Control Forest Hyper Parameters {party} | R Documentation

## Control for Conditional Tree Forests

### Description

Various parameters that control aspects of the `cforest' fit.

### Usage

    cforest_control(teststat = "max",
                    testtype = "Teststatistic",
                    mincriterion = qnorm(0.9),
                    savesplitstats = FALSE,
                    ntree = 500, mtry = 5, replace = TRUE,
                    fraction = 0.632, ...)

### 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 - p-value that must be exceeded in order to implement a split.
`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.
`ntree` | number of trees to grow in a forest.
`replace` | a logical indicating whether sampling of observations is done with or without replacement.
`fraction` | fraction of number of observations to draw without replacement (`replace = TRUE`).
`...` | additional arguments to be passed to `ctree_control`.

### 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 those length is determined by the number of observations in the learning sample and thus much more memory is required.

### Value

An object of class `ForestControl-class`.

[Package party version 0.8-6 Index]