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04_evaluate.R
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04_evaluate.R
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## ---------------------------
##
##
## Author: Peter Horvath
##
## Date : 2021-10-02
##
##
## ---------------------------
##
## Notes:
## this script is the fourth stage in the process of producing a WALL-to-wall map of Vegetation types
## here we evaluate the three assembled maps using kappa
##
##
## ---------------------------
library(raster)
library(rgdal)
in_folder <- "E:/Project_1.5/OUTPUT_agri"
out_folder <- "E:/Project_1.5/OUTPUT_agri"
git_folder <- "C:/Users/peterhor/Documents/GitHub_UiO/Project_1.5/"
out_folder_maxval <- paste0(out_folder, "/DM_maxval/")
out_folder_preval <- paste0(out_folder, "/DM_preval/")
out_folder_AUC <- paste0(out_folder, "/DM_AUC/")
# read in DM *preliminary results from 3 DM attempts
#A method
DM_AUC <- raster(paste0(in_folder,"/DM_AUC/DM_AUC_fill.tif"))
#B method
DM_preval <- raster(paste0(in_folder,"/DM_preval/DM_preval_fill.tif"))
#C method
DM_maxval <- raster(paste0(in_folder,"/DM_maxVal/DM_maxval.tif"))
#### Eval 1 ####
# # compare overlap between 3 methods
# extent(DM_AUC)
#### OVERLAY VT - small extent ####
# test_ext <- extent(c(-2000,10000,6600000,6610000))
# #plot(DM_maxval)
# #plot(test_ext, add=TRUE)
# DM_AUC_test <- crop(DM_AUC, test_ext)
# DM_Maxval_test <- crop(DM_maxval, test_ext)
# DM_preval_test <- crop(DM_preval, test_ext)
#
# DM_AUC_test <- as.factor(DM_AUC_test)
# DM_Maxval_test <- as.factor(DM_Maxval_test)
# DM_preval_test <- as.factor(DM_preval_test)
#
# par(mfrow=c(1,2))
# plot(DM_AUC_test)
# plot(DM_Maxval_test)
#
# #spatialEco package
#
# library(spatialEco)
# r.kappa <- raster.change(DM_AUC_test,DM_Maxval_test)
# par(mfrow=c(1,3))
# plot(DM_AUC_test)
# plot(DM_Maxval_test)
# plot(r.kappa)
# table(values(DM_AUC_test))
# table(values(DM_Maxval_test))
# r1<- overlay(DM_AUC_test,DM_Maxval_test, fun=function(x, y){ x - y} )
# par(mfrow=c(1,3))
# plot(DM_AUC_test)
# plot(DM_Maxval_test)
# breakpoints <- c(-100,-0.1,0.1, 100)
# colors <- c("black","red", "black")
# plot(r1, col=colors, breaks=breakpoints)
# table(values(r1))
# sum(table(values(r1)))
#
# class.comparison(DM_AUC_test,DM_Maxval_test, x.idx = 8, y.idx = 8, stat="both") #does not work with rasters
# # try diffeR package
# library(diffeR)
#
# crosstabm(DM_AUC_test,DM_Maxval_test)
# MADscatterplot(DM_AUC_test,DM_Maxval_test)
# categoryComponentsPlot(DM_AUC_test,DM_Maxval_test)
# categorySourcesPlot(DM_AUC_test,DM_Maxval_test)
# differenceMR(DM_AUC_test,DM_Maxval_test, fact = 2)
# diffTablej(crosstabm(DM_AUC_test,DM_Maxval_test))
# exchangeDij(crosstabm(DM_AUC_test,DM_Maxval_test))
# exchangeDj(crosstabm(DM_AUC_test,DM_Maxval_test))
# overallAllocD(crosstabm(DM_AUC_test,DM_Maxval_test))
# overallComponentsPlot(DM_AUC_test,DM_Maxval_test)
# overallSourcesPlot(DM_AUC_test,DM_Maxval_test)
# overallDiffCatj(crosstabm(DM_AUC_test,DM_Maxval_test))
# # find intersection of all three rasters
# r3<- overlay(DM_AUC_test,DM_Maxval_test,DM_preval_test, fun=function(x, y, z){(x==y)*(x==z)*(y==z)} )
# plot(r3)
# table(values(r3))
#### OVERLAY VT whole raster ####
#first testing subtraction
# r_whole <- overlay(DM_AUC,DM_maxval, fun=function(x, y){ x - y} )
# par(mfrow=c(1,3))
# plot(DM_AUC)
# plot(DM_maxval)
# breakpoints <- c(-100,-0.1,0.1, 100)
# colors <- c("black","red", "black")
# plot(r_whole, col=colors, breaks=breakpoints)
# but better with binary tests
# table(values(DM_AUC))
# table(values(DM_preval))
# table(values(DM_maxval))
rAB <- overlay(DM_AUC,DM_preval, fun=function(x, y){(x==y)} )
rAC <- overlay(DM_AUC,DM_maxval, fun=function(x, y){(x==y)} )
rBC <- overlay(DM_preval, DM_maxval, fun=function(x, y){(x==y)} )
rABC <- overlay(DM_AUC,DM_preval,DM_maxval, fun=function(x, y, z){(x==y)*(x==z)*(y==z)} )
par(mfrow=c(2,2))
plot(rAB, main="AUC vs Preval")
plot(rAC, main="AUC vs Maxval")
plot(rBC, main="Preval vs Maxval")
plot(rABC, main="AUC vs Preval vs Maxval")
# create table to output EVALUATION data into
nrows <- length(32)
comp_ABC <- data.frame(AB_TRUE=integer(nrows), AB_FALSE=integer(nrows), AC_TRUE=integer(nrows),
AC_FALSE=integer(nrows),BC_TRUE=integer(nrows), BC_FALSE=integer(nrows),
ABC_TRUE =integer(nrows), ABC_FALSE=integer(nrows))
comp_ABC[1,2:1] <- t(table(values(rAB)))
comp_ABC[1,4:3] <- t(table(values(rAC)))
comp_ABC[1,6:5] <- t(table(values(rBC)))
comp_ABC[1,8:7] <- t(table(values(rABC)))
#percentage agreement vs total
comp_ABC[2,2:1]<-comp_ABC[1,1]/(comp_ABC[1,1]+comp_ABC[1,2])
comp_ABC[2,4:3]<-comp_ABC[1,3]/(comp_ABC[1,3]+comp_ABC[1,4])
comp_ABC[2,6:5]<-comp_ABC[1,5]/(comp_ABC[1,5]+comp_ABC[1,6])
comp_ABC[2,8:7]<-comp_ABC[1,7]/(comp_ABC[1,7]+comp_ABC[1,8])
write.csv(comp_ABC, paste0(out_folder, "overlay_3methods.csv"))
# tab <- data.frame(ncol(4), nrow(3))
# tab <- data.frame(AB=integer(3), AC=integer(nrows), BC=integer(nrows), ABC =integer(nrows))
# tab[2:1,1] <- t(table(values(rAB)))
# tab[2:1,2] <- t(table(values(rAC)))
# tab[2:1,3] <- t(table(values(rBC)))
# tab[2:1,4] <- table(values(rABC))
# SAVE OUTPUTS
writeRaster(rAB, filename = paste0(out_folder,"compare_AUC_preval.tiff"),format = "GTiff")
writeRaster(rAC, filename = paste0(out_folder,"compare_AUC_maxval.tiff"),format = "GTiff")
writeRaster(rBC, filename = paste0(out_folder,"compare_preval_maxval.tiff"),format = "GTiff")
writeRaster(rABC, filename = paste0(out_folder,"compare_all3.tiff"),format = "GTiff")
# rAB <- raster(paste0(out_folder, "Overlay_3Methods/","DM_AUC_preval_agreement.tif"))
# rAC <- raster(paste0(out_folder,"Overlay_3Methods/","DM_AUC_maxval_agreement.tif"))
# rBC <- raster(paste0(out_folder,"Overlay_3Methods/","DM_preval_maxval_agreement.tif"))
# rABC <- raster(paste0(out_folder, "Overlay_3Methods/","DM_all3_agreement.tif"))
# https://rdrr.io/github/environmentalinformatics-marburg/Rsenal/man/kstat.html
##### Eval 2 ####
#prepare evaluation data#
library(rgdal)
library(dplyr)
in_shp <- "D:/Spatial_DATA/NIBIO_vegetation_mapping/"
# AR 18x18
ar <- readOGR(dsn = paste0(in_shp, "AR18x18"), layer = "n18x18")
table(ar$VEG1)
#load in translation rules
transl <- read.csv(paste0(git_folder, "transl_VT_DMvt.csv"), sep = ";", dec = ".")
