#' Process the output of Zmix_univ_tempered
#'
#' This function draws samples from a Wishart dist
#' @param v and s
#' @keywords Wishart
#' @export
#' @examples
#' #nope
Process_Output_Zmix<-function( Grun, isSim=FALSE, Burn=2000, LineUp=1, Pred_Reps=100, Zswitch_Sensitivity=0.01, makePlots=TRUE, Plot_Title="Results", SaveFileName="zmix", PlotType="Boxplot"){
mydata<-Grun$YZ
#require(wq)
Grun<-trimit(Out=Grun, Burn)
ifelse(isSim==TRUE, Y<-mydata$Y, Y<-mydata)
n<-length(Y)
K<-dim(Grun$Ps)[2]
minq4PLOT<-0.01
both<-cbind(melt(Grun$Mu)[,3], log(melt(Grun$Sig)[,3]^2))
Weights<-melt(Grun$Ps)[,3]
color <- as.factor(melt(Grun$Mu)[,2])
raincol<-rainbow(length(levels(color))); levels(color)<-raincol
trancol<-sapply(c(1:length(color)), function(x) adjustcolor(color[x], alpha.f=Weights[x]))
minmaxMEANS<-c(min(both[Weights>minq4PLOT ,1]), max(both[Weights>minq4PLOT ,1]))
# PLOT 1
slices <- prop.table(table((factor(Grun$SteadyScore, levels=c(1:K)))))
Lab.palette <- colorRampPalette(rainbow(K*3, alpha=.3), space = "Lab")
if(makePlots==TRUE){
png( file= paste(SaveFileName ,"_MCMCpp.png", sep=""),width = 900, height = 300)
par(mfrow=c(1, 3))
barplot(slices, ylim=c(0,1), main="Number of non-empty states", xlab="Number of non-empty states", ylab="Probability (from MCMC)")
abline(h=seq(0, 1, .05), lwd=0.5, col='LightGrey')
smoothScatter(both, colramp = Lab.palette, main="Posterior Surface", nrpoints = 0, xlab="Mean", ylab="LOG(Variance)")
plot(both, col=trancol, xlim=minmaxMEANS, ylim=c(0,100), xlab="Mean", ylab="Variance", bg='grey', main=paste("Posterior Samples:" ,"\n Transparency By Weight"))
dev.off()
}
## 1. split by number of components
K0<-as.numeric(names(table(Grun$SteadyScore)))
# SAVE table of tests, parameter estimates and clustering (Z's)
p_vals<-data.frame("K0"=K0, "Probability"=as.numeric(table(Grun$SteadyScore))/dim(Grun$Ps)[1],
"MAE"=NA, "MSE"=NA,"Pmin"=NA, "Pmax"=NA, "Concordance"=NA, "MAPE"=NA, "MSPE"=NA)
K0estimates<-vector("list", length(K0))
Zestimates<-vector("list", length(K0))
GrunK0us_FIN<-vector("list", length(K0))
ZTable<-vector("list", length(K0))
#for each K0:
for ( .K0 in 1:length(K0)){
if( p_vals$Probability[.K0]>0.05){
GrunK0<-Grun
# split data by K0
.iterK0<-c(1:dim(Grun$Ps)[1])[Grun$SteadyScore==K0[.K0]]
GrunK0$Mu<- Grun$Mu[.iterK0,]
GrunK0$Sig<-Grun$Sig[.iterK0,]
GrunK0$Ps<- Grun$Ps[.iterK0,]
GrunK0$Loglike<-Grun$Loglike[.iterK0]
GrunK0$Zs<- Grun$Zs[,.iterK0]
GrunK0$SteadyScore<-Grun$SteadyScore[.iterK0]
## 2. unswitch
GrunK0us<-Zswitch(GrunK0, LineUp, Zswitch_Sensitivity )
GrunK0us_FIN[[.K0]]<-GrunK0us
# PLOTS density pars
if(makePlots==TRUE){
GrunK0us$Pars$k<-as.factor(GrunK0us$Pars$k)
if(PlotType=='Density'){
p1<-ggplot(data=GrunK0us$Pars, aes(x=P, fill=k)) + geom_density( alpha=0.4)+ggtitle( bquote( atop(italic( .(Plot_Title) ), atop("Weights"))))+ ylab("")+xlab("") +theme_bw()+ theme(legend.position = "none")
p2<-ggplot(data=GrunK0us$Pars, aes(x=Mu, fill=k)) + geom_density( alpha=0.4)+ggtitle(ggtitle(bquote(atop(italic( "Posterior summaries"), atop("Means")))))+ylab("")+xlab("") +theme_bw()+ theme(legend.position = "none")
p3<-ggplot(data=GrunK0us$Pars, aes(x=Sig, fill=k)) +geom_density(alpha=0.4)+ggtitle(ggtitle(bquote(atop(italic(paste( "p(K=", .(K0[.K0]), ")=", .(p_vals$Probability[.K0]), sep="")), atop("Variances")))))+ylab("")+xlab("") +theme_bw()+ theme(legend.position = "none")
#grobframe <- arrangeGrob(p1, p2, p3, ncol=3, nrow=1,main = textGrob(paste(Plot_Title,": posterior parameter estimates for", K0[.K0]," groups"), gp = gpar(fontsize=8, fontface="bold.italic", fontsize=14)))
#ggsave(plot=grobframe, filename= paste("PosteriorParDensities_",Plot_Title,"_K0", K0[.K0],".pdf", sep="") , width=20, height=7, units='cm' )
} else if (PlotType=="Boxplot"){
pii.mean = aggregate(P ~ k, GrunK0us$Pars, mean)
mu.mean = aggregate(Mu ~ k, GrunK0us$Pars, mean)
var.mean = aggregate(Sig ~ k, GrunK0us$Pars, mean)
p1<-ggplot(data=GrunK0us$Pars, aes(y=P, x=k)) + geom_boxplot(aes(fill=k), outlier.size=0.5)+ ylab("")+xlab("Components (k)") +theme_bw()+ theme(legend.position = "none")+ggtitle( bquote( atop(italic( .(Plot_Title) ), atop("Weights"))))#+ geom_text(data =pii.mean, aes(label=signif(P,4)),size=4, col='yellow',vjust = 1)
p2<-ggplot(data=GrunK0us$Pars, aes(y=Mu, x=k))+ geom_boxplot(aes(fill=k), outlier.size=0.5)+ ylab("")+xlab("Components (k)") +theme_bw()+ theme(legend.position = "none")+ggtitle(ggtitle(bquote(atop(italic( "Posterior summaries"), atop("Means")))))
p3<-ggplot(data=GrunK0us$Pars, aes(y=Sig, x=k)) + geom_boxplot(aes(fill=k), outlier.size=0.5)+ ylab("")+xlab("Components (k)") +theme_bw()+ theme(legend.position = "none")+ggtitle(ggtitle(bquote(atop(italic(paste( "p(K=", .(K0[.K0]), ")=", .(p_vals$Probability[.K0]), sep="")), atop("sqrt(Variance).")))))
