lambdamax: Lasso: get the Maximal lambda

In lassogrp: Lasso Regression Including Group Lasso and Adaptive Lasso

Description

Calculates the maximal value of the weight lambda of the L1 penalty term in a Lasso regression. For values >= this value, the null model will be obtained as the result of the penalized regression.

Usage

lambdamax(x, ...)

## S3 method for class 'formula'
lambdamax(formula, nonpen = ~1, data,
  weights, subset, na.action, offset, coef.init,
  penscale = sqrt, model = LogReg(),
  center = NA, standardize = TRUE, contrasts = NULL,
  nlminb.opt = list(), ...)

## Default S3 method:
lambdamax(x, y, index, weights = NULL,
  offset = rep(0, length(y)), coef.init = rep(0, ncol(x)),
  penscale = sqrt, model = LogReg(),
  center = NA, standardize = TRUE,
  nlminb.opt = list(), ...)

Arguments

x	design matrix (including intercept)
y	response vector
formula	formula of the penalized variables. The response has to be on the left hand side of ~.
nonpen	formula of the nonpenalized variables. This will be added to the formula argument above and doesn't need to have the response on the left hand side.
data	data.frame containing the variables in the model.
index	vector which defines the grouping of the variables. Components sharing the same number build a group. Non-penalized coefficients are marked with NA.
weights	vector of observation weights.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs.
offset	vector of offset values.
coef.init	initial parameter vector. Penalized groups are discarded.
penscale	rescaling function to adjust the value of the penalty parameter to the degrees of freedom of the parameter group. See the reference below.
model	an object of class lassoModel implementing the negative log-likelihood, gradient, hessian etc. See lassoModel for more details.
standardize, center	logical; see lasso.
contrasts	an (optional) list with the contrasts for the factors in the model.
nlminb.opt	arguments to be supplied to nlminb.
...	additional arguments to be passed to the functions defined in model.

Details

Uses nlminb to optimize the non-penalized parameters.

Value

Numerical value of the maximal lambda

Author(s)

Lukas Meier, Seminar f. Statistik, ETH Zurich

Examples

data(splice)
lambdamax(y ~ ., data = splice, model = LogReg(), center = TRUE,
          standardize = TRUE)

lassogrp documentation built on July 25, 2021, 3 a.m.