ProxReg (version 1.1.2)

lasso_fista: Lasso regression with fixed step with FISTA algorithm

Description

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

Usage

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

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

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

ini

initial value for the coefficients

tol

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

Details

lasso_fista

Examples

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