hdi: lasso.cv – R documentation

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lasso.cv

Select Predictors via (10-fold) Cross-Validation of the Lasso

Performs (n-fold) cross-validation of the lasso (via cv.glmnet) and determines the prediction optimal set of parameters.

lasso.cv(x, y,
         nfolds = 10,
         grouped = nrow(x) > 3*nfolds,
         ...)

`x`	numeric design matrix (without intercept) of dimension n p*.
`y`	response vector of length n.
`nfolds`	the number of folds to be used in the cross-validation
`grouped`	corresponds to the `grouped` argument to `cv.glmnet`. This has a smart default such that glmnet does not give a warning about too small sample size.
`...`	further arguments to be passed to `cv.glmnet`.

The function basically only calls cv.glmnet, see source code.

Vector of selected predictors.

Lukas Meier

x <- matrix(rnorm(100*1000), nrow = 100, ncol = 1000)
y <- x[,1] * 2 + x[,2] * 2.5 + rnorm(100)
sel <- lasso.cv(x, y)
sel

High-Dimensional Inference

v0.1-7

GPL

Authors

Lukas Meier [aut, cre], Ruben Dezeure [aut], Nicolai Meinshausen [aut], Martin Maechler [aut], Peter Buehlmann [aut]

Initial release

2019-03-27