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#' @title validMCMC
#' @author Oyvind Bleka <Oyvind.Bleka.at.fhi.no>
#' @description Validates aposteriori samples from MCMC method
#' @details This function takes samples from the MCMC as given in a matrix and shows the aposterior functions.
#' @param mcmcfit A object returned by contLikMCMC
#' @param trace Boolean for whether showing trace of samples.
#' @param acf Boolean for whether showing autocorrelation function of samples.
#' @export
validMCMC <- function(mcmcfit,trace=TRUE,acf=TRUE) {
txt <- colnames(mcmcfit$posttheta)
#Ubound <- mcmcfit$Ubound #upper boundaries of parameters
Ubound <- apply(mcmcfit$posttheta,2,max)
Lbound <- apply(mcmcfit$posttheta,2,min)
p <- length(txt)
par(mfrow=c(p,1+sum(c(trace,acf)) ),mar = c(1.2,1,1,0.2), mgp = c(0,0.2,0))
for(i in 1:p) {
dens <- density(mcmcfit$posttheta[,i],from=Lbound[i],to=Ubound[i],n=max(Ubound[i],1024))
xrange <- range(mcmcfit$posttheta[,i])
mled <-dnorm(dens$x,mcmcfit$MLE[i],sqrt(mcmcfit$Sigma[i,i])) #density of lazy bayes
plot(dens$x,dens$y,ty="l",main=txt[i],xlab="",ylab="",ylim=c(0,max(mled,dens$y)),xlim=xrange )
lines(dens$x,mled,col=2,lty=2,ylab="",xlab="")
if(trace) plot(mcmcfit$posttheta[,i],ty="l",ylab="",xlab="")
if(acf) acf(mcmcfit$posttheta[,i],lag.max=200,ylab="",xlab="")
}
dev.new()
op <- par(no.readonly = TRUE)
dev.off()
par(op)
}
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