 10 10 10 10 10 10 38 38 38 38 38 38
## [19633] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 10 10 10
## [19657] 10 10 10 10 10 10 10 10 10 10 10 10 10 38 38 38 38 38 38 38 59 59 59 59
## [19681] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 10 10 10 10
## [19705] 10 10 10 10 10 10 10 10 10 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59
## [19729] 59 59 59 59 59 59 59 59 59 7 7 7 7 7 10 10 10 10 10 10 10 10 10 38
## [19753] 38 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19777] 59 59 59 59 59 59 7 7 7 7 7 7 7 7 10 10 10 10 10 10 10 38 38 38
## [19801] 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19825] 59 59 59 59 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 10 10 10 10
## [19849] 10 10 10 10 10 10 38 38 38 38 38 59 59 59 59 59 59 59 59 59 59 59 59 59
## [19873] 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 7 7 7 7 7 7 10
## [19897] 10 10 10 10 10 10 10 10 10 10 10 10 10 38 38 38 38 38 38 59 59 59 59 59
## [19921] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 7 7 7 7 7 7 7
## [19945] 7 7 7 7 7 10 10 10 10 10 10 10 10 10 10 10 10 10 54 54 52 52 52 52
## [19969] 52 52 52 52 52 52 52 52 52 52 52 52 52 47 47 47 47 47 47 47 47 47 47 47
## [19993] 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 54 54 52 52 52 52
## [20017] 52 52 52 52 52 52 52 52 52 52 52 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20041] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 54 54 54 54 54
## [20065] 54 52 52 52 52 52 52 52 52 52 52 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20089] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 54 54
## [20113] 54 54 52 52 52 52 52 52 52 52 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20137] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 54 54 54 54
## [20161] 52 52 52 52 52 52 52 52 50 50 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20185] 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 54 54 54 54 54
## [20209] 54 52 52 52 52 52 52 52 52 50 50 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20233] 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 54 54 54 54 54 52 52 52
## [20257] 52 52 52 52 52 50 50 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
## [20281] 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 16 54 54 54 54 54 54 54
## [20305] 52 52 52 52 52 52 52 52 50 50 50 50 50 47 47 47 47 47 47 47 47 47 47 47
## [20329] 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 54 54 54 54 54
## [20353] 52 52 52 52 52 52 52 52 52 50 50 50 82 47 47 47 47 47 47 47 47 47 47 47
## [20377] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16 16 16 16 16 16
## [20401] 54 54 54 54 54 52 52 52 52 52 52 52 52 50 50 82 82 47 47 47 47 47 47 47
## [20425] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 16 16
## [20449] 16 16 54 54 54 54 54 54 54 52 52 52 52 50 50 50 50 50 82 82 47 47 47 47
## [20473] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16
## [20497] 16 16 16 54 54 54 54 54 54 54 52 50 50 50 47 47 47 47 47 47 47 47 47 47
## [20521] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 16 16 54 54 54 54 54 54 54
## [20545] 54 52 52 52 52 52 50 50 50 87 87 82 82 47 47 47 47 47 47 47 47 47 47 47
## [20569] 47 47 47 47 47 47 47 47 47 16 16 54 54 54 54 54 54 54 54 52 52 52 52 52
## [20593] 52 52 75 75 75 50 50 50 87 87 87 87 87 82 82 82 82 47 47 47 47 47 47 47
## [20617] 47 47 47 47 47 47 47 16 54 54 54 54 54 54 54 54 52 52 52 52 52 52 52 52
## [20641] 52 52 52 52 75 75 50 50 50 50 87 87 87 87 87 82 82 82 82 82 47 47 47 47
## [20665] 47 47 47 47 47 47 16 16 54 54 54 54 54 52 52 52 52 52 52 52 75 75 