Prediction and Interpretation in Decision Trees for Classification and Regression

Documentation for package ‘PrInDT’ version 1.0.1

data_land	Landscape analysis
data_speaker	Subject pronouns and a predictor with one very frequent level
data_vowel	Vowel length
data_zero	Subject pronouns
FindSubstr	Check for forbidden split results in trees
NesPrInDT	Nested 'PrInDT' with additional undersampling of a factor with two unbalanced levels
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
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
RePrInDT	Repeated 'PrInDT' for specified percentage combinations