Get lambda value for lasso
getLassoLambda.RdChooses a lambda value for the lasso used on a subsample of size n/2 (as in cluster stability selection) by cross-validation.
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 or integer vector. (Unlike in the more general css setup, this response must be real-valued since lambda will be determined using the lasso with cross-validation.)
- 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.
- alpha
Numeric; the elastic net mixing parameter. Default is 1 (in which case the penalty is for lasso)
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.
Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization
Paths for Generalized Linear Models via Coordinate Descent. Journal of
Statistical Software, 33(1), 1-22. URL https://www.jstatsoft.org/v33/i01/.