lasso_multi_back: Lasso regression with backtraking line research for...

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

Lasso regression with backtraking line research for multinomial response variable

Description

the function carries out the Lasso regression for multinomial response using backtraking line research and FISTA/ISTA algorithm.

Usage

lasso_multi_back(data,y,x,lambda,max_step=10000,image=FALSE,fista=TRUE,tol=10^-7,ini=0)

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
image	plots the evolution of errors in term of lambda values
fista	fista=TRUE: use FISTA algortihm for the multiclass logistic regression; fista=FALSE: use ISTA algortihm
tol	tolerance for the convergence
ini	initial value for the coefficients, default is 0 #'@examples library(glmnet) data("MultinomialExample") x<-MultinomialExample$x y<-MultinomialExample$y mult<-as.data.frame(cbind(x,y)) lasso_multi_back(mult,y="y",x=colnames(mult)[-31],max_step = 1000,lambda=0.01,image=TRUE,fista=TRUE,ini=0)

Details

lasso_multi_back

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.

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