l_CV: K-Fold Cross validation for L1/L2 regression

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

K-Fold Cross validation for L1/L2 regression

Description

the function realizes K-Fold Cross validation for ridge/Lasso regression to help to choose the lambda that minimise the RSS

Usage

l_CV(data,y,x,lambda,k,mode=2,binary=FALSE,step=1000,bound=0.5,fista=TRUE,tol=10^-7)

Arguments

data	name of the dataset
y	name of the dependent variables
x	name of the independent variable
lambda	a number or a vector of lambda-value to be evaluated in the regression
k	integer, which indicates how many training and test set will be splited from the dataset
mode	1: ridge regression; 2: lasso regression
binary	logical, if TRUE, the dependent variable is binary
step	maximum number of steps
bound	threshold for binary dependent variable
fista	logical, if TRUE, the FISTA algorithm is used
tol	tolerance for convergence, it is 10^-7 by default

Value

the lambda values that minimize the MSE

Examples

l_CV(mtcars,"hp",c("mpg","qsec","disp"),c(0.01,0.1),k=5,mode=2)

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