Fit Functions for Stability Selection

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Description

Functions that fit a model until q variables are selected and that returns the indices (and names) of the selected variables.

Usage

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## package lars:
lars.lasso(x, y, q, ...)
lars.stepwise(x, y, q, ...)

## package glmnet:
glmnet.lasso(x, y, q, ...)
glmnet.lasso_maxCoef(x, y, q, ...)

Arguments

x

a matrix containing the predictors or an object of class "mboost".

y

a vector or matrix containing the outcome.

q

number of (unique) selected variables (or groups of variables depending on the model) that are selected on each subsample.

...

additional arguments to the underlying fitting function.

Details

All fitting functions are named after the package and the type of model that is fitted: package_name.model, e.g., glmnet.lasso stands for a lasso model that is fitted using the package glmnet.

If one wants to use glmnet.lasso_maxCoef, one must specify the penalty parameter lambda (via the ... argument) or in stabsel via args.fitfun(lambda = ).

Value

A named list with elements

selected

logical. A vector that indicates which variable was selected.

path

logical. A matrix that indicates which variabkle was selected in which step. Each row represents one variable, the columns represent the steps.

Examples

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data("bodyfat", package = "TH.data")
## selected variables
lars.lasso(bodyfat[, -2], bodyfat[,2], q = 3)$selected
lars.stepwise(bodyfat[, -2], bodyfat[,2], q = 3)$selected
glmnet.lasso(bodyfat[, -2], bodyfat[,2], q = 3)$selected