transl <- rename(transl, VEG1 = VT_code)
str(transl)
#
#
# ### Eval 2 - small extent ####
# ar_sub <- subset(ar, ar@data$FLATE_NR %in% 509)
# #how many polygons of each VT
# table(ar_sub$VEG1)
# #how large area of each VT
# tapply(ar_sub$AREA,ar_sub$VEG1, sum)
# ar_sub@data <- left_join(ar_sub@data, transl, by="VEG1")
# head(ar_sub@data)
#
# # transl <- matrix(nrow= 31, ncol = 2)
# # # make into reclassify matrix # can also to like this cbind(transl_rule_DM$VT_ID, transl_rule_DM$PFT_ID)
# # for(i in 1:NROW(transl_rule_DM)){
# # transl[i,] <- c(transl_rule_DM$VT_ID[i], transl_rule_DM$PFT_ID[i])
# # # print(transl)
# # }
#
#
# # overlap with the evaluation data
# plot(DM_Maxval_test)
# plot(ar_sub, add=TRUE)
# # adjust all to cover the same area
# DM_AUC_509 <- crop(DM_AUC_test, ar_sub)
# extent(DM_AUC_509)
# plot(DM_AUC_509)
# #rasterize the 509 test polygon
# ar_sub_r <- rasterize(ar_sub, DM_AUC_509, field="DMVT_ID")
#
#
# par(mfrow=c(3,1))
# #par(mfrow=c(2,1))
# plot(ar_sub_r)
# plot(ar_sub, add=TRUE)
# polygonsLabel(ar_sub, ar_sub$VEG1, method = "buffer", cex=.8)
# plot(ar_sub_r)
# text(ar_sub_r)
# plot(DM_AUC_509)
# text(DM_AUC_509)
# # Using library spatialEco for calculating kappa
# library(spatialEco)
# r1 <- DM_AUC_509
# r2 <- ar_sub_r
# s = 3
# r.kappa <- raster.change(r1, r2, d = s, mask = TRUE)
# r.ttest <- raster.change(r1, r2, d = s, stat="t.test", mask = TRUE)
# r.ent <- raster.change(r1, r2, d = s, stat="entropy", mask = TRUE)
# r.cor <- raster.change(r1, r2, d = s, stat="cor", mask = TRUE)
# r.ce <- raster.change(r1, r2, d = s, stat = "cross-entropy", mask = TRUE)
# r.kl <- raster.change(r1, r2, d = s, stat = "divergence", mask = TRUE)
# par(mfrow=c(3,2))
# plot(r.kappa, main="Kappa")
# plot(r.ttest[[1]], main="Paired t-test")
# plot(r.ent, main="Delta Entropy")
# plot(r.cor, main="Rank Correlation")
# plot(r.kl, main="Kullback-Leibler")
# plot(r.ce, main="cross-entropy")
#
# # save
# writeRaster(ar_sub_r, filename = paste0(out_folder, "ref_plot_509.tif"), format="GTiff", overwrite=TRUE)
# writeRaster(DM_AUC_509, filename = paste0(out_folder, "mod_plot_509.tif"), format="GTiff", overwrite=TRUE)
# writeRaster(ar_sub_r, filename = paste0(out_folder, "ref_plot_509.asc"), format="ascii", overwrite=TRUE)
# writeRaster(DM_AUC_509, filename = paste0(out_folder, "mod_plot_509.asc"), format="ascii", overwrite=TRUE)
####
####
### MASK OUT EVALUATION - whole extent####
# AR 18x18 ####
# mask raster AR18x18
fast_processing <- "C:/DATA_fast_processing/"
ar_dissolved <- readOGR(dsn = paste0(fast_processing, "AR18x18"), layer = "ar18x18_dissolved") # dissolved in QGIS for better efficiency of masking
extent(ar_dissolved)
plot(DM_maxval)
plot(ar_dissolved, add=TRUE)
#mask the three methods by the AR18 dataset
AR18_DM_maxval <- mask(DM_maxval, ar_dissolved)
writeRaster(AR18_DM_maxval, filename = paste0(out_folder_maxval, "AR18_DM_maxval.tif"), format="GTiff", overwrite=TRUE)
AR18_DM_preval <- mask(DM_preval, ar_dissolved)
writeRaster(AR18_DM_preval, filename = paste0(out_folder_preval, "AR18_DM_preval.tif"), format="GTiff", overwrite=TRUE)
AR18_DM_AUC <- mask(DM_AUC, ar_dissolved)
writeRaster(AR18_DM_AUC, filename = paste0(out_folder_AUC, "AR18_DM_AUC.tif"), format="GTiff", overwrite=TRUE)
#library(rgdal)
#AR18_DM_maxval <- raster(paste0(out_folder, "AR18_DM_maxval.tif"))
# rasterize reference dataset based on a rule from reclassification table
library(dplyr)
#load in reclassification rules
transl <- read.csv(paste0(git_folder, "transl_VT_DMvt.csv"), sep = ";", dec = ".")
