misc/GLM_predictplot.R

In cesco-lab/Vigie-Chiro_scripts: Predict Distribution Of Chiropter

library(data.table)
library(glmmTMB)
library(ggeffects)
library(ggplot2)
library(moments)
library(Hmisc)
library(beepr)
VarToPredict=c("Jour","AT81","ThAve")
VarNotToPredict=c("DecOT") #list of variables in interaction with VarToPredict
#ToPredict=c("DecOT [((-10:10)/10)]","AT81 [-1,0,1]")
LevelsToPredict=c((-30:30)/10)
GLMPref="GLMnonselect_MultiSp_DecOT2_AT81_Jour2_simplifie1_CS_STI_HurdleBioclim"
FFBT="forBackTransform_variables_choisies.csv"
SpeciesList=fread("SpeciesList.csv")


DirVTP=""
for (i in 1:length(VarToPredict))
{
  DirVTP=paste(DirVTP,VarToPredict[i],sep="_")
} 



SpeciesShort=subset(SpeciesList,select=c("Esp","Scientific name","Group"))
forBackTransform=fread(FFBT)
ListMod=list.files("./VigieChiro/GLMs/",pattern=GLMPref,full.names=T)
VarMatch=match(VarToPredict,forBackTransform$VarList)

ToPredict=VarToPredict
AddPredict=as.character(LevelsToPredict[1])
for (z in 2:length(LevelsToPredict))
{
  AddPredict=paste(AddPredict,LevelsToPredict[z],sep=",")
  
}
ToPredict[1]=paste0(VarToPredict[1]," [",AddPredict,"]")
if(length(ToPredict)==2){ToPredict[2]=paste(ToPredict[2],"[1,0,-1]")}

#Species=vector()
Peak=vector()
Skewness=vector()
Kurtosis=vector()
MeanD=vector()
Q10=vector()
Q25=vector()
Q50=vector()
Q75=vector()
Q90=vector()


for (i in 1:length(ListMod))
{
  print(ListMod[i])
  load(ListMod[i])
  Terms=terms(ModSp)
  TermLabels=attr(Terms,"term.labels")
  
  
  TermSelect=vector()
  for(j in 1:length(VarToPredict))
  {
    TermSelect=c(TermSelect,subset(TermLabels,grepl(VarToPredict[j],TermLabels)))
  }
  for(j in 1:length(VarNotToPredict))
  {
    TermSelect=subset(TermSelect,!grepl(VarNotToPredict[j],TermSelect))
  }
  
  TermTarget=(!is.na(match(TermLabels,TermSelect)))
  PVal=coef(summary(ModSp))$cond[,4]
  PVal_woInt=PVal[2:length(PVal)]
  TestVar=subset(PVal_woInt,TermTarget)
  if(sum(is.na(TestVar))<length(TestVar))
  {
    if(min(subset(TestVar,!is.na(TestVar)))<0.05)
    {
      
      ModInfo=tstrsplit(ListMod[i],"_")
      #Species=c(Species,substr(ModInfo[[length(ModInfo)]],nchar(ModInfo[[length(ModInfo)]])-9,nchar(ModInfo[[length(ModInfo)]])-4))
      
      # Create predict table
      print(Sys.time())
      pr1.0 <- ggpredict(ModSp, c(terms = ToPredict),pretty = FALSE)
      Sys.time()
      pr1=pr1.0
      # Backtransform before scaling (utiliser la table crée lors de l'utilisation de la fonction scale)
      pr1$x=pr1$x*forBackTransform$Sdev[VarMatch[1]]+forBackTransform$Mean[VarMatch[1]]
      pr1$predicted=subset(pr1$predicted,!is.na(pr1$x))
      pr1$group=subset(pr1$group,!is.na(pr1$x))
      pr1$x=subset(pr1$x,!is.na(pr1$x))
      #pr1$invgroup=as.factor(-(as.numeric(as.character(pr1$group))))
      
      
      Xmin=quantile(pr1$x,0.05)
      Xmax=quantile(pr1$x,0.95)
      #Ymin=quantile(pr1$predicted,0.04)*0.5
      Ymin=0
      Ymax=quantile(pr1$predicted,0.99)*1.5
      
      
      RawPredict=log(pr1$predicted)
      
      summary(pr1$predicted)
      Peak=c(Peak,pr1$x[which.max(pr1$predicted)])
      skewness(RawPredict)
      skewness(pr1$predicted)
      #skewness(pr1$predicted[150:270])
      
