%% $Id: mvrCv.Rd 142 2007-10-01 13:10:25Z bhm $\encoding{latin1}\name{mvrCv}\alias{mvrCv}\title{Cross-validation}\description{  Performs the cross-validation calculations for \code{mvr}.}\usage{mvrCv(X, Y, ncomp,      method = pls.options()$mvralg, scale = FALSE,      segments = 10, segment.type = c("random", "consecutive", "interleaved"),      length.seg, jackknife = FALSE, trace = FALSE, \dots)}\arguments{  \item{X}{a matrix of observations.  \code{NA}s and \code{Inf}s are not    allowed.}  \item{Y}{a vector or matrix of responses.  \code{NA}s and \code{Inf}s    are not allowed.}  \item{ncomp}{the number of components to be used in the    modelling.}  \item{method}{the multivariate regression method to be used.}  \item{scale}{logical.  If \code{TRUE}, the learning \eqn{X} data for each    segment is scaled by dividing each variable by its sample standard    deviation.  The prediction data is scaled by the same amount.}  \item{segments}{the number of segments to use, or a list with segments    (see below).}  \item{segment.type}{the type of segments to use.  Ignored if    \code{segments} is a list.}  \item{length.seg}{Positive integer.  The length of the segments to    use.  If specified, it overrides \code{segments} unless    \code{segments} is a list.}  \item{jackknife}{logical.  Whether jackknifing of regression    coefficients should be performed.}  \item{trace}{logical; if \code{TRUE}, the segment number is printed    for each segment.}  \item{\dots}{additional arguments, sent to the underlying fit function.}}\details{  This function is not meant to be called directly, but through  the generic functions \code{pcr}, \code{plsr} or \code{mvr} with the  argument \code{validation} set to \code{"CV"} or \code{"LOO"}.  All  arguments to \code{mvrCv} can be specified in the generic function call.  If \code{segments} is a list, the arguments \code{segment.type} and  \code{length.seg} are ignored.  The elements of the list should be  integer vectors specifying the indices of the segments.  See  \code{\link{cvsegments}} for details.  Otherwise, segments of type \code{segment.type} are generated.  How  many segments to generate is selected by specifying the number of  segments in \code{segments}, or giving the segment length in  \code{length.seg}.  If both are specified, \code{segments} is  ignored.  If \code{jackknife} is \code{TRUE}, jackknifed regression coefficients  are returned, which can be used for for variance estimation  (\code{\link{var.jack}}) or hypothesis testing (\code{\link{jack.test}}).  \code{X} and \code{Y} do not need to be centered.    Note that this function cannot be used in situations where \eqn{X}  needs to be recalculated for each segment (except for scaling by the  standard deviation), for instance with  \code{msc} or other preprocessing.  For such models, use the more  general (but slower) function \code{\link{crossval}}.  Also note that if needed, the function will silently(!) reduce  \code{ncomp} to the maximal number of components that can be  cross-validated, which is \eqn{n - l -  1}, where \eqn{n} is the  number of observations and \eqn{l} is the length of the longest  segment.  The (possibly reduced) number of components is returned as  the component \code{ncomp}.}\value{  A list with the following components:  \item{method}{equals \code{"CV"} for cross-validation.}  \item{pred}{an array with the cross-validated predictions.}  \item{coefficients}{(only if \code{jackknife} is \code{TRUE}) an array    with the jackknifed regression coefficients.  The dimensions    correspond to the predictors, responses, number of components, and    segments, respectively.}  \item{PRESS0}{a vector of PRESS values (one for each response variable)    for a model with zero components, i.e., only the intercept.}  \item{PRESS}{a matrix of PRESS values for models with 1, \ldots,    \code{ncomp} components.  Each row corresponds to one response variable.}  \item{adj}{a matrix of adjustment values for calculating bias    corrected MSEP.  \code{MSEP} uses this.}  \item{segments}{the list of segments used in the cross-validation.}  \item{ncomp}{the actual number of components used.}}\references{  Mevik, B.-H., Cederkvist, H. R. (2004) Mean Squared Error of  Prediction (MSEP) Estimates for Principal Component Regression (PCR)  and Partial Least Squares Regression (PLSR).  \emph{Journal of Chemometrics}, \bold{18}(9), 422--429.}\author{Ron Wehrens and Bj鴕n-Helge Mevik}\note{  The \code{PRESS0} is always cross-validated using leave-one-out  cross-validation.  This usually makes little difference in practice,  but should be fixed for correctness.  The current implementation of the jackknife stores all  jackknife-replicates of the regression coefficients, which can be very  costly for large matrices.  This might change in a future version.}\seealso{  \code{\link{mvr}}  \code{\link{crossval}}  \code{\link{cvsegments}}  \code{\link{MSEP}}  \code{\link{var.jack}}  \code{\link{jack.test}}}\examples{data(yarn)yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV", segments = 10)\dontrun{plot(MSEP(yarn.pcr))}}\keyword{regression}\keyword{multivariate}