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\name{estim_ncp}
\alias{estim_ncp}
\title{Estimate the number of components in Principal Component Analysis}
\description{
Estimate the number of components in PCA .
}
\usage{
estim_ncp(X, ncp.min=0, ncp.max=NULL, scale=TRUE, method="GCV")
}
\arguments{
\item{X}{a data frame with continuous variables}
\item{ncp.min}{minimum number of dimensions to interpret, by default 0}
\item{ncp.max}{maximum number of dimensions to interpret, by default NULL which corresponds to the number of columns minus 2}
\item{scale}{a boolean, if TRUE (value set by default) then data are scaled to unit variance}
\item{method}{method used to estimate the number of components, "GCV" for the generalized cross-validation approximation or "Smooth" for the smoothing method (by default "GCV")}
}
\value{
Returns ncp the best number of dimensions to use (find the minimum or the first local minimum) and the
mean error for each dimension tested
}
\author{Francois Husson \email{francois.husson@institut-agro.fr}, Julie Josse\email{Julie.Josse@agrocampus-ouest.fr}}
\references{Josse, J. and Husson, F. (2012). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis, 56, 1869-1879.
}
\seealso{ \code{\link{PCA}}}
\examples{
data(decathlon)
nb.dim <- estim_ncp(decathlon[,1:10],scale=TRUE)
}
\keyword{multivariate}
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