ctree_control: Control for Conditional Inference Trees

Description Usage Arguments Details Value References

View source: R/ctree.R

Description

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

Usage

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ctree_control(teststat = c("quadratic", "maximum"),
    splitstat = c("quadratic", "maximum"),
    splittest = FALSE,
    testtype = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic"),
    pargs = GenzBretz(),
    nmax = Inf, alpha = 0.05, mincriterion = 1 - alpha,
    logmincriterion = log(mincriterion), minsplit = 20L, minbucket = 7L, 
    minprob = 0.01, stump = FALSE, lookahead = FALSE, nresample = 9999L, 
    MIA = FALSE, maxsurrogate = 0L, numsurrogate = FALSE, mtry = Inf, maxdepth = Inf, 
    multiway = FALSE, splittry = 2L, intersplit = FALSE, majority = FALSE, 
    caseweights = TRUE, applyfun = NULL, cores = NULL, saveinfo = TRUE)

Arguments

teststat

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

splitstat

a character specifying the type of the test statistic to be applied for splitpoint selection. Prior to version 2.0-0, maximum was implemented only.

splittest

a logical changing linear (the default FALSE) to maximally selected statistics for variable selection. Currently needs testtype = "MonteCarlo".

testtype

a character specifying how to compute the distribution of the test statistic. The first three options refer to p-values as criterion, Teststatistic uses the raw statistic as criterion. Bonferroni and Univariate relate to p-values from the asymptotic distribution (adjusted or unadjusted). Bonferroni-adjusted Monte-Carlo p-values are computed when both Bonferroni and MonteCarlo are given.

pargs

control parameters for the computation of multivariate normal probabilities, see GenzBretz.

nmax

an integer defining the number of bins each variable is divided into prior to tree building. The default Inf does not apply any binning. Highly experimental, use at your own risk.

alpha

a double, the significance level for variable selection.

mincriterion

the value of the test statistic or 1 - p-value that must be exceeded in order to implement a split.

logmincriterion

the value of the test statistic or 1 - p-value that must be exceeded in order to implement a split on the log-scale.

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.

minprob

proportion of observations needed to establish a terminal node.

stump

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

lookahead

a logical determining whether a split is implemented only after checking if tests in both daughter nodes can be performed.

nresample

number of permutations for testtype = "MonteCarlo".

MIA

a logical determining the treatment of NA as a category in split, see Twala et al. (2008).

maxsurrogate

number of surrogate splits to evaluate.

numsurrogate

a logical for backward-compatibility with party. If TRUE, only at least ordered variables are considered for surrogate splits.

mtry

number of input variables randomly sampled as candidates at each node for random forest like algorithms. The default mtry = Inf means that no random selection takes place.

maxdepth

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

multiway

a logical indicating if multiway splits for all factor levels are implemented for unordered factors.

splittry

number of variables that are inspected for admissible splits if the best split doesn't meet the sample size constraints.

intersplit

a logical indicating if splits in numeric variables are simply x <= a (the default) or interpolated x <= (a + b) / 2. The latter feature is experimental, see Galili and Meilijson (2016).

majority

if FALSE, observations which can't be classified to a daughter node because of missing information are randomly assigned (following the node distribution). If TRUE, they go with the majority (the default in ctree).

caseweights

a logical interpreting weights as case weights.

applyfun

an optional lapply-style function with arguments function(X, FUN, ...). It is used for computing the variable selection criterion. The default is to use the basic lapply function unless the cores argument is specified (see below).

cores

numeric. If set to an integer the applyfun is set to mclapply with the desired number of cores.

saveinfo

logical. Store information about variable selection procedure in info slot of each partynode.

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 variable with most extreme p-value or test statistic is selected for splitting. If this isn't possible due to sample size constraints explained in the next paragraph, up to splittry other variables are inspected for possible splits.

A split is established when all of the following criteria are met: 1) the sum of the weights in the current node is larger than minsplit, 2) a fraction of the sum of weights of more than minprob will be contained in all daughter nodes, 3) the sum of the weights in all daughter nodes exceeds minbucket, and 4) the depth of the tree is smaller than maxdepth. This avoids pathological splits deep down the tree. 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.

In each inner node, maxsurrogate surrogate splits are computed (regardless of any missing values in the learning sample). Factors in test samples whose levels were empty in the learning sample are treated as missing when computing predictions (in contrast to ctree. Note also the different behaviour of majority in the two implementations.

Value

A list.

References

B. E. T. H. Twala, M. C. Jones, and D. J. Hand (2008), Good Methods for Coping with Missing Data in Decision Trees, Pattern Recognition Letters, 29(7), 950–956.

Tal Galili, Isaac Meilijson (2016), Splitting Matters: How Monotone Transformation of Predictor Variables May Improve the Predictions of Decision Tree Models, https://arxiv.org/abs/1611.04561.


partykit documentation built on May 31, 2017, 3:38 a.m.

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