BRLasso {elasso}

Bootstrap ranking LASSO model.

Description

This function performs a LASSO logistic regression model using a bootstrap ranking procedure.

Usage

BRLasso(x, y, B = 5, Boots = 100, kfold = 10)

Arguments

x

the predictor matrix

y

the response variable, a factor object with values of 0 and 1

B

the external loop for intersection operation, with the default value 5

Boots

the internal loop for bootstrap sampling, with the default value 100

kfold

the K-fold cross validation, with the default value 10

References

Guo, P., Zeng, F., Hu, X., Zhang, D., Zhu, S., Deng, Y., & Hao, Y. (2015). Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents. PLoS One, 27;10(7):e0134151.

Examples

library(datasets)
head(iris)
X <- as.matrix(subset(iris,iris$Species!="setosa")[,-5])
Y <- as.factor(ifelse(subset(iris,iris$Species!="setosa")[,5]=='versicolor',0,1))
# Fitting a bootstrap ranking LASSO (BRLASSO) logistic regression model
BRLasso.fit <- BRLasso(x=X, y=Y, B=2, Boots=10, kfold=10)
# Variables selected by the BRLASSO model
BRLasso.fit$var.selected
# Coefficients of the selected variables
BRLasso.fit$var.coef