R: Function to find the maximal value of the penalty parameter...

lambdamax {grplasso}

R Documentation

Function to find the maximal value of the penalty parameter lambda

Description

Determines the value of the penalty parameter lambda when the first penalized parameter group enters the model.

Usage

lambdamax(x, ...)

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

## Default S3 method:
lambdamax(x, y, index, weights = rep(1, length(y)),
          offset = rep(0, length(y)), coef.init = rep(0, ncol(x)),
          penscale = sqrt, model = LogReg(), center = TRUE,
          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 'NA's.
`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 `grpl.model` implementing the negative log-likelihood, gradient, hessian etc. See `grpl.model` for more details.
`center`	logical. If true, the columns of the design matrix will be centered (except a possible intercept column).
`standardize`	logical. If true, the design matrix will be blockwise orthonormalized, such that for each block X^TX = n 1 (after possible centering).
`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

An object of type numeric is returned.

Examples

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

[Package grplasso version 0.4-5 Index]