prediction_performance: Prediction Performance of Adaptive PENSE Fits

In pense: Penalized Elastic Net S/MM-Estimator of Regression

Prediction Performance of Adaptive PENSE Fits

Description

Extract the prediction performance of one or more (adaptive) PENSE fits.

Usage

prediction_performance(..., alpha = NULL, lambda = "min", se_mult = 1)

## S3 method for class 'pense_pred_perf'
print(x, ...)

Arguments

...	one or more (adaptive) PENSE fits with cross-validation information.
alpha	Either a numeric vector or NULL (default). If given, only fits with the given alpha value are considered. If lambda is a numeric value and object was fit with multiple alpha values, the parameter alpha must not be missing.
lambda	either a string specifying which penalty level to use ("min", "se", ⁠"{x}-se⁠") or a single numeric value of the penalty parameter. See details.
se_mult	If lambda = "se", the multiple of standard errors to tolerate.
x	an object with information on prediction performance created with prediction_performance().

Details

If lambda = "se" and the cross-validation was performed with multiple replications, use the penalty level whit prediction performance within se_mult of the best prediction performance.

Value

a data frame with details about the prediction performance of the given PENSE fits. The data frame has a custom print method summarizing the prediction performances.

See Also

summary.pense_cvfit() for a summary of the fitted model.

Other functions for plotting and printing: plot.pense_cvfit(), plot.pense_fit(), summary.pense_cvfit()

pense documentation built on Sept. 11, 2024, 6:51 p.m.