lasso_ista: Lasso regression with fixed step with ISTA algorithm

In ProxReg: Linear Models for Prediction and Classification using Proximal Operators

Lasso regression with fixed step with ISTA algorithm

Description

the function carries out the Lasso regression using fixed step using ISTA algorithm.

Usage

lasso_ista(data,y,x,lambda,max_step=10000,type="Gaussian",image=TRUE,tol=10^-7,ini=0.5)

Arguments

data	name of the dataset
y	name of the dependent variables
x	name of the independent variable
lambda	a vector of lambda-value to be evaluated in the regression
max_step	maximum number of steps
type	type of response variable, by default, it is 'Gaussian' for continuos response and can be modified as 'Binomial' for binary response
image	logical, if TRUE, the evolution of errors in term of lambda values will be plotted
tol	tolerance for convergence, it is 10^-7 by default
ini	initial value for the coefficients

Details

lasso_ista

Value

A list containing:

• coefficients: A matrix where each column represents the estimated regression coefficients for a different lambda value.

• error_evolution: A numeric vector tracking the error at certain step.

• num_steps: An integer vector indicating the number of steps in which errors are calculated.

Examples

library("glmnet")
data("QuickStartExample")
test<-as.data.frame(cbind(QuickStartExample$y,QuickStartExample$x))
lasso_ista(test,"V1",colnames(test)[2:21],lambda=0.1,image=TRUE,max_step=1000)

ProxReg documentation built on April 3, 2025, 9:21 p.m.