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\name{gausspr-class}
\docType{class}
\alias{gausspr-class}
\alias{alpha,gausspr-method}
\alias{cross,gausspr-method}
\alias{error,gausspr-method}
\alias{kcall,gausspr-method}
\alias{kernelf,gausspr-method}
\alias{kpar,gausspr-method}
\alias{lev,gausspr-method}
\alias{type,gausspr-method}
\alias{alphaindex,gausspr-method}
\alias{xmatrix,gausspr-method}
\alias{ymatrix,gausspr-method}
\alias{scaling,gausspr-method}
\title{Class "gausspr"}
\description{The Gaussian Processes object class}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("gausspr", ...)}.
or by calling the \code{gausspr} function
}
\section{Slots}{
\describe{
\item{\code{tol}:}{Object of class \code{"numeric"} contains
tolerance of termination criteria}
\item{\code{kernelf}:}{Object of class \code{"kfunction"} contains
the kernel function used}
\item{\code{kpar}:}{Object of class \code{"list"} contains the
kernel parameter used }
\item{\code{kcall}:}{Object of class \code{"list"} contains the used
function call }
\item{\code{type}:}{Object of class \code{"character"} contains
type of problem }
\item{\code{terms}:}{Object of class \code{"ANY"} contains the
terms representation of the symbolic model used (when using a formula)}
\item{\code{xmatrix}:}{Object of class \code{"input"} containing
the data matrix used }
\item{\code{ymatrix}:}{Object of class \code{"output"} containing the
response matrix}
\item{\code{fitted}:}{Object of class \code{"output"} containing the
fitted values }
\item{\code{lev}:}{Object of class \code{"vector"} containing the
levels of the response (in case of classification) }
\item{\code{nclass}:}{Object of class \code{"numeric"} containing
the number of classes (in case of classification) }
\item{\code{alpha}:}{Object of class \code{"listI"} containing the
computes alpha values }
\item{\code{alphaindex}}{Object of class \code{"list"} containing
the indexes for the alphas in various classes (in multi-class
problems).}
\item{\code{sol}}{Object of class \code{"matrix"} containing the solution to the Gaussian Process formulation, it is used to compute the variance in regression problems.}
\item{\code{scaling}}{Object of class \code{"ANY"} containing
the scaling coefficients of the data (when case \code{scaled = TRUE} is used).}
\item{\code{nvar}:}{Object of class \code{"numeric"} containing the
computed variance}
\item{\code{error}:}{Object of class \code{"numeric"} containing the
training error}
\item{\code{cross}:}{Object of class \code{"numeric"} containing the
cross validation error}
\item{\code{n.action}:}{Object of class \code{"ANY"} containing the
action performed in NA }
}
}
\section{Methods}{
\describe{
\item{alpha}{\code{signature(object = "gausspr")}: returns the alpha
vector}
\item{cross}{\code{signature(object = "gausspr")}: returns the cross
validation error }
\item{error}{\code{signature(object = "gausspr")}: returns the
training error }
\item{fitted}{\code{signature(object = "vm")}: returns the fitted values }
\item{kcall}{\code{signature(object = "gausspr")}: returns the call performed}
\item{kernelf}{\code{signature(object = "gausspr")}: returns the
kernel function used}
\item{kpar}{\code{signature(object = "gausspr")}: returns the kernel
parameter used}
\item{lev}{\code{signature(object = "gausspr")}: returns the
response levels (in classification) }
\item{type}{\code{signature(object = "gausspr")}: returns the type
of problem}
\item{xmatrix}{\code{signature(object = "gausspr")}: returns the
data matrix used}
\item{ymatrix}{\code{signature(object = "gausspr")}: returns the
response matrix used}
\item{scaling}{\code{signature(object = "gausspr")}: returns the
scaling coefficients of the data (when \code{scaled = TRUE} is used)}
}
}
\author{Alexandros Karatzoglou\cr \email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{
\code{\link{gausspr}},
\code{\link{ksvm-class}},
\code{\link{vm-class}}
}
\examples{
# train model
data(iris)
test <- gausspr(Species~.,data=iris,var=2)
test
alpha(test)
error(test)
lev(test)
}
\keyword{classes}
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