Various parameters that control aspects of the ‘ctree’ fit.
1 2 3 4 5 6 
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 (for 
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. 
stump 
a logical determining whether a stump (a tree with three nodes only) is to be computed. 
nresample 
number of MonteCarlo replications to use when the distribution of the test statistic is simulated. 
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

savesplitstats 
a logical determining if the process of standardized twosample statistics for split point estimate is saved for each primary split. 
maxdepth 
maximum depth of the tree. The default 
remove_weights 
a logical determining if weights attached to nodes shall be removed after fitting the tree. 
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
argument nresample
is the number of MonteCarlo replications to be
used when testtype = "MonteCarlo"
.
A split is established when the sum of the weights in both daugther nodes
is larger than minsplit
, this avoids pathological splits at the
borders. 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.
It might be informative to look at scatterplots of input variables against
the standardized twosample split statistics, those are available when
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.
An object of class TreeControl
.
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