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S7_show_parameter_estimates.R
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S7_show_parameter_estimates.R
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#setwd("") # set directory to the folder where the folders "data", "models" and "panels" are
library(Hmsc)
library(Hmsc)
library(colorspace)
library(corrplot)
library(writexl)
thin = 100
samples = 250
nChains = 4
filename = paste("models/models_thin_", as.character(thin),
"_samples_", as.character(samples),
"_chains_",as.character(nChains),
".Rdata",sep = "")
load(filename)
nm = length(models)
filename = paste("panels/parameter_estimates.pdf")
pdf(file = filename)
for(j in 1:nm){
m = models[[j]]
VP = computeVariancePartitioning(m)
vals = VP$vals
mycols = rainbow(nrow(VP$vals))
plotVariancePartitioning(hM=m, VP=VP,cols = mycols, args.leg=list(bg="white",cex=0.7),
main = paste0("Proportion of explained variance, ",modelnames[[j]]),cex.main=0.8)
preds = computePredictedValues(m)
MF = evaluateModelFit(hM=m, predY=preds)
R2 = NULL
if(!is.null(MF$TjurR2)){
TjurR2 = MF$TjurR2
vals = rbind(vals,TjurR2)
R2=TjurR2
}
if(!is.null(MF$R2)){
R2=MF$R2
vals = rbind(vals,R2)
}
filename = paste0("panels/parameter_estimates_VP_",modelnames[[j]],".csv")
write.csv(vals,file=filename)
if(!is.null(R2)){
VPr = VP
for(k in 1:m$ns){
VPr$vals[,k] = R2[k]*VPr$vals[,k]
}
VPr$vals = VPr$vals[,order(-R2)]
plotVariancePartitioning(hM=m, VP=VPr,cols = mycols, args.leg=list(bg="white",cex=0.7),ylim=c(0,1),
main=paste0("Proportion of raw variance, ",modelnames[[j]]),cex.main=0.8)
}
}
for(j in 1:nm){
m = models[[j]]
postBeta = getPostEstimate(m, parName="Beta")
show.sp.names = (is.null(m$phyloTree) && m$ns<=20)
plotBeta(m, post=postBeta, supportLevel = 0.95,param="Sign",
plotTree = !is.null(m$phyloTree),
covNamesNumbers = c(TRUE,FALSE),
spNamesNumbers=c(show.sp.names,FALSE),
cex=c(0.6,0.6,0.8))
mymain = paste0("BetaPlot, ",modelnames[[j]])
if(!is.null(m$phyloTree)){
mpost = convertToCodaObject(m)
rhovals = unlist(poolMcmcChains(mpost$Rho))
mymain = paste0(mymain,", E[rho] = ",round(mean(rhovals),2),", Pr[rho>0] = ",round(mean(rhovals>0),2))
}
title(main=mymain, line=2.5, cex.main=0.8)
me = as.data.frame(t(postBeta$mean))
me = cbind(m$spNames,me)
colnames(me) = c("Species",m$covNames)
po = as.data.frame(t(postBeta$support))
po = cbind(m$spNames,po)
colnames(po) = c("Species",m$covNames)
ne = as.data.frame(t(postBeta$supportNeg))
ne = cbind(m$spNames,ne)
colnames(ne) = c("Species",m$covNames)
vals = list("Posterior mean"=me,"Pr(x>0)"=po,"Pr(x<0)"=ne)
filename = paste0("panels/parameter_estimates_Beta_",modelnames[j],".xlsx")
writexl::write_xlsx(vals,path = filename)
}
for(j in 1:nm){
if(m$nt>1){
m = models[[j]]
postGamma = getPostEstimate(m, parName="Gamma")
plotGamma(m, post=postGamma, supportLevel = 0.9, param="Sign",
covNamesNumbers = c(TRUE,FALSE),
trNamesNumbers=c(m$nt<21,FALSE),
cex=c(0.6,0.6,0.8))
title(main=paste0("GammaPlot ",modelnames[[j]]), line=2.5,cex.main=0.8)
}
}
for(j in 1:nm){
m = models[[j]]
OmegaCor = computeAssociations(m)
