PrInDT (version 2.0.1)

PrInDTMulevAll: Conditional inference tree (ctree) for multiple classes on all observations

Description

ctree for more than 2 classes on all observations. Interpretability is checked (see 'ctestv'). The parameters 'conf.level', 'minsplit', and 'minbucket' can be used to control the size of the trees.

Usage

PrInDTMulevAll(datain,classname,ctestv=NA,conf.level=0.95,minsplit=NA,minbucket=NA)

Value

treeall

ctree based on all observations

baAll

balanced accuracy of 'treeall'

interpAll

criterion of interpretability of 'treeall' (TRUE / FALSE)

confAll

confusion matrix of 'treeall'

Arguments

datain

Input data frame with class factor variable 'classname' and the
influential variables, which need to be factors or numericals (transform logicals and character variables to factors)

classname

Name of class variable (character)

ctestv

Vector of character strings of forbidden split results;
(see function PrInDT for details.)
If no restrictions exist, the default = NA is used.

conf.level

(1 - significance level) in function ctree (numerical, > 0 and <= 1)
(default = 0.95)

minsplit

Minimum number of elements in a node to be splitted;
default = 20

minbucket

Minimum number of elements in a node;
default = 7

Details

Standard output can be produced by means of print(name) or just name as well as plot(name) where 'name' is the output data frame of the function.

Examples

datastrat <- PrInDT::data_zero
data <- na.omit(datastrat)
ctestv <- rbind('ETH == {C2a,C1a}', 'MLU == {1, 3}')
data$rel[data$ETH %in% c("C1a","C1b","C1c") & data$real == "zero"] <- "zero1"
data$rel[data$ETH %in% c("C2a","C2b","C2c") & data$real == "zero"] <- "zero2"
data$rel[data$real == "realized"] <- "real"
data$rel <- as.factor(data$rel) # rel is new class variable
data$real <- NULL # remove old class variable
conf.level <- 0.99 # 1 - significance level (mincriterion) in ctree
outAll <- PrInDTMulevAll(data,"rel",ctestv,conf.level) 
outAll # print model based on all observations
plot(outAll)