}
}
# ALOC PROBABILITIES
Ztemp<-GrunK0us$Zs
ZTable[[.K0]]<-data.frame("myY"=NULL, "k"=NULL, "Prob"=NULL)
maxK<-max(Ztemp)
for (i in 1:dim(Ztemp)[1]){rr<-factor(Ztemp[i,], levels=1:maxK)
ZTable[[.K0]]<-rbind(ZTable[[.K0]],cbind(i,c(1:maxK), matrix(table(rr)/ length(rr) ))) }
names(ZTable[[.K0]])<-c("Yid", "k", "Prob")
ZTable[[.K0]]$k<-as.factor(ZTable[[.K0]]$k)
ggAllocationPlot<-function( outZ, myY){
grr<-outZ[order(myY),]
grrTable<-data.frame("myY"=NULL, "k"=NULL, "Prob"=NULL)
maxK<-max(grr)
for (i in 1:length(myY)){rr<-factor(grr[i,], levels=1:maxK)
grrTable<-rbind(grrTable,cbind(i,c(1:maxK), matrix(table(rr)/ length(rr) ))) }
names(grrTable)<-c("myY", "k", "Prob")
grrTable$k<-as.factor(grrTable$k)
gp<-ggplot(grrTable, aes(x=myY, y=k, fill=Prob)) + geom_tile()+ggtitle( "Posterior allocations")+
xlab("index of ordered y")+
scale_fill_gradientn(colours = c("#ffffcc","#a1dab4","#41b6c4","#2c7fb8","#253494" ))+theme_bw()+theme(legend.position='right')
#ggsave( plot=gp, filename=paste( "Allocations_", plotfilename ,"K_",maxK, ".pdf",sep="") )
gp
}
if(makePlots==TRUE){ p4<-ggAllocationPlot(GrunK0us$Zs, Y )}
maxZ<-function (x) as.numeric(names(which.max(table( x ))))
Zhat<- factor( apply(t(GrunK0us$Zs), 2,maxZ))
Zestimates[[.K0]]<-Zhat
## 3. , MSE
GrunK0us$Pars$k<-as.numeric(as.character(GrunK0us$Pars$k))
Zetc<-Zagg(GrunK0us, Y)
p_vals$MAE[.K0]<- Zetc$MAE
p_vals$MSE[.K0]<- Zetc$MSE
postPredTests<-PostPredFunk( GrunK0us,Zetc, Y, Pred_Reps, Plot_Title)
# store output in p_vasl
p_vals$Pmin[.K0]<-postPredTests$MinP
p_vals$Pmax[.K0]<-postPredTests$MaxP
p_vals$MAPE[.K0]<-postPredTests$MAPE
p_vals$MSPE[.K0]<-postPredTests$MSPE
p_vals$Concordance[.K0]<-1-postPredTests$Concordance
p5<-postPredTests$ggp
# CI
.par<-melt(GrunK0us$Pars, id.vars=c("Iteration", "k"))
theta<-aggregate( value~variable+factor(k), mean ,data=.par)
mu<-round(aggregate( value~variable+factor(k), mean ,data=.par)[,3], 2)
ci<-round(aggregate( value~variable+factor(k), quantile,c(0.025, 0.975) ,data=.par)[,3],2)
thetaCI<-cbind( theta[,c(1,2)] , "value"=paste( mu, "(", ci[,1] , "," ,ci[,2] ,")", sep="" ))
K0estimates[[.K0]]<-cbind(thetaCI, "K0"=K0[.K0])
if(makePlots==TRUE){
if(K0[.K0]>1){
png( file= paste("PPplots_", SaveFileName ,"K_", K0[.K0] ,".png", sep=""),width = 1200, height = 600)
print( layOut( list(p1, 1, 1:2),
list(p2, 1, 3:4),
list(p3, 1,5:6),
list(p4, 2,1:3),
list(p5, 2,4:6)))
dev.off()
}}
}}
Final_Pars<-do.call(rbind, K0estimates)
print(p_vals)
#Result<-list( Final_Pars, p_vals, "Z"=Zhat)
#save(Result, file=paste("PPresults_", SaveFileName ,".RDATA", sep=""))
return(list( Final_Pars, p_vals, Zestimates, ZTable, "Pars_us"=GrunK0us_FIN))
}
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