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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{cv-indices}
\alias{cv-indices}
\alias{cvfolds}
\alias{cv_ids}
\title{Create cross-validation indices}
\usage{
cvfolds(n, K, seed = NULL)
cv_ids(n, K, out = c("foldwise", "indices"), seed = NULL)
}
\arguments{
\item{n}{Number of data points.}
\item{K}{Number of folds. Must be at least 2 and not exceed \code{n}.}
\item{seed}{Random seed so that the same division could be obtained again if
needed.}
\item{out}{Format of the output, either 'foldwise' (default) or 'indices'.
See below for details.}
}
\value{
\code{cvfolds} returns a vector of length \code{n} such that each
element is an integer between 1 and \code{k} denoting which fold the
corresponding data point belongs to. The returned value of \code{cv_ids}
depends on the \code{out}-argument. If \code{out}='foldwise', the returned
value is a list with \code{k} elements, each having fields \code{tr} and
\code{ts} which give the training and test indices, respectively, for the
corresponding fold. If \code{out}='indices', the returned value is a list
with fields \code{tr} and \code{ts} each of which is a list with \code{k}
elements giving the training and test indices for each fold.
}
\description{
Divide indices from 1 to \code{n} into subsets for \code{k}-fold cross
validation. These functions are potentially useful when creating the
\code{cvfits} and \code{cvfun} arguments for
\link[=init_refmodel]{init_refmodel}. The returned value is different for
these two methods, see below for details.
}
\examples{
\donttest{
### compute sample means within each fold
n <- 100
y <- rnorm(n)
cv <- cv_ids(n, K=5)
cvmeans <- lapply(cv, function(fold) mean(y[fold$tr]))
}
}
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