\name{Control ctree Hyper Parameters}

\alias{ctree_control}

\title{ Control for Conditional Inference Trees }

\description{

  Various parameters that control aspects of the `ctree' fit.

}

\usage{

ctree_control(teststat = c("quad", "max"), 

              testtype = c("Bonferroni", "MonteCarlo", 

                           "Univariate", "Teststatistic"), 

              mincriterion = 0.95, minsplit = 20, minbucket = 7, 

              stump = FALSE, nresample = 9999, maxsurrogate = 0, 

              mtry = 0, savesplitstats = TRUE, maxdepth = 0)

}

\arguments{

  \item{teststat}{ a character specifying the type of the test statistic

                       to be applied. }

  \item{testtype}{ a character specifying how to compute the distribution of

                   the test statistic. }

  \item{mincriterion}{ the value of the test statistic (for \code{testtype == "Teststatistic"}),

                       or 1 - p-value (for other values of \code{testtype}) that

                       must be exceeded in order to implement a split. }

  \item{minsplit}{ the minimum sum of weights in a node in order to be considered

                   for splitting. }

  \item{minbucket}{ the minimum sum of weights in a terminal node. }

  \item{stump}{ a logical determining whether a stump (a tree with three

                nodes only) is to be computed. }

  \item{nresample}{ number of Monte-Carlo replications to use when the

                    distribution of the test statistic is simulated.}

  \item{maxsurrogate}{ number of surrogate splits to evaluate. Note the

                       currently only surrogate splits in ordered

                       covariables are implemented. }

  \item{mtry}{ number of input variables randomly sampled as candidates 

               at each node for random forest like algorithms. The default

               \code{mtry = 0} means that no random selection takes place.}

  \item{savesplitstats}{ a logical determining if the process of standardized

                         two-sample statistics for split point estimate

                         is saved for each primary split.}

  \item{maxdepth}{ maximum depth of the tree. The default \code{maxdepth = 0}

                   means that no restrictions are applied to tree sizes.}

}

\details{

  The arguments \code{teststat}, \code{testtype} and \code{mincriterion}

  determine how the global null hypothesis of independence between all input

  variables and the response is tested (see \code{\link{ctree}}). The 

  argument \code{nresample} is the number of Monte-Carlo replications to be

  used when \code{testtype = "MonteCarlo"}.

  A split is established when the sum of the weights in both daugther nodes

  is larger than \code{minsplit}, this avoids pathological splits at the

  borders. When \code{stump = TRUE}, a tree with at most two terminal nodes

  is computed.

  The argument \code{mtry > 0} means that a random forest like `variable

  selection', i.e., a random selection of \code{mtry} input variables, is

  performed in each node.

  It might be informative to look at scatterplots of input variables against

  the standardized two-sample split statistics, those are available when

  \code{savesplitstats = TRUE}. Each node is then associated with a vector

  whose length is determined by the number of observations in the learning

  sample and thus much more memory is required.

}

\value{

  An object of class \code{\link{TreeControl}}.

}

\keyword{misc}