transl <- rename(transl, VEG1 = VT_code)
str(transl)
table(ar$VEG1)
ar@data <- left_join(ar@data, transl, by="VEG1")
head(ar@data)
#rasterize reference dataset AR18
AR18_ref <- rasterize(ar, DM_maxval, field="DMVT_ID")
writeRaster(AR18_ref, filename = paste0(out_folder, "AR18_ref.tif"), format="GTiff", overwrite=TRUE)
#AR18_ref <- raster(paste0(out_folder, "AR18_ref.tif"))
####
####
# AR 9X9 ####
# similar procedure as above
# crop and mask raster AR9x9
ar9_dissolved <- readOGR(dsn = paste0(fast_processing, "AR9x9"), layer = "AR_9x9_dissolved")
DM_maxval_mask9 <- mask(DM_maxval, ar9_dissolved)
writeRaster(DM_maxval_mask9, filename = paste0(out_folder_maxval, "AR9_DM_maxval.tif"), format="GTiff", overwrite=TRUE)
DM_preval_mask9 <- mask(DM_preval, ar9_dissolved)
writeRaster(DM_preval_mask9, filename = paste0(out_folder_preval, "AR9_DM_preval.tif"), format="GTiff", overwrite=TRUE)
DM_AUC_mask9 <- mask(DM_AUC, ar9_dissolved)
writeRaster(DM_AUC_mask9, filename = paste0(out_folder_AUC, "AR9_DM_AUC.tif"), format="GTiff", overwrite=TRUE)
# rasterize AR9x9 Maps
ar9 <- readOGR(dsn = paste0(in_folder, "AR9x9"), layer = "AR_9xFortett_WGS")
head(ar9@data)
as.data.frame(table(ar9$VEG1))
#load in translation rules
transl <- read.csv(paste0(git_folder, "transl_VT_DMvt_ar9.csv"), sep = ";", dec = ".")
transl <- rename(transl, VEG1 = VT_code)
table(ar9$VEG1)
ar9@data <- left_join(ar9@data, transl, by="VEG1")
head(ar9@data)
# rasterize reference dataset raster ar18x18
ar9_raster <- rasterize(ar9, DM_maxval, field="DMVT_ID")
writeRaster(ar9_raster, filename = paste0(out_folder, "AR9_ref.tif"), format="GTiff", overwrite=TRUE)
####
####
# AR VEG maps ####
## crop and mask raster ar VEG Maps
ar_veg_dissolved <- readOGR(dsn = paste0(fast_processing, "Veg_maps"), layer = "Vegetasjonskart1997-2015_dissolved")
DM_maxval_mask_veg <- mask(DM_maxval, ar_veg_dissolved)
writeRaster(DM_maxval_mask_veg, filename = paste0(out_folder_maxval, "VEG_DM_maxval.tif"), format="GTiff", overwrite=TRUE)
DM_preval_mask_veg <- mask(DM_preval, ar_veg_dissolved)
writeRaster(DM_preval_mask_veg, filename = paste0(out_folder_preval, "VEG_DM_preval.tif"), format="GTiff", overwrite=TRUE)
DM_AUC_mask_veg <- mask(DM_AUC, ar_veg_dissolved)
writeRaster(DM_AUC_mask_veg, filename = paste0(out_folder_AUC, "VEG_DM_AUC.tif"), format="GTiff", overwrite=TRUE)
# rasterize VEG Maps
ar_veg <- readOGR(dsn = paste0(in_folder, "Veg_maps"), layer = "Vegetasjonskart1997-2015_WGS")
transl <- read.csv(paste0(git_folder, "transl_VT_DMvt_vegmaps.csv"), sep = ";", dec = ".")
transl <- rename(transl, Veg1 = VT_code)
ar_veg@data <- left_join(ar_veg@data, transl, by="Veg1")
head(ar_veg@data)
#writeOGR(obj=ar_veg, dsn = paste0(in_folder, "Veg_maps"), layer = "Veg_map_DMVT_ID", driver="ESRI Shapefile" )
ar_veg_raster <- rasterize(ar_veg, DM_maxval, field="DMVT_ID")
writeRaster(ar_veg_raster, filename = paste0(out_folder, "VEG_ref.tif"), format="GTiff", overwrite=TRUE)
# ### Merge AR9 and VEG_maps ####
#
#
# # for simplification of results and procedures, merging ar9 & veg maps for
# #1. DM_maxval, 2. DM_preval, 3. DM_auc, 4. reference of AR9&VEG
# AR
# x <- list(r1, r2)
# names(x) <- c("x", "y")
# x$filename <- 'test.tif'
# x$overwrite <- TRUE
# m <- do.call(merge, x)
###
#### 1 Cohens kappa ####
library(diffeR)
library(spatialEco)
library(rfUtilities)
#final kappa for the whole matrix
kappa.own <- function(m) {
N <- sum(m)
No <- sum(diag(m))
Ne <- 1 / N * sum(colSums(m) * rowSums(m))
return( (No - Ne) / (N - Ne) )
}
#kappa.own(conf.matrix)
# output all the pairs of rasters
# REF vs DM AUC, DM preval, DM maxval
# create table to output EVALUATION data into
nrows <- length(vt_list)
df_kappa <- data.frame(tot.obs=numeric(nrows),
tot.cor=numeric(nrows),
tot.cor.perc=numeric(nrows),
kappa=numeric(nrows)
)
# VT RASTERS ####
#read in raster, then reference raster, then do crosstabMATRIX and finally create kappa
#across all the extents
# AR18
ras18 <- list(raster(paste0(out_folder, "AR18_ref.tif")),
raster(paste0(out_folder_maxval, "AR18_DM_maxval.tif")),
raster(paste0(out_folder_AUC , "AR18_DM_AUC.tif")),
raster(paste0(out_folder_preval, "AR18_DM_preval.tif"))
)
ras9 <- list(raster(paste0(out_folder, "AR9_ref.tif")),
raster(paste0(out_folder_maxval, "AR9_DM_maxval.tif")),
raster(paste0(out_folder_AUC , "AR9_DM_AUC.tif")),
raster(paste0(out_folder_preval, "AR9_DM_preval.tif"))
)
rasVEG <- list(raster(paste0(out_folder, "VEG_ref.tif")),
raster(paste0(out_folder_maxval, "VEG_DM_maxval.tif")),
raster(paste0(out_folder_AUC , "VEG_DM_AUC.tif")),
raster(paste0(out_folder_preval, "VEG_DM_preval.tif"))
)
ras<-list(ras18,ras9,rasVEG)
ras
mod=1
i=1
# for each area that is evaluated
for(mod in 1:3){
print(mod)
# so far testing without the upper loop
# for each model pair that is evaluated at each area
for(i in 1:3){
print(i)
s <- stack(ras[[mod]][[1]],ras[[mod]][[i+1]])
# create confusion matrices
c.mat<-crosstabm(s[[1]],s[[2]])
write.csv(c.mat)
# user's/ modelled accuracy
UA <- diag(c.mat)/colSums(c.mat)*100
# producer's/ reference accuracy
PA <- diag(c.mat)/rowSums(c.mat)*100
# add data to the table matrix
df_kappa$tot.obs[i] <- sum(c.mat)# total observations
