stepPlr (version 0.91)

cv.step.plr: Computes cross-validated deviance or prediction errors for step.plr

Description

This function computes cross-validated deviance or prediction errors for step.plr. The parameters that can be cross-validated are lambda and cp.

Usage

cv.step.plr(x, y, weights = rep(1, length(y)), nfold = 5,
              folds = NULL, lambda = c(1e-4, 1e-2, 1),
              cp = c("aic", "bic"), cv.type=c("deviance", "class"),
              trace = TRUE, ...)

Details

This function computes cross-validated deviance or prediction errors for step.plr. The parameters that can be cross-validated are lambda and cp. If both are input as vectors (of length greater than 1), then a two-dimensional cross-validation is done. If either one is input as a single value, then the cross-validation is done only on the parameter with multiple inputs.

References

Mee Young Park and Trevor Hastie (2006) Penalized Logistic Regression for Detecting Gene Interactions - available at the authors' websites, http://stat.stanford.edu/~mypark or http://stat.stanford.edu/~hastie/pub.htm.

See Also

step.plr

Examples

n <- 100
p <- 5
x <- matrix(sample(seq(3),n*p,replace=TRUE),nrow=n)
y <- sample(c(0,1),n,replace=TRUE)
level <- vector("list",length=p)
for (i in 1:p) level[[i]] <- seq(3)
cvfit1 <- cv.step.plr(x,y,level=level,lambda=c(1e-4,1e-2,1),cp="bic")
cvfit2 <- cv.step.plr(x,y,level=level,lambda=1e-4,cp=c(2,3,4))
cvfit3 <- cv.step.plr(x,y,level=level,lambda=c(1e-4,1e-2,1),cp=c(2,3,4))