elnet_obj: Elastic net objective function for model based optimization

In tramnet: Penalized Transformation Models

Elastic net objective function for model based optimization

Description

This function generates an objective function for model-based optimization based on the cross-validated log-likelihood of a tramnet model with an elastic net penalty. It is not intended to be called by the user directly, instead it will be given as an argument to mbo_tramnet.

Usage

elnet_obj(object, minlambda = 0, maxlambda = 16, minalpha = 0,
  maxalpha = 1, folds, noisy = FALSE, fold)

Arguments

object	object of class tramnet
minlambda	minimum value for lambda (default: 0)
maxlambda	maximum value for lambda (default: 16)
minalpha	minimum value for alpha (default: 0)
maxalpha	maximum value for alpha (default: 1)
folds	self specified folds for cross validation (mainly for reproducibility and comparability purposes)
noisy	indicates whether folds for k-fold cross-validation should be random for each iteration, leading to a noisy objective function (default: FALSE)
fold	fold for cross validation

Value

Single objective function for model based optimization.

tramnet documentation built on April 1, 2023, 12:20 a.m.