cv_rlasso: Cross Validate Robust LASSO Regression

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

Cross Validate Robust LASSO Regression

Usage

cv_rlasso(
  formula,
  data,
  cv.method = c("boot632", "boot", "cv", "repeatedcv", "LOOCV"),
  nfolds = 5,
  nrep = 4,
  folds = NULL,
  tunlen = 25,
  crit = c("TauScale2", "RobustMAE", "RobustMSE")
)

Arguments

formula	a model formula
data	a training data set
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 25.
crit	the criterion by which to evaluate the model performance. must be one of "TauScale2" (the default), "RobustMAE", or "RobustMSE". "TauScale2" gives the squared tau estimate of the scale of the residuals. "RobustMAE" and "RobustMSE" are the tau estimates of mean absolute and squared errors respectively.
cv.methodone	of "boot632" (the default), "boot", "cv", "repeatedcv", or "LOOCV".

Value

a train object

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.