%% $Id: crossval.Rd 142 2007-10-01 13:10:25Z bhm $\encoding{latin1}\name{crossval}\alias{crossval}\title{Cross-validation of PLSR and PCR models}\description{  A \dQuote{stand alone} cross-validation function for \code{mvr} objects.}\usage{crossval(object, segments = 10,         segment.type = c("random", "consecutive", "interleaved"),         length.seg, jackknife = FALSE, trace = 15, \dots)}\arguments{  \item{object}{an \code{mvr} object; the regression to cross-validate.}  \item{segments}{the number of segments to use, or a list with segments    (see below).  Ignored if \code{loo = TRUE}.}  \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}{if \code{TRUE}, tracing is turned on.  If numeric, it    denotes a time limit (in seconds).  If the estimated total time of    the cross-validation exceeds this limit, tracing is turned on.}  \item{\dots}{additional arguments, sent to the underlying fit function.}}\details{  This function performs cross-validation on a model fit by \code{mvr}.  It can handle models such as \code{plsr(y ~ msc(X), \dots)} or other  models where the predictor variables need to be recalculated for each  segment.  When recalculation is not needed, the result of  \code{crossval(mvr(\dots))} is identical to \code{mvr(\dots,    validation = "CV")}, but slower.  Note that to use \code{crossval}, the data \emph{must} be specified  with a \code{data} argument when fitting \code{object}.  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}}).  When tracing is turned on, the segment number is printed for each segment.}\value{  The supplied \code{object} is returned, with an additional component  \code{validation}, which is a list with components  \item{method}{euqals \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 number of components.}}\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{mvrCv}}  \code{\link{cvsegments}}  \code{\link{MSEP}}  \code{\link{var.jack}}  \code{\link{jack.test}}}\examples{data(yarn)yarn.pcr <- pcr(density ~ msc(NIR), 6, data = yarn)yarn.cv <- crossval(yarn.pcr, segments = 10)\dontrun{plot(MSEP(yarn.cv))}}\keyword{regression}\keyword{multivariate}