df_kappa$tot.cor[i] <- sum(diag(c.mat)) # total correct observations
df_kappa$tot.cor.perc[i] <- sum(diag(c.mat))/sum(c.mat)*100 #Total correct observations in %
df_kappa$kappa[i] <- kappa.own(c.mat) # kappa
}
row=nrow(df_kappa)+1
}
names(df_kappa) <- c("DM_maxval", "DM_AUC", "DM_preval")
rownames(df_kappa) <- c("kappa_AR18", "kappa_AR9", "kappa_VEG")
#adjusting kappa function
#https://blogs.fu-berlin.de/reseda/accuracy-statistics-in-r/
# #individual kappa for each class called Fleiss' kappa
# library(irr)
# m<-c.mat_AR18_maxval
# reduce.matrix <- function(m){
# for i in m
# rowSums(m)[i]-i
#
#
# }
# (m[1,1]-(rowSums(m)[1]*colSums(m)[1]))/(rowSums(m)[1]+colSums(m)[1])/(2-(rowSums(m)[1]*colSums(m)[1]))
# (m[2,2]-(rowSums(m)[2]*colSums(m)[2]))/(rowSums(m)[2]+colSums(m)[2])/(2-(rowSums(m)[2]*colSums(m)[2]))
#
#
# kappam.fleiss(c.mat_AR18_maxval, detail = TRUE)
#
# kappa.ind <- function(n){
# #using confusion matrix here
# #individual entry for each row-total for each row * total for each column / ((total for each row+total for column)/2-total for each row * total for each column )
# ki=(n[,i])
# }
# (1619-5027*4867)/((5027+4867)/2-(5027*4867))
# summary.accuracy(c.mat_AR18_preval)
# doesn't work yet
#do manually instead
# VT - total kappa ####
AR18_ref <- raster(paste0(out_folder, "AR18_ref.tif"))
AR18_DM_maxval <- raster(paste0(out_folder_maxval, "AR18_DM_maxval.tif"))
AR18_DM_AUC <- raster(paste0(out_folder_AUC , "AR18_DM_AUC.tif"))
AR18_DM_preval <- raster(paste0(out_folder_preval, "AR18_DM_preval.tif"))
# create confusion matrices
VT_c.mat_AR18_maxval <- crosstabm(AR18_ref,AR18_DM_maxval)
VT_c.mat_AR18_AUC <- crosstabm(AR18_ref,AR18_DM_AUC)
VT_c.mat_AR18_preval <- crosstabm(AR18_ref,AR18_DM_preval)
# calculate kappa
VT_kappa <- data.frame()
VT_kappa[1,1] <- kappa.own(VT_c.mat_AR18_maxval)
VT_kappa[2,1] <- sum(VT_c.mat_AR18_maxval) # total observations
VT_kappa[3,1] <- sum(diag(VT_c.mat_AR18_maxval)) # total correct observations
VT_kappa[4,1] <- sum(diag(VT_c.mat_AR18_maxval))/sum(VT_c.mat_AR18_maxval)*100 #Total correct observations in %
VT_kappa[1,2] <- kappa.own(VT_c.mat_AR18_AUC)
VT_kappa[2,2] <- sum(VT_c.mat_AR18_AUC) # total observations
VT_kappa[3,2] <- sum(diag(VT_c.mat_AR18_AUC)) # total correct observations
VT_kappa[4,2] <- sum(diag(VT_c.mat_AR18_AUC))/sum(VT_c.mat_AR18_AUC)*100 #Total correct observations in %
VT_kappa[1,3] <- kappa.own(VT_c.mat_AR18_preval)
VT_kappa[2,3] <- sum(VT_c.mat_AR18_preval) # total observations
VT_kappa[3,3] <- sum(diag(VT_c.mat_AR18_preval)) # total correct observations
VT_kappa[4,3] <- sum(diag(VT_c.mat_AR18_preval))/sum(VT_c.mat_AR18_preval)*100 #Total correct observations in %
# AR9
AR9_ref <- raster(paste0(out_folder, "AR9_ref.tif"))
AR9_DM_maxval <- raster(paste0(out_folder_maxval, "AR9_DM_maxval.tif"))
AR9_DM_AUC <- raster(paste0(out_folder_AUC , "AR9_DM_AUC.tif"))
AR9_DM_preval <- raster(paste0(out_folder_preval, "AR9_DM_preval.tif"))
# create confusion matrices
VT_c.mat_AR9_maxval <- crosstabm(AR9_ref,AR9_DM_maxval)
VT_c.mat_AR9_preval <- crosstabm(AR9_ref,AR9_DM_preval)
VT_c.mat_AR9_AUC <- crosstabm(AR9_ref,AR9_DM_AUC)
# calculate kappa
VT_kappa[5,1] <- kappa.own(VT_c.mat_AR9_maxval)
VT_kappa[6,1] <- sum(VT_c.mat_AR9_maxval) # total observations
VT_kappa[7,1] <- sum(diag(VT_c.mat_AR9_maxval)) # total correct observations
VT_kappa[8,1] <- sum(diag(VT_c.mat_AR9_maxval))/sum(VT_c.mat_AR9_maxval)*100 #Total correct observations in %
VT_kappa[5,2] <- kappa.own(VT_c.mat_AR9_AUC)
VT_kappa[6,2] <- sum(VT_c.mat_AR9_AUC) # total observations
VT_kappa[7,2] <- sum(diag(VT_c.mat_AR9_AUC)) # total correct observations
VT_kappa[8,2] <- sum(diag(VT_c.mat_AR9_AUC))/sum(VT_c.mat_AR9_AUC)*100 #Total correct observations in %
VT_kappa[5,3] <- kappa.own(VT_c.mat_AR9_preval)
VT_kappa[6,3] <- sum(VT_c.mat_AR9_preval) # total observations
VT_kappa[7,3] <- sum(diag(VT_c.mat_AR9_preval)) # total correct observations
VT_kappa[8,3] <- sum(diag(VT_c.mat_AR9_preval))/sum(VT_c.mat_AR9_preval)*100 #Total correct observations in %
# VEG
VEG_ref <- raster(paste0(out_folder, "VEG_ref.tif"))
VEG_DM_maxval <- raster(paste0(out_folder_maxval, "VEG_DM_maxval.tif"))
VEG_DM_AUC <- raster(paste0(out_folder_AUC , "VEG_DM_AUC.tif"))
VEG_DM_preval <- raster(paste0(out_folder_preval, "VEG_DM_preval.tif"))
# create confusion matrices
VT_c.mat_VEG_maxval <- crosstabm(VEG_ref,VEG_DM_maxval)
VT_c.mat_VEG_AUC <- crosstabm(VEG_ref,VEG_DM_AUC)
VT_c.mat_VEG_preval <- crosstabm(VEG_ref,VEG_DM_preval)
# calculate kappa
VT_kappa[9,1] <- kappa.own(VT_c.mat_VEG_maxval)
VT_kappa[10,1] <- sum(VT_c.mat_VEG_maxval) # total observations
VT_kappa[11,1] <- sum(diag(VT_c.mat_VEG_maxval)) # total correct observations
VT_kappa[12,1] <- sum(diag(VT_c.mat_VEG_maxval))/sum(VT_c.mat_VEG_maxval)*100 #Total correct observations in %
VT_kappa[9,2] <- kappa.own(VT_c.mat_VEG_AUC)
VT_kappa[10,2] <- sum(VT_c.mat_VEG_AUC) # total observations
VT_kappa[11,2] <- sum(diag(VT_c.mat_VEG_AUC)) # total correct observations
VT_kappa[12,2] <- sum(diag(VT_c.mat_VEG_AUC))/sum(VT_c.mat_VEG_AUC)*100 #Total correct observations in %
VT_kappa[9,3] <- kappa.own(VT_c.mat_VEG_preval)
