ctree_control
Control for Conditional Inference Trees
Various parameters that control aspects of the `ctree' fit.
 Keywords
 misc
Usage
ctree_control(teststat = c("quad", "max"), testtype = c("Bonferroni", "Univariate", "Teststatistic"), mincriterion = 0.95, minsplit = 20L, minbucket = 7L, minprob = 0.01, stump = FALSE, maxsurrogate = 0L, mtry = Inf, maxdepth = Inf, multiway = FALSE, splittry = 2L, majority = FALSE, applyfun = NULL, cores = NULL)
Arguments
 teststat
 a character specifying the type of the test statistic to be applied.
 testtype
 a character specifying how to compute the distribution of the test statistic.
 mincriterion
 the value of the test statistic or 1  pvalue that must be exceeded in order to implement a split.
 minsplit
 the minimum sum of weights in a node in order to be considered for splitting.
 minbucket
 the minimum sum of weights in a terminal node.
 minprob
 proportion of observations needed to establish a terminal node.
 stump
 a logical determining whether a stump (a tree with three nodes only) is to be computed.
 maxsurrogate
 number of surrogate splits to evaluate. Note the currently only surrogate splits in ordered covariables are implemented.
 mtry
 number of input variables randomly sampled as candidates
at each node for random forest like algorithms. The default
mtry = Inf
means that no random selection takes place.  maxdepth
 maximum depth of the tree. The default
maxdepth = Inf
means that no restrictions are applied to tree sizes.  multiway
 a logical indicating if multiway splits for all factor levels are implemented for unordered factors.
 splittry
 number of variables that are inspected for admissible splits if the best split doesn't meet the sample size constraints.
 majority
 if
FALSE
, observations which can't be classified to a daughter node because of missing information are randomly assigned (following the node distribution). IfFALSE
, they go with the majority (the default inctree
).  applyfun
 an optional
lapply
style function with argumentsfunction(X, FUN, ...)
. It is used for computing the variable selection criterion. The default is to use the basiclapply
function unless thecores
argument is specified (see below).  cores
 numeric. If set to an integer the
applyfun
is set tomclapply
with the desired number ofcores
.
Details
The arguments teststat
, testtype
and mincriterion
determine how the global null hypothesis of independence between all input
variables and the response is tested (see ctree
).
The variable with most extreme pvalue or test statistic is selected
for splitting. If this isn't possible due to sample size constraints
explained in the next paragraph, up to splittry
other variables
are inspected for possible splits.
A split is established when all of the following criteria are met:
1) the sum of the weights in the current node
is larger than minsplit
, 2) a fraction of the sum of weights of more than
minprob
will be contained in all daughter nodes, 3) the sum of
the weights in all daughter nodes exceeds minbucket
, and 4)
the depth of the tree is smaller than maxdepth
.
This avoids pathological splits deep down the tree.
When stump = TRUE
, a tree with at most two terminal nodes is computed.
The argument mtry > 0
means that a random forest like `variable
selection', i.e., a random selection of mtry
input variables, is
performed in each node.
In each inner node, maxsurrogate
surrogate splits are computed
(regardless of any missing values in the learning sample). Factors
in test samples whose levels were empty in the learning sample
are treated as missing when computing predictions (in contrast
to ctree
. Note also the different behaviour of
majority
in the two implementations.
Value

A list.