\name{Memory Allocation}

\alias{ctree_memory}

\title{ Memory Allocation }

\description{

    This function sets up the memory needed for tree growing. It might be 

    convenient to allocate memory only once but build multiple trees.

}

\usage{

ctree_memory(object, MPinv = FALSE)

}

\arguments{

  \item{object}{an object of class \code{LearningSample}.}

  \item{MPinv}{a logical indicating whether memory for the Moore-Penrose

               inverse of covariance matrices should be allocated. }

}

\details{

  This function is normally not to be called by users. However, for

  performance reasons it might be nice to allocate memory and re-fit trees

  using the same memory for the computations. Below is an example.

}

\value{

  An object of class \code{TreeFitMemory}.

}

\examples{

    set.seed(290875)

    ### setup learning sample

    airq <- subset(airquality, !is.na(Ozone))

    ls <- dpp(conditionalTree, Ozone ~ ., data = airq)

    ### setup memory and controls 

    mem <- ctree_memory(ls)

    ct <- ctree_control(teststat = "max")

    ### fit 50 trees on bootstrap samples

    bs <- rmultinom(50, nrow(airq), rep(1, nrow(airq))/nrow(airq))

    storage.mode(bs) <- "double"

    cfit <- conditionalTree@fit

    ens <- apply(bs, 2, function(w) cfit(ls, ct, weights = w, 

                                         fitmem = mem))

}

\keyword{misc}