This R package provides a program to conduct group-wise variable
selection and estimation for quantile and robust mean regression. The
group lasso penalty
Currently not available on CRAN. User can install from github with following code.
::install_github("shaobo-li/hrqglas") devtools
library(hrqglas)
<- 200
n<- 30
p<- matrix(rnorm(n*p),n,p)
x0<- cbind(x0, x0^2, x0^3)[,order(rep(1:p,3))]
X<- -2+X[,1]+0.5*X[,2]-X[,3]-0.5*X[,7]+X[,8]-0.2*X[,9]+rt(n,2)
y<- rep(1:p, each=3)
group
# quantile regression
<- hrq_glasso(x=X, y=y, group.index=group, method="quantile", tau=0.3)
fit<- cv.hrq_glasso(x=X, y=y, group.index=group, method="quantile", tau=0.3, loss="check")
fit.cvplot(fit.cv)
# mean regression
<- hrq_glasso(x=X, y=y, group.index=group, method="mean")
fit1<- cv.hrq_glasso(x=X, y=y, group.index=group, method="mean", loss="se")
fit.cv1plot(fit.cv1)
Sherwood, B., & Li, S. (2022). Quantile regression feature selection and estimation with grouped variables using Huber approximation. Statistics and Computing, 32(5), 75. https://doi.org/10.1007/s11222-022-10135-w
Yang, Y. and Zou, H., (2015) A Fast Unified Algorithm for Solving Group-lasso Penalize Learning Problems, Statistics and Computing, 25 1129-1141. https://doi.org/10.1007/s11222-014-9498-5.
Yuan, M. and Lin, Y., (2005) Model Selection and Estimation in Regression with Grouped Variables, , 68 49-67. https://doi.org/10.1111/j.1467-9868.2005.00532.x.