R: Control for Conditional Inference Trees

Control ctree Hyper Parameters {party} | R Documentation

## 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 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 = 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`.

[Package party version 1.3-7 Index]