lambdaseq: Generate sequence of lambda for LASSO regression

In tsutils: Time Series Exploration, Modelling and Forecasting

Generate sequence of lambda for LASSO regression

Description

Calculates the lambdaMax value, which is the penalty term (lambda) beyond which coefficients are guaranteed to be all zero and provides a sequence of nLambda values to lambdaMin in logarithmic descent.

Usage

lambdaseq(
  x,
  y,
  weight = NA,
  alpha = 1,
  standardise = TRUE,
  lambdaRatio = 1e-04,
  nLambda = 100,
  addZeroLambda = FALSE
)

Arguments

x	matrix of regressors. See glmnet.
y	response variable. See glmnet.
weight	vector of length(nrow(y)) for weighted LASSO estimation. See glmnet.
alpha	elastic net mixing value. See glmnet.
standardise	if TRUE, then variables are standardised.
lambdaRatio	ratio between lambdaMax and lambdaMin. That is, lambdaMin <- lambdaMax * lambdaRatio.
nLambda	length of the lambda sequence.
addZeroLambda	if TRUE, then set the last value in the lambda sequence to 0, which is the OLS solution.

Value

A list that contains:

• lambda: sequence of lambda values, from lambdaMax to lambdaMin.

• lambdaMin: minimal lambda value.

• lambdaMax: maximal lambda value.

• nullMSE: MSE of the fit using just a constant term.

Author(s)

Oliver Schaer, [e-mail],

Nikolaos Kourentzes, [e-mail].

References

Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: the lasso and generalizations. CRC press.

Examples

y <- mtcars[,1]
x <- as.matrix(mtcars[,2:11])
lambda <- lambdaseq(x, y)$lambda

## Not run: 
  library(glmnet)
  fit.lasso <- cv.glmnet(x, y, lambda = lambda)
  coef.lasso <- coef(fit.lasso, s = "lambda.1se")

## End(Not run)

tsutils documentation built on Nov. 15, 2023, 1:08 a.m.