# PrInDT analysis for a classification problem with multiple classes. PrInDT analysis for a classification problem with more than 2 classes. For each combination of one class vs. the other classes a 2-class `PrInDT` analysis is carried out. The percentages for undersampling of the larger class ('percl' in `PrInDT`) are chosen so that the resulting sizes are comparable with the size of the smaller classes for which all their observations are used in undersampling ('percs' = 1 in `PrInDT`). The class with the highest probability in the K (= number of classes) analyses is chosen for prediction. Interpretability is checked (see 'ctestv'). ```r PrInDTMulev(datain, classname, ctestv=NA, N, conf.level=0.95) ``` ## 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. - `N`: Number of repetitions (integer > 0) - `conf.level`: (1 - significance level) in function `ctree` (numerical, > 0 and <= 1) (default = 0.95) ## Returns - **class**: levels of class variable - **trees**: trees for the levels of the class variable; refer to an individual tree as `trees[[k]]`, k = 1, ..., no. of levels - **ba**: balanced accuracy of combined predictions - **conf**: confusion matrix of combined predictions - **ninterp**: no. of non-interpretable trees ## 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. The plot function will produce a series of more than one plot. If you use R, you might want to specify `windows(record=TRUE)` before `plot(name)` to save the whole series of plots. In R-Studio this functionality is provided automatically. ## Examples ```r datastrat <- PrInDT::data_zero data <- na.omit(datastrat) ctestv <- NA 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 N <- 51 conf.level <- 0.99 # 1 - significance level (mincriterion) in ctree out <- PrInDTMulev(data,"rel",ctestv,N,conf.level) out # print best models based on subsamples plot(out) # corresponding plots ```

PrInDTMulev function