50 50
## [20689] 50 50 50 50 87 87 87 82 82 82 47 47 47 47 47 47 47 47 47 47 47 47 47 16
## [20713] 16 16 16 16 54 54 54 54 54 52 52 52 52 52 52 50 50 50 50 50 82 82 82 82
## [20737] 47 47 47 47 47 47 47 47 47 47 47 47 16 16 16 54 54 54 52 52 52 52 52 52
## [20761] 52 52 50 50 50 82 82 82 82 82 47 47 47 47 47 47 47 47 47 47 47 47 47 16
## [20785] 16 54 54 54 54 54 52 52 52 52 52 52 52 52 52 52 50 50 50 50 50 50 50 87
## [20809] 87 82 82 82 82 47 47 47 47 47 47 47 47 47 54 52 52 52 52 52 52 52 52 52
## [20833] 52 50 50 50 50 50 50 50 50 87 82 82 82 82 82 82 82 47 47 47 47 47 47 54
## [20857] 52 52 52 52 52 52 52 52 50 50 50 82 82 82 82 82 82 47 47 47 47 52 52 52
## [20881] 52 52 52 52 52 52 52 50 50 82 82 82 82 82 47 47 47 47 52 52 52 52 52 52
## [20905] 52 50 50 82 47 47 47 52 52 52 52 50 50 50 50 50 50 47 47 52 52 52 52 52
## [20929] 52 50 50 50 50 50 50 47 47 47 52 52 52 50 50 50 87 47 47 47 52 52 52 52
## [20953] 75 75 75 50 50 50 50 87 87 82 82 47 47 47 54 52 52 52 52 52 52 75 75 75
## [20977] 50 87 87 82 82 82 82 47 47 47 54 52 52 52 52 87 87 87 87 82 82 47 47 47
## [21001] 47 47 47 47 52 52 75 87 87 87 87 87 82 82 47 47 47 47 47 47 47 47 75 75
## [21025] 75 87 87 87 87 82 82 82 82 82 56 47 47 47 47 47 47 47 47 75 75 75 75 50
## [21049] 50 87 87 87 87 87 82 82 82 82 82 56 56 56 47 47 47 47 47 75 75 75 50 50
## [21073] 87 87 87 87 87 87 82 56 56 56 56 56 56 56 47 47 47 47 47 47 47 47 75 75
## [21097] 75 50 50 50 50 87 87 87 87 87 87 56 56 56 56 56 56 56 56 56 47 47 47 47
## [21121] 47 50 50 50 50 50 87 87 87 87 87 87 87 87 56 56 56 56 56 56 56 47 47 50
## [21145] 50 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 56 56 56 56 56 56 50 87
## [21169] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 56 56 56 56 56 56 56 56 56 50
## [21193] 50 50 87 87 87 87 87 87 87 87 87 87 87 87 87 82 56 56 56 56 56 56 56 56
## [21217] 50 50 50 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 82 82 82 82 56
## [21241] 56 56 56 75 50 50 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [21265] 87 82 82 82 56 56 56 56 56 56 56 56 56 56 75 75 75 50 50 50 46 87 87 87
## [21289] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 82 82 82 56 56 56 56 56
## [21313] 56 56 56 56 56 56 56 56 56 56 56 56 54 54 75 75 75 50 87 87 87 87 87 87
## [21337] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 82 82 82 82 82 82 56 56 56
## [21361] 56 56 56 56 56 56 56 56 56 56 56 56 54 54 75 75 75 75 75 87 87 87 87 87
## [21385] 87 87 87 87 87 87 87 87 87 87 87 82 82 56 56 56 56 56 56 56 56 56 56 56
## [21409] 56 56 56 56 50 46 46 46 87 87 87 87 87 87 87 87 87 87 87 87 56 56 56 56
## [21433] 56 56 56 56 56 56 56 56 54 54 75 75 46 46 46 46 46 46 87 87 87 87 87 87
## [21457] 87 87 87 87 87 87 87 87 87 87 56 56 56 56 56 56 56 56 54 54 75 75 50 50
## [21481] 46 46 46 46 46 46 46 46 46 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [21505] 87 87 87 87 87 87 82 56 56 54 54 54 50 46 46 46 46 46 46 46 46 46 46 46
## [21529] 46 46 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 50 50 46 46
## [21553] 46 46 46 46 46 46 46 46 46 46 87 87 87 87 87 87 87 87 87 87 87 87 87 87
## [21577] 87 87 87 87 50 50 46 46 46 46 46 46 46 46 46 46 46 46 46 87 87 87 87 87
## [21601] 87 87 87 87 87 87 87 87 87 87 87 87 75 75 50 46 46 46 46 46 46 46 46 46
## [21625] 46 46 46 46 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 54 54 54 75 50
## [21649] 50 32 32 46 46 46 46 46 46 46 46 46 46 46 46 46 87 87 87 87 87 87 87 87
## [21673] 87 87 87 54 54 54 54 54 54 50 50 50 32 32 46 46 46 46 46 46 46 46 87 87
## [21697] 87 87 54 54 54 54 54 50 32 32 32 46 46 46 46 46 46 87 87 87 54 54 