Lambda.sel: Select the Penalty Parameter of LASSO-type Linear Regression

In SLBDD: Statistical Learning for Big Dependent Data

Select the Penalty Parameter of LASSO-type Linear Regression

Description

Use out-of-sample Root Mean Square Error to select the penalty parameter of LASSO-type linear regression.

Usage

Lambda.sel(X, y, newX, newY, family = "gaussian", alpha = 1)

Arguments

X	Matrix of predictors of the estimation sample.
y	Dependent variables of the estimation sample.
newX	Design matrix in the forecasting subsample.
newY	Dependent variable in the forecasting subsample.
family	Response type. See the glmnet command in R. Possible types are "gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian". Default is "gaussian".
alpha	The elasticnet mixing parameter, with 0 ≤q α ≤q 1. See the glmnet command in R. Default value is 1.

Value

A list containing:

• lambda.min - lambda that achieves the minimum mean square error.

• beta - estimated coefficients for lambda.min.

• mse - mean squared error.

• lambda - the actual sequence of lambda values used.

Examples

X <- cbind(rnorm(200),rnorm(200,2,1),rnorm(200,4,1))
y <- rnorm(200)
newX <- cbind(rnorm(200),rnorm(200,2,1),rnorm(200,4,1))
newy <- rnorm(200)
output <- Lambda.sel(X, y, newX, newy)

SLBDD documentation built on April 27, 2022, 5:08 p.m.