print.cv.glmnet: print a cross-validated glmnet object

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

print a cross-validated glmnet object

Description

Print a summary of the results of cross-validation for a glmnet model.

Usage

## S3 method for class 'cv.glmnet'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	fitted 'cv.glmnet' object
digits	significant digits in printout
...	additional print arguments

Details

A summary of the cross-validated fit is produced, slightly different for a 'cv.relaxed' object than for a 'cv.glmnet' object. Note that a 'cv.relaxed' object inherits from class 'cv.glmnet', so by directly invoking print.cv.glmnet(object) will print the summary as if relax=TRUE had not been used.

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie [e-mail]

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent
https://arxiv.org/abs/1707.08692
Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

See Also

glmnet, predict and coef methods.

Examples

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = cv.glmnet(x, y)
print(fit1)
fit1r = cv.glmnet(x, y, relax = TRUE)
print(fit1r)
## print.cv.glmnet(fit1r)  ## CHECK WITH TREVOR

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.