Conditional Trees



Conditional Trees

Recursive partitioning for continuous, censored, ordered, nominal and multivariate response variables in a conditional inference framework.

ctree(formula, data, subset = NULL, weights = NULL, 
      controls = ctree_control(), xtrafo = NULL, ytrafo = NULL, 
      scores = NULL)
a symbolic description of the model to be fit.
an data frame containing the variables in the model.
an optional vector specifying a subset of observations to be used in the fitting process.
an optional vector of weights to be used in the fitting process. Only non-negative integer valued weights are allowed.
an object of class TreeControl, which can be obtained using ctree_control.
an optional function to be applied to all input variables.
an optional function to be applied to all response variables.
an optional named list of scores to be attached to ordered factors.

Conditional trees estimate a regression relationship by binary recursive partitioning in a conditional inference framework. Roughly, the algorithm works as follows: 1) Test the global null hypothesis of independence between any of the input variables and the response (which may be multivariate as well). Stop if this hypothesis cannot be rejected. Otherwise select the input variable with strongest association to the resonse. This association is measured by a p-value corresponding to a test for the partial null hypothesis of a single input variable and the response. 2) Implement a binary split in the selected input variable. 3) Recursively repeate steps 1) and 2).

The implementation utilizes a unified framework for conditional inference, or permutation tests, developed by Strasser and Weber (1999). The stop criterion in step 1) is either based on a p-value (teststattype = "Bonferroni" or teststattype = "MonteCarlo" in ctree_control) or on the raw (standardized) test statistic (teststattype = "Raw"). In both cases, the criterion is maximized, i.e., 1 - p-value is used. A split is implemented when the criterion exceeds the value given by mincriterion as specified in ctree_control. For example, when mincriterion = 0.95, the p-value must be smaller than $0.05$ in order to split this node. This statistical approach ensures that the right sized tree is grown and no form of pruning or cross-validation or whatsoever is needed. The selection of the input variable to split in is based on the univariate p-values avoiding a variable selection bias towards input variables with many possible cutpoints.

By default, the scores for each ordinal factor x are 1:length(x), this may be changed using scores = list(x = c(1,5,6)), for example.

For a general description of the methodology see Hothorn, Hornik and Zeileis (2004).


  • An object of class BinaryTree.


Torsten Hothorn, Kurt Hornik and Achim Zeileis (2004). Unbiased Recursive Partitioning: A Conditional Inference Framework. Technical Report Nr. 8, Research Report Series / Department of Statistics and Mathematics, WU Wien.

Helmut Strasser and Christian Weber (1999). On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics, 8, 220--250.

  • ctree
  • conditionalTree
### regression
    airq <- subset(airquality, !
    airct <- ctree(Ozone ~ ., data = airq, 
                   controls = ctree_control(maxsurrogate = 3))
    mean((airq$Ozone - predict(airct))^2)

    ### classification
    irisct <- ctree(Species ~ .,data = iris)
    table(predict(irisct), iris$Species)

    ### estimated class probabilities, a list
    tr <- treeresponse(irisct, newdata = iris[1:10,])

    ### ordinal regression
    mammoct <- ctree(ME ~ ., data = mammoexp) 

    ### estimated class probabilities
    treeresponse(mammoct, newdata = mammoexp[1:10,])

    ### survival analysis
    if (require(ipred)) {
        data(GBSG2, package = "ipred")
        GBSG2ct <- ctree(Surv(time, cens) ~ .,data = GBSG2)
        treeresponse(GBSG2ct, newdata = GBSG2[1:2,])        
Documentation reproduced from package party, version 0.2-1, License: GPL

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