Nothing

```
# ----------------------
# Author: Andreas Alfons
# KU Leuven
# ----------------------
# Model selection criteria based on prediction loss
#
# Determine the best model based on prediction loss.
#
# \code{selectMin} selects the model with the smallest prediction loss.
#
# \code{selectHastie} is useful for nested models or for models where a
# tuning parameter controls the complexity of the model (e.g., penalized
# regression). It selects the most parsimonious model whose prediction error
# is no larger than \code{seFactor} standard errors above the prediction error
# of the best model. In particular a one-standard-error rule is frequently
# applied.
#
# @rdname selectBest
# @name selectBest
#
# @param x a numeric vector containing the estimated prediction errors for
# the models.
# @param se a numeric vector containing the estimated standard errors of the
# prediction loss for the models.
# @param seFactor a numeric value giving the multiplication factor of the
# standard error.
#
# @return An integer giving the index of the best model.
#
# @note These functions are designed to be supplied to \code{\link{cvSelect}}
# or \code{\link{cvTuning}}.
#
# @author Andreas Alfons
#
# @references
# Hastie, T., Tibshirani, R. and Friedman, J. (2009) \emph{The Elements of
# Statistical Learning: Data Mining, Inference, and Prediction}. Springer,
# 2nd edition.
#
# @seealso \code{\link{cvSelect}}, \code{\link{cvTuning}}
#
# @keywords utilities
#
# NULL
#
# @rdname selectBest
# @export
selectMin <- function(x) which.min(x)
# @rdname selectBest
# @export
selectHastie <- function(x, se, seFactor = 1) {
i <- which.min(x)
within <- which(x[seq_len(i)] < (x[i] + seFactor * se[i]))
if(length(within) == 0) i else within[1] # ensure it works if se[i] is NA
}
#selectDiff <- function(x, se, seFactor = 1) {
# i <- which.min(x)
# seqI <- seq_len(i)
# out <- which(c(TRUE, (diff(x[seqI]) + seFactor * se[seqI[-1]]) < 0))
# tail(out, 1)
#}
#
#selectRelChange <- function(x, se, seFactor = 1, threshold = 0.001) {
# # find models with large enough relative change
# i <- which.min(x)
# xSeqI <- x[seq_len(i)]
# keep <- which((xSeqI - x[i]) / max(xSeqI) > threshold)
# # call selectHastie() for the remaining models
# selectHastie(xSeqI[keep], se[keep], seFactor=seFactor)
#}
```

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.