summary.beset: Summary Methods for 'beset' Objects

View source: R/methods_summary.R

summary.besetR Documentation

Summary Methods for beset Objects

Description

These functions are all methods for class beset objects.

Usage

## S3 method for class 'beset'
summary(
  object,
  n_pred = NULL,
  alpha = NULL,
  lambda = NULL,
  metric = "auto",
  oneSE = TRUE,
  ...
)

Arguments

object

An object for which a summary is desired.

n_pred

(Optional) integer number of predictors that the best model should contain. If specified, all other arguments are ignored.

alpha

Value of the elastic-net mixing parameter alpha at which predictions are required. Must be a value of alpha that was tried during training. If omitted, cross-validated optimal or 1SE-from-optimal alpha will be used, depending on oneSE argument.

lambda

Value of the elastic-net penalty parameter lambda at which predictions are required. If omitted, cross-validated optimal or 1SE-from-optimal lambda will be used, depending on oneSE argument.

metric

Character string giving prediction metric on which to base model selection. Can be one of "auc" for area under the (ROC) curve (only available for binomial family), "mae" for mean absolute error (not available for binomial family), "mae" for mean absolute error, "mce" for mean cross entropy, or "mse" for mean squared error. Default is "auto" which plots MSE for Gaussian-family models and MCE for all other families.

oneSE

Logical indicating whether or not to use the "one standard error" rule. If TRUE (default) the simplest model within one standard error of the optimal model is returned. If FALSE the model with the optimal cross-validation performance is returned.

...

Additional arguments passed to model summary methods.

robust

Logical indicating whether or not to produce summary output for only robust predictors, defined here as having non-zero beta coefficients for every repetition of the cross-validation procedure.


jashu/beset documentation built on April 20, 2023, 5:28 a.m.