ctree_control: Control for Conditional Inference Trees

In party: A Laboratory for Recursive Partytioning

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, remove_weights = FALSE)

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 (for testtype == "Teststatistic"), or 1 - p-value (for other values of testtype) 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.
stump	a logical determining whether a stump (a tree with three nodes only) is to be computed.
nresample	number of Monte-Carlo replications to use when the distribution of the test statistic is simulated.
maxsurrogate	number of surrogate splits to evaluate. Note that 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 = 0 means that no random selection takes place.
savesplitstats	a logical determining if the process of standardized two-sample statistics for split point estimate is saved for each primary split.
maxdepth	maximum depth of the tree. The default maxdepth = 0 means that no restrictions are applied to tree sizes.
remove_weights	a logical determining if weights attached to nodes shall be removed after fitting the tree.

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 argument nresample is the number of Monte-Carlo 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 two-sample 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.

Value

An object of class TreeControl.

party documentation built on Sept. 11, 2024, 5:18 p.m.