VT_kappa[10,3] <- sum(VT_c.mat_VEG_preval) # total observations
VT_kappa[11,3] <- sum(diag(VT_c.mat_VEG_preval)) # total correct observations
VT_kappa[12,3] <- sum(diag(VT_c.mat_VEG_preval))/sum(VT_c.mat_VEG_preval)*100 #Total correct observations in %
names(VT_kappa) <- c("DM_maxval", "DM_AUC", "DM_preval")
rownames(VT_kappa) <- c("kappa_AR18",
"tot.obs_AR18",
"tot.obs.corr_AR18",
"perc.obs.corr_AR18",
"kappa_AR9",
"tot.obs_AR9",
"tot.obs.corr_AR9",
"perc.obs.corr_AR9",
"kappa_VEG",
"tot.obs_VEG",
"tot.obs.corr_VEG",
"perc.obs.corr_VEG"
)
round(VT_kappa, digits = 2)
VT_kappa_ord <- VT_kappa[order(VT_kappa$DM_maxval),]
write.csv(VT_kappa, file= paste0(out_folder,"VT_kappa.csv"))
write.csv(VT_kappa_ord, file= paste0(out_folder,"VT_kappa_ord.csv"))
#AR18
write.csv(VT_c.mat_AR18_maxval, file= paste0(out_folder,"VT_c.mat_AR18_maxval.csv"))
write.csv(VT_c.mat_AR18_AUC, file= paste0(out_folder,"VT_c.mat_AR18_AUC.csv"))
write.csv(VT_c.mat_AR18_preval, file= paste0(out_folder,"VT_c.mat_AR18_preval.csv"))
#AR9
write.csv(VT_c.mat_AR9_maxval, file= paste0(out_folder,"VT_c.mat_AR9_maxval.csv"))
write.csv(VT_c.mat_AR9_AUC, file= paste0(out_folder,"VT_c.mat_AR9_AUC.csv"))
write.csv(VT_c.mat_AR9_preval, file= paste0(out_folder,"VT_c.mat_AR9_preval.csv"))
#VEG
write.csv(VT_c.mat_VEG_maxval, file= paste0(out_folder,"VT_c.mat_VEG_maxval.csv"))
write.csv(VT_c.mat_VEG_AUC, file= paste0(out_folder,"VT_c.mat_VEG_AUC.csv"))
write.csv(VT_c.mat_VEG_preval, file= paste0(out_folder,"VT_c.mat_VEG_preval.csv"))
#calculate kappa and individual kappa using the Rossiter functions ####
# VT - individual kappa####
# *AR18 ####
c.mat.AR18.vt <- list(VT_c.mat_AR18_maxval, VT_c.mat_AR18_AUC, VT_c.mat_AR18_preval)
c.mat.AR18.vt.kappa <- lapply(c.mat.AR18.vt, kappa)
#subset 1:5
AR18.vt.tocsv.tot <- sapply(c.mat.AR18.vt.kappa,"[",c(1:5))
colnames(AR18.vt.tocsv.tot) <- c("AR18_DM_maxval", "AR18_DM_AUC", "AR18_DM_preval")
#write.csv(AR18.vt.tocsv.tot, file= paste0(out_folder,"VT_kappa_total_AR18.csv"))
#subset 6:11
AR18.vt.tocsv <- t(as.data.frame(lapply(c.mat.AR18.vt.kappa, function(x) {x[c(6:8,9)]})))
row.names(AR18.vt.tocsv) <- paste0(row.names(AR18.vt.tocsv), c(rep(c("_AR18_DM_maxval", "_AR18_DM_AUC", "_AR18_DM_preval"), each=4)))
rownames(AR18.vt.tocsv)
#output to csv
write.csv(AR18.vt.tocsv, file= paste0(out_folder,"VT_kappa_individ_AR18.csv"))
# *AR9 ####
c.mat.AR9.vt <- list(VT_c.mat_AR9_maxval, VT_c.mat_AR9_AUC[1:29, 1:29], VT_c.mat_AR9_preval)
c.mat.AR9.vt.kappa <- lapply(c.mat.AR9.vt, kappa)
#subset 1:5
AR9.vt.tocsv.tot <- sapply(c.mat.AR9.vt.kappa,"[",c(1:5))
colnames(AR9.vt.tocsv.tot) <- c("AR9_DM_maxval", "AR9_DM_AUC", "AR9_DM_preval")
#write.csv(AR9.vt.tocsv.tot, file= paste0(out_folder,"VT_kappa_total_AR9.csv"))
#subset 6:11
#sapply(c.mat.AR9.vt.kappa,"[",c(6:11))
AR9.vt.tocsv <- t(as.data.frame(lapply(c.mat.AR9.vt.kappa, function(x) {x[c(6:8,9)]})))
row.names(AR9.vt.tocsv) <- paste0(row.names(AR9.vt.tocsv), c(rep(c("_AR9_DM_maxval", "_AR9_DM_AUC", "_AR9_DM_preval"), each=4)))
rownames(AR9.vt.tocsv)
#output to csv
write.csv(AR9.vt.tocsv, file= paste0(out_folder,"VT_kappa_individ_AR9.csv"))
# *VEG ####
c.mat.VEG.vt <- list(VT_c.mat_VEG_maxval, VT_c.mat_VEG_AUC, VT_c.mat_VEG_preval)
c.mat.VEG.vt.kappa <- lapply(c.mat.VEG.vt, kappa)
#subset 1:5
VEG.vt.tocsv.tot <- sapply(c.mat.VEG.vt.kappa,"[",c(1:5))
colnames(VEG.vt.tocsv.tot) <- c("VEG_DM_maxval", "VEG_DM_AUC", "VEG_DM_preval")
#write.csv(VEG.vt.tocsv.tot, file= paste0(out_folder,"VT_kappa_total_VEG.csv"))
#subset 6:11
sapply(c.mat.VEG.vt.kappa,"[",c(6:11))
VEG.vt.tocsv <- t(as.data.frame(lapply(c.mat.VEG.vt.kappa, function(x) {x[c(6:8,9)]})))
row.names(VEG.vt.tocsv) <- paste0(row.names(VEG.vt.tocsv), c(rep(c("_VEG_DM_maxval", "_VEG_DM_AUC", "_VEG_DM_preval"), each=4)))
rownames(VEG.vt.tocsv)
#output to csv
write.csv(VEG.vt.tocsv, file= paste0(out_folder,"VT_kappa_individ_VEG.csv"))
# COMBINE VTs into one dataframe
VT_kappa_all <- cbind(AR18.vt.tocsv.tot, AR9.vt.tocsv.tot, VEG.vt.tocsv.tot)
VT_kappa_all[c(seq(5,45, by=5))] <- as.numeric(VT_kappa_all[c(seq(5,45, by=5))]) # correcting the extra information in the list
write.csv(VT_kappa_all, file= paste0(out_folder,"VT_kappa_all.csv"))
#
#
# create.conf.mat <- function(x){
# conf.matrix<-res_VEG_maxvalXref
# # user's/ modelled accuracy
# UA <- diag(conf.matrix)/colSums(conf.matrix)*100
# # producer's/ reference accuracy
# PA <- diag(conf.matrix)/rowSums(conf.matrix)*100
# # overall accuracy
# OA <- sum(diag(conf.matrix))/sum(conf.matrix)
# # add data to the conf matrix
# conf.matrix.ext <- addmargins(conf.matrix)
# conf.matrix.ext <- rbind(conf.matrix.ext, "Users" = c(PA, NA))
# conf.matrix.ext <- cbind(conf.matrix.ext, "Producers" = c(UA, NA, OA))
# colnames(conf.matrix.ext) <- c(1:31, "Sum", "PA")
# rownames(conf.matrix.ext) <- c(1:31, "Sum", "UA") #levels(as.factor(shp.train$classes))
# dimnames(conf.matrix.ext) <- list("Prediction" = colnames(conf.matrix.ext),
# "Reference" = rownames(conf.matrix.ext))
# #class(conf.matrix.ext) <- "table"
# conf.matrix.ext <- round(conf.matrix.ext, digits = 1)
# #conf.matrix.ext[1:31,1:31] <- round(conf.matrix.ext[1:31,1:31], digits = 0)
# return(conf.matrix.ext)
# # SAVE TO CSV file
# # write.csv(tab_kappa, file= paste0(out_folder,names(pred.obs[[1]]),"_x_",names(pred.obs[[2]]),"conf.matrix.csv"))
# }
#
# c.mat.ext_VEG_maxval <- create.conf.mat(res_VEG_maxvalXref)
#
#
#
#
# sign <- binom.test(x = sum(diag(conf.matrix)),
# n = sum(conf.matrix),
# alternative = c("two.sided"),
# conf.level = 0.95)
# sign$conf.int[1:2]
#
#
# conf.matrix <- rbind(res_VEG_maxvalXref,colSums(res_VEG_maxvalXref))
# conf.matrix <- cbind(conf.matrix,rowSums(res_VEG_maxvalXref))
#
#
#
#
# raster_VEG_maxvalXref <- raster.change(VEG_ref,VEG_DM_maxval, d = c(3, 3))
#
#
#
# MADscatterplot(DM_AUC_test,DM_Maxval_test)
# categoryComponentsPlot(DM_AUC_test,DM_Maxval_test)
# categorySourcesPlot(DM_AUC_test,DM_Maxval_test)
# library(SDMTools)
# class(res_VEG_maxvalXref)
# Kappa(res_VEG_maxvalXref)
#
#
# ### 2 running window kappa ####
# ### jeffrey Evans on github
# #https://gis.stackexchange.com/questions/103616/map-accuracy-assessment-by-moving-window-in-r
# require(raster)
# require(asbio)
#
# setwd("D:/TEST")
#
# ws <- 3 # window size
# p=0.95 # probability threshold
#
# # Create example data
# pred <- raster(ncol=100, nrow=100)
# pred[pred] <- runif(length(pred[pred]),0,1)
# obs <- pred
# obs[obs] <- runif(length(pred[pred]),0,1)
# pred2 <- AR18_DM_maxval
# obs2 <- AR18_ref
# pred.obs <- stack(pred2, obs2)
# #pred.obs <- stack(obs,pred)
# names(pred.obs) <- c("pred", "obs")
#
#
#
# # Loop to read raster in blocks using getValuesFocal
# genrate.comparison <- function(pred.obs){
# ws <- 3 # window size
# p=0.95 # probability threshold
# # Create new on-disk raster
# s <- writeStart(pred.obs[[1]], "Kappa.img", overwrite=TRUE)
# tr <- blockSize(pred.obs)
# options(warn=-1)
#
# for (i in 1:tr$n) {
# # Get focal values as list matrix object
# v <- getValuesFocal(pred.obs, row=tr$row[i], nrows=tr$nrows[i],
# ngb=ws, array=FALSE)
# # reclassify data to [0,1] using lapply
# v <- lapply(v, FUN=function(x) {
# if( length(x[is.na(x)]) == length(x) ) {
# return( NA )
# } else {
# #print(x)
# return( ifelse(x >= p, 1, 0) )
# }
# }
# )
# # Loop to calculate Kappa and assign to new raster using writeValues
# r <- vector()
# for( j in 1:dim(v[[1]])[1]) {
# Obs <- v[[1]][j,]
# Obs <- Obs[!is.na(Obs)]
# Pred <- v[[2]][j,]
# Pred <- Pred[!is.na(Pred)]
# #print(paste('Pred=', Pred))
# #print(paste('OBS=', Obs))
# if( length(Obs) >= 2 && length(Obs) == length(Pred) ) {
# r <- append(r, Kappa(Pred, Obs)$khat)
# } else {
# r <- append(r, NA)
# }
# }
# writeValues(s, r, tr$row[i])
# }
# s <- writeStop(s)
# k <- raster("Kappa.img")
# plot(k)
# return(k)
# }
# #test1 <- pred.obs
# plot(pred.obs)
# # create table to store kappa values
# tab_kappa <- data.frame(nrow(31),ncol(2))
# # define path to save output
# subfolder<-"AR18_kappa/"
# ### loop through VTs ####
#
# for(i in 1:31){
# print(i)
# #i=11
# # Split the values of the currently running VT into binary 0 and 1 and NA
# pred.obs.veg = stackApply(pred.obs,indices = c(1,2), na.rm = FALSE, fun= function(x,...) {
# if (is.na(x)){
# return(NA)
# }
#
# if (x==i){
# return(1)
# }
# else{
# return(0)
# }
# })
# writeRaster(pred.obs.veg, filename = paste0(out_folder,subfolder,names(pred.obs[[1]]),"_x_",names(pred.obs[[2]]),"_VT_",i ,"_binary.tiff"),
# format="GTiff", overwrite=TRUE)
# print(paste("stackApply finished for", i))
# # run the Kappa function on the current VT
# pred.obs.veg.out <- genrate.comparison(pred.obs.veg)
# # save output
# writeRaster(pred.obs.veg.out, filename = paste0(out_folder,subfolder,names(pred.obs[[1]]),"_x_",names(pred.obs[[2]]),"_VT_",i ,"_kappa.tiff"),
# format="GTiff", overwrite=TRUE)
# print(paste("generate.comparison finished for", i))
# # fill table with the kappa data
# tab_kappa[i,1] <- i
# veg.kappa.mean <- mean(values(pred.obs.veg.out),na.rm=TRUE)
# tab_kappa[i,2] <- veg.kappa.mean
# if (is.na(veg.kappa.mean))
# next()
#
# # thumbnail image (redundant in full extent)
# tiff(filename = paste0(out_folder,subfolder,names(pred.obs[[1]]),"_x_",names(pred.obs[[2]]),"_VT_",i ,".tiff"),
# height = 20, width = 40, units = 'cm', res=100)
# par(mfrow=c(3,1))
# plot(pred.obs.veg[[1]], main="Prediction")
# plot(pred.obs.veg[[2]], main="Observation")
# plot(pred.obs.veg.out, main="kappa")
# text(pred.obs.veg.out)
# dev.off()
# }
#
# # save table tab_kappa
# write.csv(tab_kappa, file= paste0(out_folder,subfolder,names(pred.obs[[1]]),"_x_",names(pred.obs[[2]]),"tab_kappa.csv"))
#
#
# par(mfrow=c(3,1))
# plot(test1[[1]])
# plot(test1[[2]])
# plot(test1.out)
# text(test1.out)
#
# #check values
# table(values(ar_sub_r))
# table(values(DM_AUC_509))
# # eval polygons
#
#
# http://greenbrown.r-forge.r-project.org/man/CompareClassification.html
# https://www.rdocumentation.org/packages/spatialEco/versions/1.2-0/topics/class.comparison
### Eval 3 -Ecosytem groups ####
# test kappa not on VT but on groups of VT (according to BRYN 2018 - there are 12 groups)
#### reclassify
#load in translation rules
# "GR" also stands for GROUP or ECOSYSTEM GROUP
transl_vt_groups <- read.csv(paste0(git_folder, "transl_VT_VEGgroup.csv"), sep = ";", dec = ".")
