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NonParamStat_matrixplot.R
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NonParamStat_matrixplot.R
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# This function is written to generate matrix plot for non-parametric stat results for all locations
# Input :
# data_ln_all : a list of length of all locations (output from NonParamStat.R for all locations)
# resloc : location to save the results
# nvar : an integer : number of factors which influence css between considered species
# nvar_names : a vector of characters containing names of n-variables
# tagon : logical (argument for vivj_matrix fn call)
# npa_stats : a character tag to choose any one of 3 npa stats
# wd,ht : width and height of fenarated plot
#---------------------------
source("mycorrplot.R")
#---------------------------
NonParamStat_matrixplot<-function(data_ln_all,resloc,nvar,nvar_names,tagon,npa_stats,wd,ht){
#------------------------------------
# initialize
CorlmCoru<-NA
PlmPu<-NA
D2umD2l<-NA
summary_nLU_CorlmCoru<-NA
summary_nLU_PlmPu<-NA
summary_nLU_D2umD2l<-NA
summary_LU_CorlmCoru<-NA
summary_LU_PlmPu<-NA
summary_LU_D2umD2l<-NA
#-------------------------------------
numloc<-length(data_ln_all)
selected_loc<-names(data_ln_all)
ind_loc<-stringr::str_sub(selected_loc,4,5)
#--------------------------Spearman plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$spear
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Spearman.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
#cat("loc=",loc,"\n")
dev.off()
}
#--------------------------Kendall plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$kend
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Kendall.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#========================================= For cor npa stats ===============================================
if(npa_stats=="cor"){
#--------------------------Corl plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Corl
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Corl.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(-cr,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------Coru plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Coru
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Coru.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(-cr,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------Corl-Coru plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Corl-data_ln_all[[i]]$Coru
indI<-data_ln_all[[i]]$posnI
indN<-data_ln_all[[i]]$posnN
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
CorlmCoru<-tempo
names(CorlmCoru)<-selected_loc
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
summary_nLU_CorlmCoru<-matrix(NA,2,numloc) # to keep total count on CorlmCoru for +ve or -ve numbers
colnames(summary_nLU_CorlmCoru)<-selected_loc
rownames(summary_nLU_CorlmCoru)<-c("nL","nU")
summary_LU_CorlmCoru<- summary_nLU_CorlmCoru # to keep sum on CorlmCoru values only for +ve or -ve numbers
rownames(summary_LU_CorlmCoru)<-c("L","U")
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Corl-Coru.pdf",sep=''),width=wd, height=ht)
#op<-par(mar=c(5.1, 2, 4.1, 8), mgp=c(3, 1, 0), las=0)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(-cr,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
z[data_ln_all[[loc]]$posnN]<-NA # this line was added to exclude -vely correlated species pair from nL,nU,L,U
# calculation, but it does not matter as for -vely correlated cells [sp_i,sp_j] and
# [sp_j,sp_i] nL,nU both will increase by same number, whereas L+U remains same and
# both L, U will change by same +, - factor
dl2<-nrow(z)-nvar
z1<-z[1:dl2,1:dl2] # only select sp-sp interaction matrix to calculate nL,nU,L,U
nL<-sum(z1>0,na.rm = T)
nU<-sum(z1<0,na.rm = T)
L<-sum(z1[which(z1>0,arr.ind=T)])
U<-sum(z1[which(z1<0,arr.ind=T)])
summary_nLU_CorlmCoru[1,loc]<-nL
summary_nLU_CorlmCoru[2,loc]<-nU
summary_LU_CorlmCoru[1,loc]<-L
summary_LU_CorlmCoru[2,loc]<-U
if(tagon == T){
mtext(paste0("n = ",ind_loc[loc]," : "),cex=6,side=1,adj=0.3,line=-1)
}
#mtext(paste0("nL =",nL,", nU =",nU),cex=5,side=1,adj=0.7)
mtext((as.expression(bquote('N'['L']^'n'*' = '*.(nL)))),cex=6,side=1,adj=0.5,col="red")# number of cells with Lower or Left tail dep.
mtext((as.expression(bquote('N'['R']^'n'*' = '*.(nU)))),cex=6,side=1,adj=0.8,col="blue")# number of cells with Upper or Right tail dep.
#par(op)
dev.off()
}
res<-list(CorlmCoru_all_ln_list=CorlmCoru,
summary_nLU_CorlmCoru=summary_nLU_CorlmCoru,
summary_LU_CorlmCoru=summary_LU_CorlmCoru)
}
#=========================================== For P npa stats ==========================================
if(npa_stats=="P"){
#--------------------------Pl plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Pl
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Pl.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------Pu plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Pu
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Pu.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------Pl-Pu plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$Pl-data_ln_all[[i]]$Pu
indI<-data_ln_all[[i]]$posnI
indN<-data_ln_all[[i]]$posnN
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
PlmPu<-tempo
names(PlmPu)<-selected_loc
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
summary_nLU_PlmPu<-matrix(NA,2,numloc)
colnames(summary_nLU_PlmPu)<-selected_loc
rownames(summary_nLU_PlmPu)<-c("nL","nU")
summary_LU_PlmPu<- summary_nLU_PlmPu # to keep sum on PlmPu values only for +ve or -ve numbers
rownames(summary_LU_PlmPu)<-c("L","U")
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_Pl-Pu.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(-cr,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
z[data_ln_all[[loc]]$posnN]<-NA # this line was added to exclude -vely correlated species pair from nL,nU,L,U
# calculation, but it does not matter as for -vely correlated cells [sp_i,sp_j] and
# [sp_j,sp_i] nL,nU both will increase by same number, whereas L+U remains same and
# both L, U will change by same +, - factor
dl2<-nrow(z)-nvar
z1<-z[1:dl2,1:dl2] # only select sp-sp interaction matrix to calculate nL,nU,L,U
nL<-sum(z1>0,na.rm = T)
nU<-sum(z1<0,na.rm = T)
L<-sum(z1[which(z1>0,arr.ind=T)])
U<-sum(z1[which(z1<0,arr.ind=T)])
summary_nLU_PlmPu[1,loc]<-nL
summary_nLU_PlmPu[2,loc]<-nU
summary_LU_PlmPu[1,loc]<-L
summary_LU_PlmPu[2,loc]<-U
if(tagon == T){
mtext(paste0("n = ",ind_loc[loc]," : "),cex=6,side=1,adj=0.3,line=-1)
}
mtext((as.expression(bquote('N'['L']^'n'*' = '*.(nL)))),cex=6,side=1,adj=0.5,col="red")# number of cells with Lower or Left tail dep.
