lambdamax: Function to Find the Maximal Value of the Penalty Parameter...

In grplasso: Fitting User-Specified Models with Group Lasso Penalty

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.

References

Lukas Meier, Sara van de Geer and Peter B\"uhlmann (2008), The Group Lasso for Logistic Regression, Journal of the Royal Statistical Society, 70 (1), 53 - 71

Examples

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

Example output

[1] 68.30414

grplasso documentation built on July 8, 2020, 6:46 p.m.