\HeaderA{gbmCMA}{Tree-based Gradient Boosting}{gbmCMA}
\keyword{multivariate}{gbmCMA}
\begin{Description}\relax
Roughly speaking, Boosting combines 'weak learners'
in a weighted manner in a stronger ensemble. This
method calls the function \code{gbm.fit} from the
package \code{gbm}. The 'weak learners' are
simple trees that need only very few splits (default: 1).

For \code{S4} method information, see \code{\LinkA{gbmCMA-methods}{gbmCMA.Rdash.methods}}.
\end{Description}
\begin{Usage}
\begin{verbatim}
gbmCMA(X, y, f, learnind, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{X}] Gene expression data. Can be one of the following:
\Itemize{
\item A \code{matrix}. Rows correspond to observations, columns to variables.
\item A \code{data.frame}, when \code{f} is \emph{not} missing (s. below).
\item An object of class \code{ExpressionSet}.
}

\item[\code{y}] Class labels. Can be one of the following:
\Itemize{
\item A \code{numeric} vector.
\item A \code{factor}.
\item A \code{character} if \code{X} is an \code{ExpressionSet} that
specifies the phenotype variable.
\item \code{missing}, if \code{X} is a \code{data.frame} and a
proper formula \code{f} is provided.
}
\bold{WARNING}: The class labels will be re-coded to
range from \code{0} to \code{K-1}, where \code{K} is the
total number of different classes in the learning set.

\item[\code{f}] A two-sided formula, if \code{X} is a \code{data.frame}. The
left part correspond to class labels, the right to variables.
\item[\code{learnind}] An index vector specifying the observations that
belong to the learning set. May be \code{missing};
in that case, the learning set consists of all
observations and predictions are made on the
learning set.
\item[\code{...}] Further arguments passed to the function \code{gbm.fit}
from the package of the same name. Worth mentionning are
\describe{
\item[\code{ntrees}] Number of trees to fit (size of the ensemble),
defaults to 100. This parameter should
be optimized using \code{\LinkA{tune}{tune}}.
\item[\code{shrinkage}] The learning rate (default is
0.001). Usually fixed to a very low value.
\item[\code{distribution}] Loss function to be used. Default is \code{"bernoulli"},
i.e. \code{LogitBoost}, a (less robust) alternative
is \code{"adaboost"}.
\item[\code{interaction.depth}] Number of splits used by the 'weak learner'
(single decision tree). Default is \code{1}.
}

\end{ldescription}
\end{Arguments}
\begin{Value}
An onject of class \code{\LinkA{cloutput}{cloutput}}.
\end{Value}
\begin{Note}\relax
Up to now, this method can only be applied to binary classification.
\end{Note}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{References}\relax
Ridgeway, G. (1999).

The state of boosting.

\emph{Computing Science and Statistics, 31:172-181}

Friedman, J. (2001).

Greedy Function Approximation: A Gradient Boosting Machine.

\emph{Annals of Statistics 29(5):1189-1232}.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{compBoostCMA}{compBoostCMA}}, \code{\LinkA{dldaCMA}{dldaCMA}}, \code{\LinkA{ElasticNetCMA}{ElasticNetCMA}},
\code{\LinkA{fdaCMA}{fdaCMA}}, \code{\LinkA{flexdaCMA}{flexdaCMA}},
\code{\LinkA{knnCMA}{knnCMA}}, \code{\LinkA{ldaCMA}{ldaCMA}}, \code{\LinkA{LassoCMA}{LassoCMA}},
\code{\LinkA{nnetCMA}{nnetCMA}}, \code{\LinkA{pknnCMA}{pknnCMA}}, \code{\LinkA{plrCMA}{plrCMA}},
\code{\LinkA{pls\_ldaCMA}{pls.Rul.ldaCMA}}, \code{\LinkA{pls\_lrCMA}{pls.Rul.lrCMA}}, \code{\LinkA{pls\_rfCMA}{pls.Rul.rfCMA}},
\code{\LinkA{pnnCMA}{pnnCMA}}, \code{\LinkA{qdaCMA}{qdaCMA}}, \code{\LinkA{rfCMA}{rfCMA}},
\code{\LinkA{scdaCMA}{scdaCMA}}, \code{\LinkA{shrinkldaCMA}{shrinkldaCMA}}, \code{\LinkA{svmCMA}{svmCMA}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run tree-based gradient boosting (no tuning)
gbmresult <- gbmCMA(X=golubX, y=golubY, learnind=learnind, n.trees = 500)
show(gbmresult)
ftable(gbmresult)
plot(gbmresult)
\end{ExampleCode}
\end{Examples}

