| Title: | Enhanced Least Absolute Shrinkage and Selection Operator Regression Model |
| Version: | 1.1 |
| Author: | Pi Guo |
| Maintainer: | Pi Guo <guopi.01@163.com> |
| Description: | Performs some enhanced variable selection algorithms based on the least absolute shrinkage and selection operator for regression model. |
| Depends: | R (≥ 3.0.2),glmnet,SiZer,datasets |
| License: | GPL-2 |
| LazyData: | true |
| Packaged: | 2015-10-06 09:39:48 UTC; Administrator |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Date/Publication: | 2015-10-06 14:04:20 |
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