R: Control for Conditional Tree Forests

Control Forest Hyper Parameters {party} | R Documentation

## Control for Conditional Tree Forests

### Description

Various parameters that control aspects of the ‘cforest’ fit via its ‘control’ argument.

### Usage

    cforest_unbiased(...)
    cforest_classical(...)
    cforest_control(teststat = "max",
                    testtype = "Teststatistic",
                    mincriterion = qnorm(0.9),
                    savesplitstats = FALSE,
                    ntree = 500, mtry = 5, replace = TRUE,
                    fraction = 0.632, trace = 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.
`mtry` | number of input variables randomly sampled as candidates at each node for random forest like algorithms. Bagging, as special case of a random forest without random input variable sampling, can be performed by setting `mtry` either equal to `NULL` or manually equal to the number of input variables.
`savesplitstats` | a logical determining whether 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 (only relevant if `replace = FALSE`).
`trace` | a logical indicating if a progress bar shall be printed while the forest grows.
`...` | additional arguments to be passed to `ctree_control`.

### Details

All three functions return an object of class `ForestControl-class` defining hyper parameters to be specified via the `control` argument of `cforest`.

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 `mtry` argument regulates a random selection of `mtry` input variables in each node. Note that here `mtry` is fixed to the value 5 by default for merely technical reasons, while in `randomForest` the default values for classification and regression vary with the number of input variables. Make sure that `mtry` is defined properly before using `cforest`.

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.

The number of trees `ntree` can be increased for large numbers of input variables.

Function `cforest_unbiased` returns the settings suggested for the construction of unbiased random forests (`teststat = "quad", testtype = "Univ", replace = FALSE`) by Strobl et al. (2007) and is the default since version 0.9-90. Hyper parameter settings mimicing the behaviour of `randomForest` are available in `cforest_classical` which have been used as default up to version 0.9-14.

Please note that `cforest`, in contrast to `randomForest`, doesn't grow trees of maximal depth. To grow large trees, set `mincriterion = 0`.

### Value

An object of class `ForestControl-class`.

### References

Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis and Torsten Hothorn (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics, 8, 25. DOI: 10.1186/1471-2105-8-25

[Package party version 1.3-7 Index]