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\name{predict.gausspr}
\alias{predict.gausspr}
\alias{predict,gausspr-method}
\title{predict method for Gaussian Processes object}
\description{Prediction of test data using Gaussian Processes}
\usage{
\S4method{predict}{gausspr}(object, newdata, type = "response", coupler = "minpair")
}
\arguments{
\item{object}{an S4 object of class \code{gausspr} created by the
\code{gausspr} function}
\item{newdata}{a data frame or matrix containing new data}
\item{type}{one of \code{response}, \code{probabilities}
indicating the type of output: predicted values or matrix of class
probabilities}
\item{coupler}{Coupling method used in the multiclass case, can be one
of \code{minpair} or \code{pkpd} (see reference for more details).}
}
\value{
\item{response}{predicted classes (the classes with majority vote)
or the response value in regression.}
\item{probabilities}{matrix of class probabilities (one column for each class and
one row for each input).}
}
\references{
\itemize{
\item
C. K. I. Williams and D. Barber \cr
Bayesian classification with Gaussian processes. \cr
IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998\cr
\url{https://homepages.inf.ed.ac.uk/ckiw/postscript/pami_final.ps.gz}
\item
T.F. Wu, C.J. Lin, R.C. Weng. \cr
\emph{Probability estimates for Multi-class Classification by
Pairwise Coupling}\cr
\url{https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf}
}
}
\author{Alexandros Karatzoglou\cr
\email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\keyword{methods}
\keyword{regression}
\keyword{classif}
\examples{
## example using the promotergene data set
data(promotergene)
## create test and training set
ind <- sample(1:dim(promotergene)[1],20)
genetrain <- promotergene[-ind, ]
genetest <- promotergene[ind, ]
## train a support vector machine
gene <- gausspr(Class~.,data=genetrain,kernel="rbfdot",
kpar=list(sigma=0.015))
gene
## predict gene type probabilities on the test set
genetype <- predict(gene,genetest,type="probabilities")
genetype
}
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