transl_GR <- matrix(nrow= 31, ncol = 2)
# make into reclassify matrix # can also to like this cbind(transl_rule_DM$VT_ID, transl_rule_DM$PFT_ID)
for(i in 1:NROW(transl_vt_groups)){
transl_GR[i,] <- c(transl_vt_groups$VT_ID[i], transl_vt_groups$GRP[i])
# print(transl)
}
# these are the masked datasets *ar18, ar9 and VEG
#transl <- cbind(transl_rule_RS$RS_code, transl_rule_RS$PFT_ID)
# lapply(ras18, function(x) names(x))
AR18_GR <- lapply(ras18, function(x) {reclassify(x, transl_GR, include.lowest=TRUE, overwrite=TRUE,
filename = paste0(out_folder,"EG/","EG_", names(x),".tif"))})
AR9_GR <- lapply(ras9, function(x) {reclassify(x, transl_GR, include.lowest=TRUE, overwrite=TRUE,
filename = paste0(out_folder,"EG/","EG_",names(x),".tif"))})
ARVEG_GR <- lapply(rasVEG, function(x) {reclassify(x, transl_GR, include.lowest=TRUE, overwrite=TRUE,
filename = paste0(out_folder,"EG/","EG_",names(x),".tif"))})
# for later excercise, reclassify also full rasters, not just masked
# reclassify also the full datasets.
DM_vt_fullextent <- list(DM_maxval, DM_AUC, DM_preval)
DM_eg_fullextent <- lapply(DM_vt_fullextent, function(x) {reclassify(x, transl_GR, include.lowest=TRUE, overwrite=TRUE,
filename = paste0(out_folder,"EG/","EG_full",names(x),".tif"))})
# Load in specific rasters
library(diffeR)
AR18_ref <- AR18_GR[[1]]
AR18_DM_maxval <- AR18_GR[[2]]
AR18_DM_AUC <- AR18_GR[[3]]
AR18_DM_preval <- AR18_GR[[4]]
# create confusion matrices
EG_c.mat_AR18_maxval <- crosstabm(AR18_ref,AR18_DM_maxval)
EG_c.mat_AR18_AUC <- crosstabm(AR18_ref,AR18_DM_AUC)
EG_c.mat_AR18_preval <- crosstabm(AR18_ref,AR18_DM_preval)
# calculate kappa
EG_kappa <- data.frame()
EG_kappa[1,1] <- kappa.own(EG_c.mat_AR18_maxval)
EG_kappa[2,1] <- sum(EG_c.mat_AR18_maxval) # total observations
EG_kappa[3,1] <- sum(diag(EG_c.mat_AR18_maxval)) # total correct observations
EG_kappa[4,1] <- sum(diag(EG_c.mat_AR18_maxval))/sum(EG_c.mat_AR18_maxval)*100 #Total correct observations in %
EG_kappa[1,2] <- kappa.own(EG_c.mat_AR18_AUC)
EG_kappa[2,2] <- sum(EG_c.mat_AR18_AUC) # total observations
EG_kappa[3,2] <- sum(diag(EG_c.mat_AR18_AUC)) # total correct observations
EG_kappa[4,2] <- sum(diag(EG_c.mat_AR18_AUC))/sum(EG_c.mat_AR18_AUC)*100 #Total correct observations in %
EG_kappa[1,3] <- kappa.own(EG_c.mat_AR18_preval)
EG_kappa[2,3] <- sum(EG_c.mat_AR18_preval) # total observations
EG_kappa[3,3] <- sum(diag(EG_c.mat_AR18_preval)) # total correct observations
EG_kappa[4,3] <- sum(diag(EG_c.mat_AR18_preval))/sum(EG_c.mat_AR18_preval)*100 #Total correct observations in %
# AR9
AR9_ref <- AR9_GR[[1]]
AR9_DM_maxval <- AR9_GR[[2]]
AR9_DM_AUC <- AR9_GR[[3]]
AR9_DM_preval <- AR9_GR[[4]]
# create confusion matrices
EG_c.mat_AR9_maxval <- crosstabm(AR9_ref,AR9_DM_maxval)
EG_c.mat_AR9_AUC <- crosstabm(AR9_ref,AR9_DM_AUC)
EG_c.mat_AR9_preval <- crosstabm(AR9_ref,AR9_DM_preval)
# calculate kappa
EG_kappa[5,1] <- kappa.own(EG_c.mat_AR9_maxval)
EG_kappa[6,1] <- sum(EG_c.mat_AR9_maxval) # total observations
EG_kappa[7,1] <- sum(diag(EG_c.mat_AR9_maxval)) # total correct observations
EG_kappa[8,1] <- sum(diag(EG_c.mat_AR9_maxval))/sum(EG_c.mat_AR9_maxval)*100 #Total correct observations in %
EG_kappa[5,2] <- kappa.own(EG_c.mat_AR9_AUC)
EG_kappa[6,2] <- sum(EG_c.mat_AR9_AUC) # total observations
EG_kappa[7,2] <- sum(diag(EG_c.mat_AR9_AUC)) # total correct observations
EG_kappa[8,2] <- sum(diag(EG_c.mat_AR9_AUC))/sum(EG_c.mat_AR9_AUC)*100 #Total correct observations in %
EG_kappa[5,3] <- kappa.own(EG_c.mat_AR9_preval)
EG_kappa[6,3] <- sum(EG_c.mat_AR9_preval) # total observations
EG_kappa[7,3] <- sum(diag(EG_c.mat_AR9_preval)) # total correct observations
EG_kappa[8,3] <- sum(diag(EG_c.mat_AR9_preval))/sum(EG_c.mat_AR9_preval)*100 #Total correct observations in %
# VEG
VEG_ref <- ARVEG_GR[[1]]
VEG_DM_maxval <- ARVEG_GR[[2]]
VEG_DM_AUC <- ARVEG_GR[[3]]
VEG_DM_preval <- ARVEG_GR[[4]]
# create confusion matrices
EG_c.mat_VEG_maxval <- crosstabm(VEG_ref,VEG_DM_maxval)
EG_c.mat_VEG_AUC <- crosstabm(VEG_ref,VEG_DM_AUC)
EG_c.mat_VEG_preval <- crosstabm(VEG_ref,VEG_DM_preval)
# calculate kappa
EG_kappa[9, 1] <- kappa.own(EG_c.mat_VEG_maxval)
EG_kappa[10,1] <- sum(EG_c.mat_VEG_maxval) # total observations
EG_kappa[11,1] <- sum(diag(EG_c.mat_VEG_maxval)) # total correct observations
EG_kappa[12,1] <- sum(diag(EG_c.mat_VEG_maxval))/sum(EG_c.mat_VEG_maxval)*100 #Total correct observations in %
EG_kappa[9, 2] <- kappa.own(EG_c.mat_VEG_AUC)