supportLevel = 0.95
for (r in 1:m$nr){
plotOrder = corrMatOrder(OmegaCor[[r]]$mean,order="AOE")
toPlot = ((OmegaCor[[r]]$support>supportLevel) + (OmegaCor[[r]]$support<(1-supportLevel))>0)*sign(OmegaCor[[r]]$mean)
if(m$ns>20){
colnames(toPlot)=rep("",m$ns)
rownames(toPlot)=rep("",m$ns)
}
mymain = paste0("Associations, ",modelnames[[j]], ": ",names(m$ranLevels)[[r]])
if(m$ranLevels[[r]]$sDim>0){
mpost = convertToCodaObject(m)
alphavals = unlist(poolMcmcChains(mpost$Alpha[[1]][,1]))
mymain = paste0(mymain,", E[alpha1] = ",round(mean(alphavals),2),", Pr[alpha1>0] = ",round(mean(alphavals>0),2))
}
corrplot(toPlot[plotOrder,plotOrder], method = "color",
col=colorRampPalette(c("blue","white","red"))(3),
mar=c(0,0,1,0),
main=mymain,cex.main=0.8)
me = as.data.frame(OmegaCor[[r]]$mean)
me = cbind(m$spNames,me)
colnames(me)[1] = ""
po = as.data.frame(OmegaCor[[r]]$support)
po = cbind(m$spNames,po)
colnames(po)[1] = ""
ne = as.data.frame(1-OmegaCor[[r]]$support)
ne = cbind(m$spNames,ne)
colnames(ne)[1] = ""
vals = list("Posterior mean"=me,"Pr(x>0)"=po,"Pr(x<0)"=ne)
filename = paste0("panels/parameter_estimates_Omega_",modelnames[[j]],"_",names(m$ranLevels)[[r]],".xlsx")
writexl::write_xlsx(vals,path = filename)
}
}
dev.off()
#
# # Separate the code without space model: perform to reduce the error.
# # nm = length(models)
# ns <- 2
# filename = paste("panels/parameter_estimates2NO_space.pdf")
# nm
# for(j in 1:ns){
# m = models[[j]]
# OmegaCor = computeAssociations(m)
# supportLevel = 0.95
# for (r in 1:m$nr){
# plotOrder = corrMatOrder(OmegaCor[[r]]$mean,order="AOE")
# toPlot = ((OmegaCor[[r]]$support>supportLevel) + (OmegaCor[[r]]$support<(1-supportLevel))>0)*sign(OmegaCor[[r]]$mean)
# if(m$ns>20){
# colnames(toPlot)=rep("",m$ns)
# rownames(toPlot)=rep("",m$ns)
# }
# mymain = paste0("Associations, ",modelnames[[j]], ": ",names(m$ranLevels)[[r]])
# if(m$ranLevels[[r]]$sDim>0){
# mpost = convertToCodaObject(m)
# alphavals = unlist(poolMcmcChains(mpost$Alpha[[1]][,1]))
# mymain = paste0(mymain,", E[alpha1] = ",round(mean(alphavals),2),", Pr[alpha1>0] = ",round(mean(alphavals>0),2))
# }
# corrplot(toPlot[plotOrder,plotOrder], method = "color",
# col=colorRampPalette(c("blue","white","red"))(3),
# mar=c(0,0,1,0),
# main=mymain,cex.main=0.8)
#
# me = as.data.frame(OmegaCor[[r]]$mean)
# me = cbind(m$spNames,me)
# colnames(me)[1] = ""
# po = as.data.frame(OmegaCor[[r]]$support)
# po = cbind(m$spNames,po)
# colnames(po)[1] = ""
# ne = as.data.frame(1-OmegaCor[[r]]$support)
# ne = cbind(m$spNames,ne)
# colnames(ne)[1] = ""
# vals = list("Posterior mean"=me,"Pr(x>0)"=po,"Pr(x<0)"=ne)
# filename = paste0("panels/parameter_estimates_Omega_",modelnames[[j]],"_",names(m$ranLevels)[[r]],".xlsx")
# writexl::write_xlsx(vals,path = filename)
# }
# }
# dev.off()
# getwd()
#
# head(models[[1]])$X
# groupnames= c("Units", "HIDRO_MEAN_500", "NDVI_2000", "DIST_ARROZ", "DIST_urbano" )
# group <- c(1,1,1,1,1,1,2,3,4,5)
# VP <- list()
# for ( i in 1:2){
# VP[[i]] = computeVariancePartitioning(models[[i]], group = group, groupnames = groupnames)
# }
#
# VP