\name{kpca-class} \docType{class} \alias{kpca-class} \alias{rotated} \alias{eig,kpca-method} \alias{kcall,kpca-method} \alias{kernelf,kpca-method} \alias{pcv,kpca-method} \alias{rotated,kpca-method} \alias{xmatrix,kpca-method} \title{Class "kpca"} \description{ The Kernel Principal Components Analysis class} \section{Objects of class "kpca"}{ Objects can be created by calls of the form \code{new("kpca", ...)}. or by calling the \code{kpca} function. } \section{Slots}{ \describe{ \item{\code{pcv}:}{Object of class \code{"matrix"} containing the principal component vectors } \item{\code{eig}:}{Object of class \code{"vector"} containing the corresponding eigenvalues} \item{\code{rotated}:}{Object of class \code{"matrix"} containing the projection of the data on the principal components} \item{\code{kernelf}:}{Object of class \code{"function"} containing the kernel function used} \item{\code{kpar}:}{Object of class \code{"list"} containing the kernel parameters used } \item{\code{xmatrix}:}{Object of class \code{"matrix"} containing the data matrix used } \item{\code{kcall}:}{Object of class \code{"ANY"} containing the function call } \item{\code{n.action}:}{Object of class \code{"ANY"} containing the action performed on NA } } } \section{Methods}{ \describe{ \item{eig}{\code{signature(object = "kpca")}: returns the eigenvalues } \item{kcall}{\code{signature(object = "kpca")}: returns the performed call} \item{kernelf}{\code{signature(object = "kpca")}: returns the used kernel function} \item{pcv}{\code{signature(object = "kpca")}: returns the principal component vectors } \item{predict}{\code{signature(object = "kpca")}: embeds new data } \item{rotated}{\code{signature(object = "kpca")}: returns the projected data} \item{xmatrix}{\code{signature(object = "kpca")}: returns the used data matrix } } } \author{Alexandros Karatzoglou\cr \email{alexandros.karatzoglou@ci.tuwien.ac.at}} \seealso{ \code{\link{ksvm-class}}, \code{\link{kcca-class}} } \examples{ # another example using the iris data(iris) test <- sample(1:50,20) kpc <- kpca(~.,data=iris[-test,-5],kernel="rbfdot", kpar=list(sigma=0.2),features=2) #print the principal component vectors pcv(kpc) rotated(kpc) kernelf(kpc) eig(kpc) } \keyword{classes}