Nothing
### A function to compute the log (unormalized) posterior probabilities for all models
### in the "deleted" set for the current model.
delvar <- function(model, x, xty, lam, w, D, xbar) {
n <- nrow(x)
p <- ncol(x)
p0 <- length(model)
yty <- n-1
logw <- log(w/(1-w))
logp <- numeric(p)
if (p0 == 1) {
logp.del <- -(n-1)/2*log(yty)
} else {
logp.del <- numeric(p0)
RSS.del <- numeric(p0)
x0 <- scale(x[, model, drop=F])
xgx <- crossprod(x0) + lam*diag(p0)
for (j in 1:p0) {
# delete one variable in the current model
model.temp <- model[-j]
R0 <- chol(xgx[-j, -j])
logdetR0 <- sum(log(diag(R0)))
if(is.nan(logdetR0)) logdetR0 = Inf
RSS0 <- yty - sum(backsolve(R0, xty[model.temp], transpose = T)^2)
if(RSS0 <= 0) RSS0 = .Machine$double.eps
logp.del[j] <- 0.5*(p0-1)*log(lam) - logdetR0 - 0.5*(n-1)*log(RSS0) + (p0-1)*logw
RSS.del[j] <- RSS0
}
}
logp[model] <- logp.del
logp[-model] <- -Inf
RSS.del[model] <- RSS.del
RSS.del[-model] <- -Inf
return(list(logp=logp, RSS=RSS.del))
}
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.