sglfast is a fork of R package SGL (Simon et. al. 2013), with individual group regularization parameters, and the iterative sparse-group lasso isgl, an algorithm to select the optimal regularization parameters of the sparse-group lasso.
The easiest way to install isgl in R is using devtools.
If devtools is not installed, run
install.packages('devtools')
Then, install isgl
library(devtools)
install_github("jlaria/sglfast")
library(sglfast)
library(sglfast)
# We create beta="the true coefficient vector" to be used in the simulations.
beta = 1:5
# We generate the model matrix X with iid columns and rows and the response y
X = matrix(rnorm(100*400), nrow = 100)
y = X[,1:5]%*%beta
# We chose the variance of the error such that SNR = 3
snr = 3
error = rnorm(100, mean = 0, sd=sqrt(var(y)/snr))
y = y+error
# Rows in the training sample
train.idx = sample(100, 50)
# Group indices for the SGL
group_index = rep(1:40, each=10)
# Input data for the iterative
data.train = list(x=X[train.idx,], y=y[train.idx])
data.validate = list(x=X[-train.idx,], y=y[-train.idx])
# We run the (unpooled) iterative SGL. For the 2-parameter version use isg_simple()
isgl.fit = isgl(data.train, data.validate, group_index, type = "linear")
# Best model returned by the iSGL algorithm
isgl.fit$beta
isgl.fit$intercept
library(sglfast)
# We create beta="the true coefficient vector" to be used in the simulations.
beta = 1:5
# We generate the model matrix X with iid columns and rows and the response y
X = matrix(rnorm(100*400), nrow = 100)
y = X[,1:5]%*%beta
# We generate the response from a logit model
y = ((1+exp(-y))^-1 > 0.5) + 0
# Rows in the training sample
train.idx = sample(100, 50)
# Group indices for the SGL
group_index = rep(1:40, each=10)
# Input data for the iterative
data.train = list(x=X[train.idx,], y=y[train.idx])
data.validate = list(x=X[-train.idx,], y=y[-train.idx])
# We run the (unpooled) iterative SGL. For the 2-parameter version use isg_simple()
isgl.fit = isgl_simple(data.train, data.validate, group_index, type = "logit")
# Best model returned by the iSGL algorithm
isgl.fit$beta
isgl.fit$intercept
Simon, N., J. Friedman, T. Hastie, and R. Tibshirani (2013). A sparse-group lasso. Journal of Computational and Graphical Statistics 22 (2), 231–245.