get_risk_learner_input: Create input list for 'get_risk_learner'

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

Description

Create input list for get_risk_learner

Usage

get_risk_learner_input(split_Y, Yname, learner, all_fits, V, folds, all_sl,
  all_fit_tasks, all_weight, sl_control)

Arguments

split_Y	The outcome matrix split by relevant validation folds.
Yname	The names of the outcomes. Used to search all_fits and all_sl.
learner	Name of super learner wrapper.
all_fits	List of all learner fits.
V	Number of folds.
folds	Cross-validation folds.
all_sl	List of all super learner weight fits.
all_fit_tasks	List of all learner fit tasks (faster to search over than search over all_fits).
all_weight	List of all outcome weight fits.
sl_control	List of super learner controls.

Value

List with each entry a list with entries: valid_fold (the number of the corresponding fold), Y (univariate outcome in this validation fold), pred (matrix of outcome predictions for this validation fold from learners fit in training folds). get_risk_learner is only used to compute the cross-validated risk of individual outcomes and so is only used in the outer most cross-validation layer.

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