54 54
## [21721] 54 32 32 32 46 46 46 46 46 46 46 46 87 87 54 54 54 54 32 32 32 46 46 46
## [21745] 46 46 46 46 87 87 54 54 54 54 50 32 32 32 32 87 87 87 87 54 54 54 54 50
## [21769] 32 32 32 32 32 32 87 87 54 54 54 54 50 32 32 32 32 87 54 54 54 32 32 46
## [21793] 87 54 54 50 50 46 46 46 46 46 87 70 70 70 4 4 4 88 88 70 70 70 70 4
## [21817] 4 4 88 70 70 70 70 4 4 4 96 88 88 88 70 70 70 4 4 4 4 4 88 88
## [21841] 88 70 70 4 4 4 4 96 96 96 88 4 4 4 4 4 4 4 96 96 96 96 34 34
## [21865] 88 4 4 4 4 4 4 4 4 96 96 96 96 96 34 4 4 4 4 4 4 4 4 4
## [21889] 4 96 96 96 96 96 96 96 34 88 88 88 4 4 4 4 4 4 4 4 96 96 96 96
## [21913] 96 96 96 96 34 34 88 88 88 88 4 4 4 4 4 4 96 96 96 96 96 96 96 96
## [21937] 96 34 34 88 88 88 4 4 4 4 4 96 96 96 96 96 96 96 34 34 34 34 88 88
## [21961] 4 4 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 34 34 88 88 4
## [21985] 4 96 96 96 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 88 4 4
## [22009] 4 4 96 96 96 96 96 96 96 96 96 34 34 88 4 4 4 4 96 96 96 96 96 96
## [22033] 96 96 96 96 96 34 34 34 34 34 4 4 4 4 4 4 96 96 96 96 96 96 96 96
## [22057] 96 34 34 34 34 34 34 34 34 4 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22081] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 88 4 4 96 96 96 96 96
## [22105] 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34
## [22129] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 88 88 88 4 4 4
## [22153] 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22177] 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22201] 34 34 34 34 34 34 34 34 34 34 34 88 88 88 4 4 4 96 96 96 96 96 96 96
## [22225] 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22249] 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22273] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 88 88 88 4 4 4 96 96 96
## [22297] 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22321] 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22345] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22369] 34 34 88 4 4 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22393] 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22417] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 4
## [22441] 4 4 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 34 34
## [22465] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22489] 34 34 34 34 34 34 34 34 34 34 4 96 96 96 96 96 96 96 96 96 96 96 96 96
## [22513] 96 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22537] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 4 4 4 4 96 96 96
## [22561] 96 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22585] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 96 96
## [22609] 96 96 96 96 96 96 96 96 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22633] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 96 96 96 96 34 34 34 34
## [22657] 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 96 96 96 96
## [22681] 96 34 34 34 34 34 34 34 34 34 34 34 34 34 34 96 96 96 34 34 34 34 34 34
## [22705] 34 34 34 34 96 96 96 34 34 34 34 34 34 96 96 96 96 34 34 34 34 96 96 96
## [22729] 96 96 96 96 96 4 4 4 96 96 34 34 4 4 4 96 96 34 4 4 4 34 4 34
## [22753] 34 34 4 4 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34
## [22777] 34 4 4 4 4 55 73 73 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [22801] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [22825] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [22849] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [22873] 39 39 39 39 39 39 39 39
## 96 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 96