getLassoLambda: Get lambda value for lasso

In gregfaletto/cssr: Feature Selection Using Cluster Stability Selection

Get lambda value for lasso

Description

Chooses a lambda value for the lasso by cross-validation.

Usage

getLassoLambda(X, y, lambda_choice = "1se", nfolds = 10)

Arguments

X	An n x p numeric matrix (preferably) or a data.frame (which will be coerced internally to a matrix by the function model.matrix) containing the p >= 2 features/predictors that will be used by cluster stability selection.
y	The response; an n-dimensional numeric vector.
lambda_choice	Character; either "min" or "1se". If "min", chooses the lambda that minimizes the cross-validated error; if "1se", chooses the largest lambda within one standard error of the minimum error lambda (resulting in a smaller selected set, which may be desirable because the model size corresponding to the minimum error lambda tends to be larger than optimal. See, for example, Bühlmann and Meinshausen 2006, Prop. 1 and Bühlmann and van de Geer 2011, Section 2.5.1.). Default is "1se".
nfolds	Numeric or integer; the number of folds for cross-validation. Must be at least 4 (as specified by cv.glmnet). Default is 10.

Value

A numeric; the selected value of lambda.

Author(s)

Gregory Faletto, Jacob Bien

References

Bühlmann, P., & Meinshausen, N. (2006). High-Dimensional Graphs and Variable Selection With the Lasso. The Annals of Statistics, 34(3), 1436–1462. https://doi.org/10.1214/009053606000000281.
Peter Bühlmann and Sara van de Geer. Statistics for High-Dimensional Data. Springer Series in Statistics. Springer, Heidelberg, 2011. ISBN 978-3-642-20191-2. http://dx.doi.org/10.1007/978-3-642-20192-9.

gregfaletto/cssr documentation built on March 3, 2023, 1 p.m.