Prediction and Interpretation in Decision Trees for Classification and Regression

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Documentation for package ‘PrInDT’ version 2.0.0

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C2SPrInDT Two-stage estimation for classification
data_land Landscape analysis
data_speaker Subject pronouns and a predictor with one very frequent level
data_vowel Vowel length
data_zero Subject pronouns
Mix2SPrInDT Two-stage estimation for classification-regression mixtures
NesPrInDT Nested 'PrInDT' with additional undersampling of a factor with two unbalanced levels
OptPrInDT Optimisation of undersampling percentages for classification
participant_zero Participants of subject pronoun study
PostPrInDT Posterior analysis of conditional inference trees: distribution of a specified variable in the terminal nodes.
PrInDT The basic undersampling loop for classification
PrInDTAll Conditional inference tree (ctree) based on all observations
PrInDTAllparts Conditional inference trees (ctrees) based on consecutive parts of the full sample
PrInDTCstruc Structured subsampling for classification
PrInDTMulab Multiple label classification based on resampling by 'PrInDT'
PrInDTMulabAll Multiple label classification based on all observations
PrInDTMulev PrInDT analysis for a classification problem with multiple classes.
PrInDTMulevAll Conditional inference tree (ctree) for multiple classes on all observations
PrInDTreg Regression tree resampling by the PrInDT method
PrInDTregAll Regression tree based on all observations
PrInDTRstruc Structured subsampling for regression
R2SPrInDT Two-stage estimation for regression
RePrInDT Repeated 'PrInDT' for specified percentage combinations
SimCPrInDT Interdependent estimation for classification
SimMixPrInDT Interdependent estimation for classification-regression mixtures
SimRPrInDT Interdependent estimation for regression