1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
|
\name{rvm-class}
\docType{class}
\alias{rvm-class}
\alias{RVindex}
\alias{mlike}
\alias{nvar}
\alias{RVindex,rvm-method}
\alias{alpha,rvm-method}
\alias{cross,rvm-method}
\alias{error,rvm-method}
\alias{kcall,rvm-method}
\alias{kernelf,rvm-method}
\alias{kpar,rvm-method}
\alias{lev,rvm-method}
\alias{mlike,rvm-method}
\alias{nvar,rvm-method}
\alias{type,rvm-method}
\alias{xmatrix,rvm-method}
\alias{ymatrix,rvm-method}
\title{Class "rvm"}
\description{Relevance Vector Machine Class}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("rvm", ...)}.
or by calling the \code{rvm} function.
}
\section{Slots}{
\describe{
\item{\code{tol}:}{Object of class \code{"numeric"} contains
tolerance of termination criteria used.}
\item{\code{kernelf}:}{Object of class \code{"kfunction"} contains
the kernel function used }
\item{\code{kpar}:}{Object of class \code{"list"} contains the
hyperparameter used}
\item{\code{kcall}:}{Object of class \code{"call"} contains the
function call}
\item{\code{type}:}{Object of class \code{"character"} contains type
of problem}
\item{\code{terms}:}{Object of class \code{"ANY"} containing the
terms representation of the symbolic model used (when using a
formula interface)}
\item{\code{xmatrix}:}{Object of class \code{"matrix"} contains the data
matrix used during computation}
\item{\code{ymatrix}:}{Object of class \code{"output"} contains the
response matrix}
\item{\code{fitted}:}{Object of class \code{"output"} with the fitted
values, (predict on training set).}
\item{\code{lev}:}{Object of class \code{"vector"} contains the
levels of the response (in classification)}
\item{\code{nclass}:}{Object of class \code{"numeric"} contains the
number of classes (in classification)}
\item{\code{alpha}:}{Object of class \code{"listI"} containing the the
resulting alpha vector}
\item{\code{coef}:}{Object of class \code{"ANY"} containing the the
resulting model parameters}
\item{\code{nvar}:}{Object of class \code{"numeric"} containing the
calculated variance (in case of regression)}
\item{\code{mlike}:}{Object of class \code{"numeric"} containing the
computed maximum likelihood}
\item{\code{RVindex}:}{Object of class \code{"vector"} containing
the indexes of the resulting relevance vectors }
\item{\code{nRV}:}{Object of class \code{"numeric"} containing the
number of relevance vectors}
\item{\code{cross}:}{Object of class \code{"numeric"} containing the
resulting cross validation error }
\item{\code{error}:}{Object of class \code{"numeric"} containing the
training error}
\item{\code{n.action}:}{Object of class \code{"ANY"} containing the
action performed on NA}
}
}
\section{Methods}{
\describe{
\item{RVindex}{\code{signature(object = "rvm")}: returns the index
of the relevance vectors }
\item{alpha}{\code{signature(object = "rvm")}: returns the resulting
alpha vector}
\item{cross}{\code{signature(object = "rvm")}: returns the resulting
cross validation error}
\item{error}{\code{signature(object = "rvm")}: returns the training
error }
\item{fitted}{\code{signature(object = "vm")}: returns the fitted values }
\item{kcall}{\code{signature(object = "rvm")}: returns the function call }
\item{kernelf}{\code{signature(object = "rvm")}: returns the used
kernel function }
\item{kpar}{\code{signature(object = "rvm")}: returns the parameters
of the kernel function}
\item{lev}{\code{signature(object = "rvm")}: returns the levels of
the response (in classification)}
\item{mlike}{\code{signature(object = "rvm")}: returns the estimated
maximum likelihood}
\item{nvar}{\code{signature(object = "rvm")}: returns the calculated
variance (in regression)}
\item{type}{\code{signature(object = "rvm")}: returns the type of problem}
\item{xmatrix}{\code{signature(object = "rvm")}: returns the data
matrix used during computation}
\item{ymatrix}{\code{signature(object = "rvm")}: returns the used response }
}
}
\author{Alexandros Karatzoglou\cr \email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{
\code{\link{rvm}},
\code{\link{ksvm-class}}
}
\examples{
# create data
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.05)
# train relevance vector machine
foo <- rvm(x, y)
foo
alpha(foo)
RVindex(foo)
fitted(foo)
kernelf(foo)
nvar(foo)
## show slots
slotNames(foo)
}
\keyword{classes}
|