vimp_se

Compute standard error estimates for estimates of variable importance.

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. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Brian D. Williamson

vimp

Perform Inference on Algorithm-Agnostic Variable Importance

Jean Feng

Charlie Wolock

Noah Simon

Marco Carone

vimp_se function

<dl><dt>eif_full</dt>
<dd>the estimated efficient influence function (EIF) based on
the full set of covariates.</dd>
<dt>eif_reduced</dt>
<dd>the estimated EIF based on the reduced set of covariates.</dd>
<dt>cross_fit</dt>
<dd>logical; was cross-fitting used to compute the EIFs?
(defaults to <code>TRUE</code>)</dd>
<dt>sample_split</dt>
<dd>logical; was sample-splitting used? (defaults to <code>TRUE</code>)</dd>
<dt>na.rm</dt>
<dd>logical; should NA's be removed in computation?
(defaults to <code>FALSE</code>).</dd></dl>

Arguments

Estimate variable importance standard errors — vimp_se

<dl>

<dt>eif_full</dt>
<dd>the estimated efficient influence function (EIF) based on
the full set of covariates.</dd>


<dt>eif_reduced</dt>
<dd>the estimated EIF based on the reduced set of covariates.</dd>


<dt>cross_fit</dt>
<dd>logical; was cross-fitting used to compute the EIFs?
(defaults to <code>TRUE</code>)</dd>


<dt>sample_split</dt>
<dd>logical; was sample-splitting used? (defaults to <code>TRUE</code>)</dd>


<dt>na.rm</dt>
<dd>logical; should NA's be removed in computation?
(defaults to <code>FALSE</code>).</dd>

</dl>

vimp_se: Estimate variable importance standard errors

Description

Usage

Value

Arguments

Details