get_formatted_sl: Get a super learner fit for a given outcome with more...

In benkeser/cvma: Cross-validation-based maximal association

Description

This is called if return_outer_sl = TRUE, in which case more information on learner risks etc... is computed and returned.

Usage

get_formatted_sl(task, Y, V, all_fit_tasks, all_fits, folds, sl_control,
  return_learner_fits = TRUE, learners)

Arguments

task	A named list identifying what outcome to use.
Y	A matrix or data.frame of outcomes
V	Number of outer folds of cross-validation (nested cross-validation uses V-1 and V-2 folds), so must be at least four.
all_fit_tasks	A list of all learner fitting tasks (quicker to search over than all_fits).
all_fits	A list of all learner fits (from get_fit)
folds	Vector identifying which fold observations fall into.
sl_control	A list with named entries ensemble.fn, optim_risk_fn, weight_fn, cv_risk_fn, family. Available functions can be viewed with sl_control_options(). See ?sl_control_options for more on how users may supply their own functions.
return_learner_fits	Should the fit component of the learners be returned. Must be TRUE to obtain later predictions.
learners	Super learner wrappers. See SuperLearner::listWrappers.

Value

Named list of super learner results.

benkeser/cvma documentation built on May 5, 2019, 1:37 p.m.