      Skewness=c(Skewness,skewness(pr1$predicted))
      Kurtosis=c(Kurtosis,kurtosis(pr1$predicted))
      MeanD=c(MeanD,weighted.mean(pr1$x,w=pr1$predicted))
      Quantiles=wtd.quantile(pr1$x,weights=pr1$predicted,probs=c(0.1,0.25,0.5,0.75,0.9))
      Q10=c(Q10,Quantiles[1])
      Q25=c(Q25,Quantiles[2])
      Q50=c(Q50,Quantiles[3])
      Q75=c(Q75,Quantiles[4])
      Q90=c(Q90,Quantiles[5])
      pr1$anom=as.numeric(as.character(pr1$group))
      names(pr1)[ncol(pr1)]=VarToPredict[2]
      pr1$predicted=pmin(pr1$predicted,10000)    
      
      # Plot
      #GradColor=scale_color_gradient2(low="blue",mid="green",high="red")
      if(nlevels(pr1$group)==3)
      {
        if(!is.na(match("facet",names(pr1)))){
        for (j in 1:nlevels(as.factor(pr1$facet)))
        {
          DirNameTemp=paste0(paste0(dirname(ListMod[i]),"/Plots/",DirVTP))
          dir.create(DirNameTemp)
          GraphNameTemp=paste0(DirNameTemp,"/",j,"_",gsub(".glm","",basename(ListMod[i])),".png")
          png(filename=GraphNameTemp, res=100)
          
          prtemp=subset(pr1,pr1$facet==levels(as.factor(pr1$facet))[j])
          print(ggplot(prtemp, aes(x, predicted,fill=group)) +
                  #scale_color_gradient2(low="blue",mid="green",high="red") +
                  #scale_colour_discrete(name = "anom")+
                  scale_color_manual(values=c("blue","green","red"))+
                  geom_line(aes(color = group),size=1)  +
                  #geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==1,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="red")+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==0,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="green")+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==-1,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="blue")+
                  xlab(VarToPredict) + 
                  ylab("Acoustic Activity") +
                  scale_x_continuous(limits = c(Xmin, Xmax)) +
                  scale_y_continuous(limits = c(Ymin, Ymax)) +
                  theme_bw(base_size = 10)+
                  
                  ggtitle(gsub(".png","",basename(GraphNameTemp))) 
                #theme(plot.title = element_text(size = 8))+
                #scale_fill_discrete(values=c("blue","green","red"),guide=FALSE)+
          )
          dev.off()
        }
        }else{
          DirNameTemp=paste0(paste0(dirname(ListMod[i]),"/Plots/",DirVTP))
          dir.create(DirNameTemp)
          GraphNameTemp=paste0(DirNameTemp,"/",gsub(".glm","",basename(ListMod[i])),".png")
          png(filename=GraphNameTemp, res=100)
          
          prtemp=pr1
          print(ggplot(prtemp, aes(x, predicted,fill=group)) +
                  #scale_color_gradient2(low="blue",mid="green",high="red") +
                  #scale_colour_discrete(name = "anom")+
                  scale_color_manual(values=c("blue","green","red"))+
                  geom_line(aes(color = group),size=1)  +
                  #geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==1,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="red")+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==0,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="green")+
                  geom_ribbon(data=prtemp[prtemp[,ncol(prtemp)]==-1,]
                              ,aes(ymin = conf.low, ymax = conf.high), alpha = .1
                              ,fill="blue")+
                  xlab(VarToPredict) + 
                  ylab("Acoustic Activity") +
                  scale_x_continuous(limits = c(Xmin, Xmax)) +
                  scale_y_continuous(limits = c(Ymin, Ymax)) +
                  theme_bw(base_size = 10)+
                  
                  ggtitle(gsub(".png","",basename(GraphNameTemp))) 
                #theme(plot.title = element_text(size = 8))+
                #scale_fill_discrete(values=c("blue","green","red"),guide=FALSE)+
          )
          dev.off()
          
        }  
      }else{
        print(ggplot(pr1, aes(x, predicted)) +
                geom_line()  +
                #scale_color_gradient2(low="blue",mid="green",high="red") +
                geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)+
                xlab(VarToPredict) + 
                ylab("Acoustic Activity") +
                scale_x_continuous(limits = c(Xmin, Xmax)) +
                scale_y_continuous(limits = c(Ymin, Ymax)) +
                
                ggtitle(ListMod[i]) + 
                theme_bw(base_size = 13)
        )
        
      }
    }
  }
}

beep()