EG_kappa[10,2] <- sum(EG_c.mat_VEG_AUC) # total observations
EG_kappa[11,2] <- sum(diag(EG_c.mat_VEG_AUC)) # total correct observations
EG_kappa[12,2] <- sum(diag(EG_c.mat_VEG_AUC))/sum(EG_c.mat_VEG_AUC)*100 #Total correct observations in %
EG_kappa[9, 3] <- kappa.own(EG_c.mat_VEG_preval)
EG_kappa[10,3] <- sum(EG_c.mat_VEG_preval) # total observations
EG_kappa[11,3] <- sum(diag(EG_c.mat_VEG_preval)) # total correct observations
EG_kappa[12,3] <- sum(diag(EG_c.mat_VEG_preval))/sum(EG_c.mat_VEG_preval)*100 #Total correct observations in %
names(EG_kappa) <- c("DM_maxval", "DM_AUC", "DM_preval")
rownames(EG_kappa) <- c("kappa_AR18",
"tot.obs_AR18",
"tot.obs.corr_AR18",
"perc.obs.corr_AR18",
"kappa_AR9",
"tot.obs_AR9",
"tot.obs.corr_AR9",
"perc.obs.corr_AR9",
"kappa_VEG",
"tot.obs_VEG",
"tot.obs.corr_VEG",
"perc.obs.corr_VEG"
)
round(EG_kappa, digits = 2)
EG_kappa_ord <- EG_kappa[order(EG_kappa$DM_maxval),]
write.csv(EG_kappa, file= paste0(out_folder,"EG_kappa.csv"))
write.csv(EG_kappa_ord, file= paste0(out_folder,"EG_kappa_ord.csv"))
#AR18
write.csv(EG_c.mat_AR18_maxval, file= paste0(out_folder,"EG_c.mat_AR18_maxval.csv"))
write.csv(EG_c.mat_AR18_AUC, file= paste0(out_folder,"EG_c.mat_AR18_AUC.csv"))
write.csv(EG_c.mat_AR18_preval, file= paste0(out_folder,"EG_c.mat_AR18_preval.csv"))
#AR9
write.csv(EG_c.mat_AR9_maxval, file= paste0(out_folder,"EG_c.mat_AR9_maxval.csv"))
write.csv(EG_c.mat_AR9_AUC, file= paste0(out_folder,"EG_c.mat_AR9_AUC.csv"))
write.csv(EG_c.mat_AR9_preval, file= paste0(out_folder,"EG_c.mat_AR9_preval.csv"))
#VEG
write.csv(EG_c.mat_VEG_maxval, file= paste0(out_folder,"EG_c.mat_VEG_maxval.csv"))
write.csv(EG_c.mat_VEG_AUC, file= paste0(out_folder,"EG_c.mat_VEG_AUC.csv"))
write.csv(EG_c.mat_VEG_preval, file= paste0(out_folder,"EG_c.mat_VEG_preval.csv"))
#calculate kappa and individual kappa using the Rossiter functions ####
# EG - individual kappa ####
# *AR18 ####
c.mat.AR18.eg <- list(EG_c.mat_AR18_maxval, EG_c.mat_AR18_AUC, EG_c.mat_AR18_preval)
c.mat.AR18.eg.kappa <- lapply(c.mat.AR18.eg, kappa)
#subset 1:5
AR18.eg.tocsv.tot <- sapply(c.mat.AR18.eg.kappa,"[",c(1:5))
colnames(AR18.eg.tocsv.tot) <- c("AR18_DM_maxval", "AR18_DM_AUC", "AR18_DM_preval")
#write.csv(AR18.eg.tocsv.tot, file= paste0(out_folder,"EG_kappa_total_AR18.csv"))
#subset 6:11
AR18.eg.tocsv <- t(as.data.frame(lapply(c.mat.AR18.eg.kappa, function(x) {x[c(6:8,9)]})))
row.names(AR18.eg.tocsv) <- paste0(row.names(AR18.eg.tocsv), c(rep(c("_AR18_DM_maxval", "_AR18_DM_AUC", "_AR18_DM_preval"), each=4)))
rownames(AR18.eg.tocsv)
#output to csv
write.csv(AR18.eg.tocsv, file= paste0(out_folder,"EG_kappa_individ_AR18.csv"))
# *AR9 ####
c.mat.AR9.eg <- list(EG_c.mat_AR9_maxval, EG_c.mat_AR9_AUC, EG_c.mat_AR9_preval)
c.mat.AR9.eg.kappa <- lapply(c.mat.AR9.eg, kappa)
#subset 1:5
AR9.eg.tocsv.tot <- sapply(c.mat.AR9.eg.kappa,"[",c(1:5))
colnames(AR9.eg.tocsv.tot) <- c("AR9_DM_maxval", "AR9_DM_AUC", "AR9_DM_preval")
#write.csv(AR9.eg.tocsv.tot, file= paste0(out_folder,"EG_kappa_total_AR9.csv"))
#subset 6:11
#sapply(c.mat.AR9.eg.kappa,"[",c(6:11))
AR9.eg.tocsv <- t(as.data.frame(lapply(c.mat.AR9.eg.kappa, function(x) {x[c(6:8,9)]})))
row.names(AR9.eg.tocsv) <- paste0(row.names(AR9.eg.tocsv), c(rep(c("_AR9_DM_maxval", "_AR9_DM_AUC", "_AR9_DM_preval"), each=4)))
rownames(AR9.eg.tocsv)
#output to csv
write.csv(AR9.eg.tocsv, file= paste0(out_folder,"EG_kappa_individ_AR9.csv"))
# *VEG ####
c.mat.VEG.eg <- list(EG_c.mat_VEG_maxval, EG_c.mat_VEG_AUC, EG_c.mat_VEG_preval)
c.mat.VEG.eg.kappa <- lapply(c.mat.VEG.eg, kappa)
#subset 1:5
VEG.eg.tocsv.tot <- sapply(c.mat.VEG.eg.kappa,"[",c(1:5))
colnames(VEG.eg.tocsv.tot) <- c("VEG_DM_maxval", "VEG_DM_AUC", "VEG_DM_preval")
#write.csv(VEG.eg.tocsv.tot, file= paste0(out_folder,"EG_kappa_total_VEG.csv"))
#subset 6:11
sapply(c.mat.VEG.eg.kappa,"[",c(6:11))
VEG.eg.tocsv <- t(as.data.frame(lapply(c.mat.VEG.eg.kappa, function(x) {x[c(6:8,9)]})))
row.names(VEG.eg.tocsv) <- paste0(row.names(VEG.eg.tocsv), c(rep(c("_VEG_DM_maxval", "_VEG_DM_AUC", "_VEG_DM_preval"), each=4)))
rownames(VEG.eg.tocsv)
#output to csv
write.csv(VEG.eg.tocsv, file= paste0(out_folder,"EG_kappa_individ_VEG.csv"))
# COMBINE VTs into one dataframe
EG_kappa_all <- cbind(AR18.eg.tocsv.tot, AR9.eg.tocsv.tot, VEG.eg.tocsv.tot)
EG_kappa_all[c(seq(5,45, by=5))] <- as.numeric(EG_kappa_all[c(seq(5,45, by=5))]) # correcting the extra information in the list
write.csv(EG_kappa_all, file= paste0(out_folder,"EG_kappa_all.csv"))