# R/PosteriorChecks.R In LaplacesDemon: Complete Environment for Bayesian Inference

#### Documented in PosteriorChecks

```###########################################################################
# PosteriorChecks                                                         #
#                                                                         #
# The purpose of the PosteriorChecks function is to provide additional    #
# checks of the posterior, including the probability that each theta is   #
# greater than zero, kurtosis, and skewness. This function requires an    #
# object of class demonoid, laplace, or pmc. The kurtosis and skewness    #
# checks return nothing (NA) for classes laplace and vb, because          #
# parameters from Laplace Approximation and Variational Bayes are         #
# normally distributed, by definition.                                    #
###########################################################################

PosteriorChecks <- function(x, Parms=NULL)
{
### Initial Checks
if(missing(x)) stop("The x argument is required.")
if(!identical(class(x), "demonoid") &
!identical(class(x), "laplace") &
!identical(class(x), "pmc") &
!identical(class(x), "vb"))
stop("An object of class demonoid, iterquad, laplace, pmc, or vb is required.")
### Kurtosis and Skewness Functions
kurtosis <- function(x) {
m4 <- mean((x - mean(x))^4)
kurt <- m4 / (sd(x)^4)-3
return(kurt)}
skewness <-  function(x) {
m3 <- mean((x - mean(x))^3)
skew <- m3 / (sd(x)^3)
return(skew)}
### Posterior Checks
if(identical(class(x), "demonoid")) {
### Posterior and Monitors
if(is.matrix(x\$Posterior2) == FALSE) {
post <- cbind(x\$Posterior1, x\$Monitor)
cat("\nWARNING: Non-stationary samples used.\n\n")}
else {
post <- cbind(x\$Posterior2,
x\$Monitor[(x\$Rec.BurnIn.Thinned+1):nrow(x\$Monitor),])}
colnames(post) <- c(colnames(x\$Posterior1), colnames(x\$Monitor))
### Selecting Parms
if(is.null(Parms)) {keepcols <- 1:ncol(post)}
else {
Parms <- sub("\\[","\\\\[",Parms)
Parms <- sub("\\]","\\\\]",Parms)
Parms <- sub("\\.","\\\\.",Parms)
if(length(grep(Parms[1], colnames(post))) == 0)
stop("Parameter in Parms does not exist.")
keepcols <- grep(Parms[1], colnames(post))
if(length(Parms) > 1) {
for (i in 2:length(Parms)) {
if(length(grep(Parms[i], colnames(post))) == 0)
stop("Parameter in Parms does not exist.")
keepcols <- c(keepcols,
grep(Parms[i], colnames(post)))}}}
temp <- colnames(post)[keepcols]
post <- post[,keepcols]
colnames(post) <- temp
### Correlation Table
options(warn=-1); postcor <- cor(post); options(warn=0)
### Summary Table
Summ <- matrix(NA, ncol(post), 8)
rownames(Summ) <- colnames(post)
colnames(Summ) <- c("p(theta > 0)", "N.Modes", "Kurtosis",
"Skewness", "Burn-In", "IAT", "ISM", "AR")
options(warn=-1)
for (i in 1:ncol(post)) {
Summ[i,1] <- mean(post[,i] > 0)
Summ[i,2] <- length(Modes(post[,i])[[1]])
Summ[i,3] <- round(kurtosis(post[,i]),3)
Summ[i,4] <- round(skewness(post[,i]),3)
Summ[i,6] <- round(IAT(post[,i]),3)}
Summ[,5] <- burnin(post)
Summ[,7] <- round(ESS(post)/x\$Min, 3)
Summ[,8] <- AcceptanceRate(post)
options(warn=0)
}
### Posterior
if(any(is.na(x\$Posterior)))
stop("Posterior samples do not exist.")
post <- x\$Summary1
### Selecting Parms
if(is.null(Parms)) {keeprows <- 1:nrow(post)}
else {
Parms <- sub("\\[","\\\\[",Parms)
Parms <- sub("\\]","\\\\]",Parms)
Parms <- sub("\\.","\\\\.",Parms)
if(length(grep(Parms[1], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- grep(Parms[1], rownames(post))
if(length(Parms) > 1) {
for (i in 2:length(Parms)) {
if(length(grep(Parms[i], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- c(keeprows,
grep(Parms[i], rownames(post)))}}}
temp <- rownames(post)[keeprows]
post <- post[keeprows,]
rownames(post) <- temp
Posterior <- x\$Posterior
colnames(Posterior) <- rownames(post)
### Correlation Table
options(warn=-1); postcor <- cor(Posterior); options(warn=0)
### Summary Table
Summ <- matrix(NA, nrow(post), 8)
rownames(Summ) <- rownames(post)
colnames(Summ) <- c("p(theta > 0)", "N.Modes", "Kurtosis",
"Skewness", "Burn-In", "IAT", "ISM", "AR")
options(warn=-1)
for (i in 1:ncol(Posterior)) {
Summ[i,1] <- mean(Posterior[,i] > 0)
Summ[i,2] <- length(Modes(Posterior[,i])[[1]])
Summ[i,3] <- round(kurtosis(Posterior[,i]),3)
Summ[i,4] <- round(skewness(Posterior[,i]),3)
Summ[i,5] <- 0
Summ[i,6] <- round(IAT(Posterior[,i]),3)}
Summ[,7] <- NA
Summ[,8] <- 1
options(warn=0)
}
else if(identical(class(x), "laplace")) {
### Posterior
if(any(is.na(x\$Posterior)))
stop("Posterior samples do not exist.")
