ctree_control: Control for Conditional Inference Trees

Description Usage Arguments Details Value


Various parameters that control aspects of the ‘ctree’ fit.


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)



a character specifying the type of the test statistic to be applied.


a character specifying how to compute the distribution of the test statistic.


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.


the minimum sum of weights in a node in order to be considered for splitting.


the minimum sum of weights in a terminal node.


a logical determining whether a stump (a tree with three nodes only) is to be computed.


number of Monte-Carlo replications to use when the distribution of the test statistic is simulated.


number of surrogate splits to evaluate. Note the currently only surrogate splits in ordered covariables are implemented.


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.


a logical determining if the process of standardized two-sample statistics for split point estimate is saved for each primary split.


maximum depth of the tree. The default maxdepth = 0 means that no restrictions are applied to tree sizes.


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 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.


An object of class TreeControl.

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