Nothing
MVN_MCMC <-
function(data, steps, pars, values, tol=0.3, ...){
MCMC <- MVN_GibbsSampler(steps, data, ...)
samples_num <- length(MCMC[,1])
if(length(pars) != length(values) | length(pars) > length(data$mean)){
stop("Error assignment: please confirm that pars and values have the identical and correct dimension (be no more than that of input data). ")
}
else if(length(pars) == 0){
MCMC <- cbind(MCMC, matrix(data = 0, samples_num, 1))
MCMC <- cbind(MCMC, matrix(data = 1, samples_num, 1))
}
else{
idx <- matrix(NA, samples_num, 2)
for (i in 1:samples_num){
distance <- MCMC[i,pars]-values
idx[i,1] <- norm(as.matrix(distance), type = "2")
idx[i,2] <- (idx[i,1] <= tol)
}
MCMC <- cbind(MCMC, idx)
}
p <- length(MCMC[1,])
colnames(MCMC)[p-1] <- "Euclidean.Distance"
colnames(MCMC)[p] <- "Accept"
AcceptRate <- as.numeric(count(MCMC[,p])[2,2]/samples_num)
Accept <- MCMC[which(MCMC[,p] == 1),]
Reject <- MCMC[which(MCMC[,p] == 0),]
results <- list("AcceptRate"=AcceptRate, "MCMCdata"=MCMC, "Accept"=Accept, "Reject"=Reject)
return(results)
}
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