predict.nested.glmnetr: Give predicteds based upon the cv.glmnet output object...

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

Give predicteds based upon the cv.glmnet output object contained in the nested.glmnetr output object.

Description

This is essentially a redirect to the summary.cv.glmnetr function for nested.glmnetr output objects, based uopn the cv.glmnetr output object contained in the nested.glmnetr output object.

Usage

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

Arguments

object	A 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 validation informed 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 validation informed 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 the xs_new*Beta estimates based upon the predictor matrix, or model coefficients.

See Also

predict.cv.glmnetr , predict_ann_tab , nested.glmnetr

Examples

sim.data=glmnetr.simdata(nrows=1000, ncols=100, beta=NULL)
xs=sim.data$xs 
y_=sim.data$yt
event=sim.data$event
# for this example we use a small number for folds_n to shorten run time 
fit3 = nested.glmnetr(xs, NULL, y_, event, family="cox", folds_n=3) 
betas = predict(fit3)
betas$beta

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