print a cross-validated glmnet object

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.

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.

Author

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie [email protected]

Examples

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = cv.glmnet(x, y)
print(fit1)
#> 
#> Call:  cv.glmnet(x = x, y = y) 
#> 
#> Measure: Mean-Squared Error 
#> 
#>     Lambda Index Measure     SE Nonzero
#> min 0.1510     6   1.070 0.1010       1
#> 1se 0.2405     1   1.095 0.1029       0
fit1r = cv.glmnet(x, y, relax = TRUE)
print(fit1r)
#> 
#> Call:  cv.glmnet(x = x, y = y, relax = TRUE) 
#> 
#> Measure: Mean-Squared Error 
#> 
#>     Gamma Index Lambda Index Measure      SE Nonzero
#> min     0     1 0.1657     5   1.066 0.09863       1
#> 1se     1     5 0.2405     1   1.114 0.10027       0
## print.cv.glmnet(fit1r)  ## CHECK WITH TREVOR