README.md

In statcodes/RaSE: Random Subspace Ensemble Classification and Variable Screening

RaSEn

R Package for Random Subspace Ensemble

Installation Instructions

To install the package, please first install the devtools packages, and run the following code in R.

library(devtools)
install_github("yangfengstat/RaSEn")

Multi RaSE

Generate Data

set.seed(0, kind = "L'Ecuyer-CMRG")
train.data <- RaModel("multi_classification", model.no = 1, n = 100, p = 50, p0 = rep(1/4,4))
test.data <- RaModel("multi_classification", model.no = 1, n = 100, p = 50, p0 = rep(1/4,4))
xtrain <- train.data$x
colnames(xtrain) <- paste0("V",1:dim(xtrain)[2])
ytrain <- train.data$y
xtest <- test.data$x
colnames(xtest) <- paste0("V",1:dim(xtest)[2])
ytest <- test.data$y

Run mRaSE classifier with LDA base classifier and no iteration

fit <- RaSE(xtrain, ytrain, B1 = 20, B2 = 50, iteration = 0, base = 'lda', cores = 1)
mean(predict(fit, xtest) != ytest)

Run mRaSE classifier with LDA base classifier and one iteration

fit <- RaSE(xtrain, ytrain, B1 = 20, B2 = 50, iteration = 1, base = 'lda', cores = 6)
mean(predict(fit, xtest) != ytest)

Super Multi RaSE

Fit a Super Multi RaSE classifier by sampling base learner from kNN, LDA and logistic regression with equal probability

fit <- RaSE(xtrain, ytrain, B1 = 20, B2 = 50, base = c("knn", "lda", "logistic"), iteration = 1, cores = 6)
mean(predict(fit, xtest) != ytest)

statcodes/RaSE documentation built on April 21, 2024, 6:01 p.m.