Nothing
## This method uses flat prior for beta, 1/sigmasq prior for sigmasq
bayesregressB1S2 <- function(xtx,xtx.inv,xty,yty, Tsamp.out,
sigmasq.init,numsamp.data)
{
# define vectors and matrices
ytx<-t(xty)
n <- numsamp.data
Tsamp.out <- Tsamp.out+1
# num.predictors <- dim(xtx)[1]
betahat <- matrix(NA, nrow=Tsamp.out, ncol = dim(xtx)[1])
sigmasqhat <- rep(NA,Tsamp.out)
# set starting value for sigmasqhat
sigmasqhat[1] <- sigmasq.init
# posterior mean of betahat
betahat.mean <- xtx.inv%*%(xty)
# posterior variance of betahat
for (i in 2:Tsamp.out){
betahat[i,] <- rmvn(n=1, mu=betahat.mean, sigmasqhat[i-1]*xtx.inv)
# simulate sigmasqhat
sigmasqscale.pre <- (yty - t(betahat[i,]) %*% xty -
ytx %*% betahat[i,] + t(betahat[i,]) %*% xtx %*% betahat[i,])
sigmasqhat[i] <- 1/rgamma(1,shape=n/2,scale=(sigmasqscale.pre/2)^(-1))
} # end i
# remove starting value for sigmasqhat and NA for starting value of betahat
betahat <- betahat[-1,]
sigmasqhat <- sigmasqhat[-1]
return(list("beta"=betahat,"sigmasq"=sigmasqhat))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.