R/internal_evaluate_predictions.R

In cvms: Cross-Validation for Model Selection

# Evaluates single model object
# and extracts information like coefficients
internal_evaluate_predictions <- function(
  data,
  prediction_col,
  target_col,
  model_was_null_col,
  type,
  fold_info_cols = list(
    rel_fold = "rel_fold",
    abs_fold = "abs_fold",
    fold_column = "fold_column"
  ),
  fold_and_fold_col = NULL,
  group_info = NULL,
  model_specifics,
  metrics,
  id_col = NULL,
  id_method = NULL,
  stds_col = NULL,
  include_fold_columns = TRUE,
  include_predictions = TRUE,
  na.rm = dplyr::case_when(
    type == "gaussian" ~ TRUE,
    type == "binomial" ~ FALSE,
    type == "multinomial" ~ FALSE
  )
) {
  if (type == "gaussian") {
    eval_pred_fn <- evaluate_predictions_gaussian
  } else if (type == "binomial") {
    eval_pred_fn <- evaluate_predictions_binomial
  } else if (type == "multinomial") {
    eval_pred_fn <- evaluate_predictions_multinomial
  }

  eval_pred_fn(
    data = data,
    prediction_col = prediction_col,
    target_col = target_col,
    model_was_null_col = model_was_null_col,
    id_col = id_col,
    id_method = id_method,
    fold_info_cols = fold_info_cols,
    fold_and_fold_col = fold_and_fold_col,
    group_info = group_info,
    stds_col = stds_col,
    model_specifics = model_specifics,
    metrics = metrics,
    include_fold_columns = include_fold_columns,
    include_predictions = include_predictions,
    na.rm = na.rm
  )
}