extract_sampled_split_predictions

An object of class <code>"CV.SuperLearner"</code>

cvsl_obj

logical; should we use sample-splitting or not?
Defaults to <code>TRUE</code>.

sample_splitting

A vector of folds to use for sample splitting

sample_splitting_folds

logical; is this the fit to all covariates (<code>TRUE</code>) or not
(<code>FALSE</code>)?

full

Use the cross-validated Super Learner and a set of specified sample-splitting
folds to extract cross-fitted predictions on separate splits of the data. This
is primarily for use in cases where you have already fit a CV.SuperLearner
and want to use the fitted values to compute variable importance without having
to re-fit. The number of folds used in the CV.SuperLearner must be even.

Calculate point estimates of and valid confidence intervals for
nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
using flexible estimators of the underlying regression functions. For more information
about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (arXiv, 2020+) <arXiv:2004.03683>, and Williamson and Feng (ICML, 2020).

Brian D. Williamson

vimp

Perform Inference on Algorithm-Agnostic Variable Importance

Jean Feng

Noah Simon

Marco Carone

extract_sampled_split_predictions function

<dl><dt>cvsl_obj</dt>
<dd>An object of class <code>"CV.SuperLearner"</code></dd>
<dt>sample_splitting</dt>
<dd>logical; should we use sample-splitting or not?
Defaults to <code>TRUE</code>.</dd>
<dt>sample_splitting_folds</dt>
<dd>A vector of folds to use for sample splitting</dd>
<dt>full</dt>
<dd>logical; is this the fit to all covariates (<code>TRUE</code>) or not
(<code>FALSE</code>)?</dd></dl>

Arguments

Extract sampled-split predictions from a CV.SuperLearner object — extract_sampled_split_predictions

<dl>

<dt>cvsl_obj</dt>
<dd>An object of class <code>"CV.SuperLearner"</code></dd>


<dt>sample_splitting</dt>
<dd>logical; should we use sample-splitting or not?
Defaults to <code>TRUE</code>.</dd>


<dt>sample_splitting_folds</dt>
<dd>A vector of folds to use for sample splitting</dd>


<dt>full</dt>
<dd>logical; is this the fit to all covariates (<code>TRUE</code>) or not
(<code>FALSE</code>)?</dd>

</dl>

extract_sampled_split_predictions: Extract sampled-split predictions from a CV.SuperLearner object

Description

Usage

Value

Arguments

See Also