predict.cv.glmnetr: Give predicteds for elastic net models form a...

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

Give predicteds for elastic net models form a nested.glmnetr() output object.

Description

Give predicteds based upon a cv.glmnetr() output object. By default lambda and gamma are chosen as the minimizing values for the relaxed lasso model. If gam=1 and lam=NULL then the best unrelaxed lasso model is chosen and if gam=0 and lam=NULL then the best fully relaxed lasso model is selected.

Usage

## S3 method for class 'cv.glmnetr'
predict(
  object,
  xs_new = NULL,
  alpha = NULL,
  gamma = NULL,
  lambda = NULL,
  type = "lasso",
  comment = TRUE,
  ...
)

Arguments

object	A cv.glmnetr (or nested.glmnetr) output object.
xs_new	The predictor matrix. If NULL, then betas are provided.
alpha	A specific value of alpha for plotting. Used only when type is set to "elastic". Specifies which alpha is to be used for deviance plots. Default is "alpha.min", else must be an element of the alpha vector used in running the elastic net model. This can be reviewed using summary(fit) where fit is a nested.glmnetr() output object. Note, alpha is 1 for the lasso model and alpha is 0 for the ridge model.
gamma	The gamma value for choice of beta. If NULL, then gamma.min is used from the cross validated tuned relaxed model. We use the term gam instead of gamma as gamma usually denotes a vector in the package.
lambda	The lambda value for choice of beta. If NULL, then lambda.min is used from the cross validated tuned relaxed model. We use the term lam instead of lambda as lambda usually denotes a vector in the package.
type	type of model on which to base predictds. One of "lasso", "ridge" and "elastic" if elastic net model is fit.
comment	Default of TRUE to write to console information on lam and gam selected for output. FALSE will suppress this write to console.
...	Additional arguments passed to the predict function.

Value

Either predicteds (xs_new*beta estimates based upon the predictor matrix xs_new) or model coefficients, based upon a cv.glmnetr() output object. When outputting coefficients (beta), creates a list with the first element, beta_, including 0 and non-0 terms and the second element, beta, including only non 0 terms.

See Also

summary.cv.glmnetr , nested.glmnetr

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