#'
#' Function for partial dependency plots from BRT models
#' indf@= brt dataframe with predictions
#' filen@=name of file type of plot
#'
partbrtpl<-function(indf,model,x,y,varname,filen){
mod.l<-unique(indf[,model])
for (i in 1:length(mod.l)){
# title for response
tmp<-indf[indf[,model] %in% mod.l[i],]
f<-substr(as.character(unique(tmp[,model])), start=1, stop=1)
l<-substr(as.character(unique(tmp[,model])),start=nchar(as.character(unique(tmp[,model]))),
stop=nchar(as.character(unique(tmp[,model]))))
filen1<-paste(f,filen,l,".JPG",sep="")
dynylab<-paste(f,filen,l," richness",sep="")
# create plot
p<-ggplot(data=tmp,aes_string(x=x,y=y))+geom_line()+
theme_bw()+ylab(dynylab)+xlab("Predictor")+facet_wrap(~varname,scale="free")+
theme(strip.text.x = element_text(size=9, face="bold"),strip.text.y = element_text(size=9, face="bold"))
ggsave(p,file=filen1,width=8,height=7)
}
}
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