| print.cv.glmnet {glmnet} | R Documentation |
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 hastie@stanford.edu
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