mtext((as.expression(bquote('N'['R']^'n'*' = '*.(nU)))),cex=6,side=1,adj=0.8,col="blue")# number of cells with Upper or Right tail dep.
dev.off()
}
res<-list(PlmPu_all_ln_list=PlmPu,
summary_nLU_PlmPu=summary_nLU_PlmPu,
summary_LU_PlmPu=summary_LU_PlmPu)
}
#=========================================== For D2 npa stats ==========================================
if(npa_stats=="D2"){
#--------------------------D2l plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$D2l
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_D2l.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------D2u plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$D2u
indI<-data_ln_all[[i]]$posnI
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_D2u.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(0,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
dev.off()
}
#--------------------------D2u-D2l plot---------------------------
tempo<-vector("list",numloc)
for(i in 1:numloc){
tempo[[i]]<-data_ln_all[[i]]$D2u-data_ln_all[[i]]$D2l
indI<-data_ln_all[[i]]$posnI
indN<-data_ln_all[[i]]$posnN
tempo[[i]][indI]<-NA
diag(tempo[[i]])<-NA
}
D2umD2l<-tempo
names(D2umD2l)<-selected_loc
minval<-min(unlist(lapply(tempo,min,na.rm=T)))
maxval<-max(unlist(lapply(tempo,max,na.rm=T)))
cr<-max(abs(minval),abs(maxval))
summary_nLU_D2umD2l<-matrix(NA,2,numloc)
colnames(summary_nLU_D2umD2l)<-selected_loc
rownames(summary_nLU_D2umD2l)<-c("nL","nU")
summary_LU_D2umD2l<- summary_nLU_D2umD2l # to keep sum on D2umD2l values only for +ve or -ve numbers
rownames(summary_LU_D2umD2l)<-c("L","U")
for(loc in 1:numloc){
resloc2<-paste(resloc,selected_loc[loc],"/",sep="")
pdf(paste(resloc2,selected_loc[loc],file="_D2u-D2l.pdf",sep=''),width=wd, height=ht)
z<-tempo[[loc]]
if(nvar!=0){
dl<-nrow(z)-nvar+1
rownames(z)[c(dl:nrow(z))]<-nvar_names
}
colnames(z)<-rownames(z)
mycorrplot(z=z,
posnI_ind=data_ln_all[[loc]]$posnI,
posnN_ind=data_ln_all[[loc]]$posnN,
colrange=c(-cr,cr),nvar=nvar)
#segments(x0=c(0.5, 0.5), y0=c(0.5+nvar, 0.5+nrow(z)), x1=c(0.5+nrow(z), 0.5+nrow(z)), y1=c(0.5+nvar,0.5+nrow(z)), lwd=6,col="green")
#segments(c(0.5,0.5+nrow(z)), rep(0.5+nvar,1), c(0.5,0.5+nrow(z)), rep(0.5+nrow(z),1), lwd=6, col="green")
z[data_ln_all[[loc]]$posnN]<-NA # this line was added to exclude -vely correlated species pair from nL,nU,L,U
# calculation, but it does not matter as for -vely correlated cells [sp_i,sp_j] and
# [sp_j,sp_i] nL,nU both will increase by same number, whereas L+U remains same and
# both L, U will change by same +, - factor
dl2<-nrow(z)-nvar
z1<-z[1:dl2,1:dl2] # only select sp-sp interaction matrix to calculate nL,nU,L,U
nL<-sum(z1>0,na.rm = T)
nU<-sum(z1<0,na.rm = T)
L<-sum(z1[which(z1>0,arr.ind=T)])
U<-sum(z1[which(z1<0,arr.ind=T)])
summary_nLU_D2umD2l[1,loc]<-nL
summary_nLU_D2umD2l[2,loc]<-nU
summary_LU_D2umD2l[1,loc]<-L
summary_LU_D2umD2l[2,loc]<-U
if(tagon == T){
mtext(paste0("n = ",ind_loc[loc]," : "),cex=6,side=1,adj=0.3,line=-1)
}
mtext((as.expression(bquote('N'['L']^'n'*' = '*.(nL)))),cex=6,side=1,adj=0.5,col="red")# number of cells with Lower or Left tail dep.
mtext((as.expression(bquote('N'['R']^'n'*' = '*.(nU)))),cex=6,side=1,adj=0.8,col="blue")# number of cells with Upper or Right tail dep.
dev.off()
}
res<-list(D2umD2l_all_ln_list=D2umD2l,
summary_nLU_D2umD2l=summary_nLU_D2umD2l,
summary_LU_D2umD2l=summary_LU_D2umD2l)
}
return(res)
}