cv_pense_fa: Cross Validate Penalized Elastic Net S-Estimator with a Fixed...
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Cross Validate Penalized Elastic Net S-Estimator with a Fixed Alpha Parameter (PENSE)

cv_pense_fa(
  formula,
  data,
  alpha = 1,
  cv.method = "boot632",
  nfolds = 5,
  nrep = 4,
  folds = NULL,
  tunlen = 25,
  crit = "RobustMAE",
  max.c = 50
)

`formula`	a model formula
`data`	a training data set
`alpha`	the mixing parameter for the elastic net. alpha = 0 yields ridge regression, and alpha = 1 yields the LASSO.
`cv.method`	preferably one of "boot632" (the default), "cv", or "repeatedcv".
`nfolds`	the number of bootstrap or cross-validation folds to use. defaults to 5.
`nrep`	the number of repetitions for cv.method = "repeatedcv". defaults to 4.
`folds`	a vector of pre-set cross-validation or bootstrap folds from caret::createResample or caret::createFolds.
`tunlen`	the number of values for the unknown hyperparameter to test. defaults to 10.
`crit`	the criterion by which to evaluate the model performance. must be one of "RobustMAE" (the default) or "RobustMSE".
`max.c`	the largest value of the constant for calculating lambda. defaults to 8, but may be adjusted. for example, if the error metric becomes constant after a certain value of C, it may be advisable to lower max.c to a smaller value to obtain a more fine-grained grid over the plausible values.