summary.cv.glmnetr_0_6_1: Output summary for elastic net models fit within a...

In glmnetr: Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

Output summary for elastic net models fit within a nested.glmnetr() output object.

Description

Summarize the cross-validation informed model fit. The fully penalized (gamma=1) beta estimate will not be given by default but can too be output using printg1=TRUE.

Usage

## S3 method for class 'cv.glmnetr_0_6_1'
summary(
  object,
  type = NULL,
  printg1 = "FALSE",
  orderall = FALSE,
  betatol = 1e-14,
  ...
)

Arguments

object	a nested.glmnetr() output object.
type	one of c("lasso", "elastic", "ridge") to select for summarizing, with default of "lasso".
printg1	TRUE to also print out the fully penalized lasso beta, else FALSE to suppress.
orderall	By default (orderall=FALSE) the order terms enter into the lasso model is given for the number of terms that enter in lasso minimizing loss model. If orderall=TRUE then all terms that are included in any lasso fit are described.
betatol	beta values less than betatol are set to 0 when they are approaching the rounding error of the machine architecture. Default is set to 1e-14.
...	Additional arguments passed to the summary function.

Value

Coefficient estimates (beta)

See Also

predict.cv.glmnetr , nested.glmnetr

glmnetr documentation built on June 8, 2025, 10:12 a.m.