RotationForest: Defines the constructor for the rotationForest module

In mananshah99/rotationforest: An implementation of the "Rotation Forest" algorithm from Rodriguez et al. 2006 in R

Description

Requires the dependent and response values (data frames), the number of predictor variables to use in each rotation, the number of trees to train, and a logical for determining if progress should be printed

Usage

rotationForest(xdf, ydf, npredictor, ntree = 10, verbose = F, ...)

Arguments

xdf	a data frame of X dependent vectors
ydf	a data frame of Y response values
npredictor	the number of predictor variables that are to be used in each rotation
ntree	the number of trees that are to be used to train the ensemble
verbose	a logical, set true for classification output to be printed
...	extra variables to be passed on to the rpart function

Value

an object of class rotationForest

Examples

fpath <- system.file("extdata", "balance-scale.data", package="rotationForest")
data <- read.table(fpath, sep = ",", header = FALSE)
data.dependent <- data[,-1]
data.response <- data[,1]
data.response <- as.factor(data.response)
total <- data.frame(data.response, data.dependent)
groups <- sample(rep(1:10, times = ceiling(nrow(total) / 19)), size = nrow(total), replace = TRUE)
data.train <- total[!groups %in% 1,]
data.test <- total[groups %in% 1,]
fit <- rotationForest(data.train[,-1], data.train[,1], 2, 10)

mananshah99/rotationforest documentation built on May 21, 2019, 11:23 a.m.