ProxReg (version 1.1.2)

lasso_fista_back: Lasso regression with backtraking line research with FISTA algorithm

Description

the function carries out the Lasso regression using backtraking line research and FISTA algorithm.

Usage

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

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.

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

tol

tolerance for convergence, it is 10^-7 by default

type

type of response variable, by default, it is 'Gaussian' for continuos response and can be modified as 'Binomial' for binary response

ini

initial value for the coefficients, default is 0.5

image

plots the evolution of errors in term of lambda values

Details

lasso_fista_back

Examples

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