grplasso (version 0.4-7)

lambdamax: 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 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(), ...)
# S3 method for default
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.

Value

An object of type numeric is returned.

Details

Uses nlminb to optimize the non-penalized parameters.

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

# NOT RUN {
data(splice)
lambdamax(y ~ ., data = splice, model = LogReg(), center = TRUE,
          standardize = TRUE)
# }