# 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 `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 `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

 ```1 2 3``` ```data(splice) lambdamax(y ~ ., data = splice, model = LogReg(), center = TRUE, standardize = TRUE) ```

lassogrp documentation built on May 31, 2017, 4:04 a.m.