elnet_obj {tramnet} | R Documentation |
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 |
minlambda |
minimum value for lambda (default: |
maxlambda |
maximum value for lambda (default: |
minalpha |
minimum value for alpha (default: |
maxalpha |
maximum value for alpha (default: |
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: |
fold |
fold for cross validation |
Value
Single objective function for model based optimization.