post <- x\$Summary1
### Selecting Parms
if(is.null(Parms)) {keeprows <- 1:nrow(post)}
else {
Parms <- sub("\\[","\\\\[",Parms)
Parms <- sub("\\]","\\\\]",Parms)
Parms <- sub("\\.","\\\\.",Parms)
if(length(grep(Parms[1], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- grep(Parms[1], rownames(post))
if(length(Parms) > 1) {
for (i in 2:length(Parms)) {
if(length(grep(Parms[i], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- c(keeprows,
grep(Parms[i], rownames(post)))}}}
temp <- rownames(post)[keeprows]
post <- post[keeprows,]
rownames(post) <- temp
Posterior <- x\$Posterior
colnames(Posterior) <- rownames(post)
### Correlation Table
options(warn=-1); postcor <- cor(Posterior); options(warn=0)
### Summary Table
Summ <- matrix(NA, nrow(post), 8)
rownames(Summ) <- rownames(post)
colnames(Summ) <- c("p(theta > 0)", "N.Modes", "Kurtosis",
"Skewness", "Burn-In", "IAT", "ISM", "AR")
options(warn=-1)
for (i in 1:ncol(Posterior)) {
Summ[i,1] <- mean(Posterior[,i] > 0)
Summ[i,2] <- length(Modes(Posterior[,i])[[1]])
Summ[i,3] <- round(kurtosis(Posterior[,i]),3)
Summ[i,4] <- round(skewness(Posterior[,i]),3)
Summ[i,5] <- 0
Summ[i,6] <- round(IAT(Posterior[,i]),3)}
Summ[,7] <- NA
Summ[,8] <- 1
options(warn=0)
}
else if(identical(class(x), "vb")) {
### Posterior
if(any(is.na(x\$Posterior)))
stop("Posterior samples do not exist.")
post <- x\$Summary1
### Selecting Parms
if(is.null(Parms)) {keeprows <- 1:nrow(post)}
else {
Parms <- sub("\\[","\\\\[",Parms)
Parms <- sub("\\]","\\\\]",Parms)
Parms <- sub("\\.","\\\\.",Parms)
if(length(grep(Parms[1], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- grep(Parms[1], rownames(post))
if(length(Parms) > 1) {
for (i in 2:length(Parms)) {
if(length(grep(Parms[i], rownames(post))) == 0)
stop("Parameter in Parms does not exist.")
keeprows <- c(keeprows,
grep(Parms[i], rownames(post)))}}}
temp <- rownames(post)[keeprows]
post <- post[keeprows,]
rownames(post) <- temp
Posterior <- x\$Posterior
colnames(Posterior) <- rownames(post)
### Correlation Table
options(warn=-1); postcor <- cor(Posterior); options(warn=0)
### Summary Table
Summ <- matrix(NA, nrow(post), 8)
rownames(Summ) <- rownames(post)
colnames(Summ) <- c("p(theta > 0)", "N.Modes", "Kurtosis",
"Skewness", "Burn-In", "IAT", "ISM", "AR")
options(warn=-1)
for (i in 1:ncol(Posterior)) {
Summ[i,1] <- mean(Posterior[,i] > 0)
Summ[i,2] <- length(Modes(Posterior[,i])[[1]])
Summ[i,3] <- round(kurtosis(Posterior[,i]),3)
Summ[i,4] <- round(skewness(Posterior[,i]),3)
Summ[i,5] <- 0
Summ[i,6] <- round(IAT(Posterior[,i]),3)}
Summ[,7] <- NA
Summ[,8] <- 1
options(warn=0)
}
if(identical(class(x), "pmc")) {
### Posterior and Monitors
post <- cbind(x\$Posterior2, x\$Monitor)
colnames(post) <- c(colnames(x\$Posterior2), colnames(x\$Monitor))
### Selecting Parms
if(is.null(Parms)) {keepcols <- 1:ncol(post)}
else {
Parms <- sub("\\[","\\\\[",Parms)
Parms <- sub("\\]","\\\\]",Parms)
Parms <- sub("\\.","\\\\.",Parms)
if(length(grep(Parms[1], colnames(post))) == 0)
stop("Parameter in Parms does not exist.")
keepcols <- grep(Parms[1], colnames(post))
if(length(Parms) > 1) {
for (i in 2:length(Parms)) {
if(length(grep(Parms[i], colnames(post))) == 0)
stop("Parameter in Parms does not exist.")
keepcols <- c(keepcols,
grep(Parms[i], colnames(post)))}}}
temp <- colnames(post)[keepcols]
post <- post[,keepcols]
colnames(post) <- temp
### Correlation Table
options(warn=-1); postcor <- cor(post); options(warn=0)
### Summary Table
Summ <- matrix(NA, ncol(post), 8)
rownames(Summ) <- colnames(post)
colnames(Summ) <- c("p(theta > 0)", "N.Modes", "Kurtosis",
"Skewness", "Burn-In", "IAT", "ISM", "AR")
options(warn=-1)
for (i in 1:ncol(post)) {
Summ[i,1] <- mean(post[,i] > 0)
Summ[i,2] <- length(Modes(post[,i])[[1]])
Summ[i,3] <- round(kurtosis(post[,i]),3)
Summ[i,4] <- round(skewness(post[,i]),3)
Summ[i,6] <- round(IAT(post[,i]),3)}
Summ[,5] <- rep(1, nrow(Summ))
Summ[,7] <- NA
Summ[,8] <- 1
options(warn=0)
}
### Output
out <- list(Posterior.Correlation=postcor, Posterior.Summary=Summ)
class(out) <- "posteriorchecks"
return(out)
}

#End
```

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LaplacesDemon documentation built on July 1, 2018, 9:02 a.m.