Fit a bag of logistic regression/RSF models with penalized regression in a train-validate-test setup

Usage

bag_fit_net_logit(
  f,
  data,
  samples,
  metric = c(AUC, conditionalBoyce, conditionalSomersD, conditionalAUC)[[1]],
  method = c("Lasso", "Ridge", "AdaptiveLasso", "DecayAdaptiveLasso", "ElasticNet")[1],
  standardize = c("internal", "external", FALSE)[1],
  alpha = NULL,
  penalty.factor = NULL,
  predictor_table = NULL,
  na.action = "na.pass",
  out_dir_file = NULL,
  parallel = c(FALSE, "foreach", "mclapply")[1],
  mc.cores = 2L,
  verbose = FALSE,
  ...
)

Arguments

standardize: internal = internal glmnet standaridization, i.e. using glmnet with argument standardize = TRUE. This also standardizes dummy variables, but returns the estimated coefficients back to the original scale. This however can cause baises in the estimates because of the bias-variance tradeoff that L1 and L1 regularization methods try to minimize. See more info in https://stackoverflow.com/questions/17887747/how-does-glmnets-standardize-argument-handle-dummy-variables external = glmnet is called with argument standardize = FALSE, but standization is done by the bag_fit_net_logit function. Return coefs in the original scale?? Implement. If FALSE, no standardization of predictors is done.
mc.cores: Only relevant if parallel == "mclapply". If parallel == "foreach", cores must be assigned before running fit_multi_net_logit() using parallel::makeCluster() and doParallel::registerDoParallel().
...: Options for net_logit and glmnet