print a cross-validated glmnet object

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

# S3 method for 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.

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 Measure     SE Nonzero
#> min 0.2092    1.14 0.1223       0
#> 1se 0.2092    1.14 0.1223       0
fit1r = cv.glmnet(x, y, relax = TRUE)
print(fit1r)
#> 
#> Call:  cv.glmnet(x = x, y = y, relax = TRUE) 
#> 
#> Measure: Mean-Squared Error 
#> 
#>     Gamma Lambda Measure      SE Nonzero
#> min   0.5 0.1583   1.116 0.06643       1
#> 1se   1.0 0.2092   1.127 0.06724       0
## print.cv.glmnet(fit1r)  ## CHECK WITH TREVOR