check_inputs

Check inputs to a call to vim, cv_vim, or sp_vim

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

check_inputs function

<dl><dt>Y</dt>
<dd>the outcome</dd>
<dt>X</dt>
<dd>the covariates</dd>
<dt>f1</dt>
<dd>estimator of the population-optimal prediction function
using all covariates</dd>
<dt>f2</dt>
<dd>estimator of the population-optimal prediction function
using the reduced set of covariates</dd>
<dt>indx</dt>
<dd>the index or indices of the covariate(s) of interest</dd></dl>

Arguments

Check inputs to a call to vim, cv_vim, or sp_vim — check_inputs

<dl>

<dt>Y</dt>
<dd>the outcome</dd>


<dt>X</dt>
<dd>the covariates</dd>


<dt>f1</dt>
<dd>estimator of the population-optimal prediction function
using all covariates</dd>


<dt>f2</dt>
<dd>estimator of the population-optimal prediction function
using the reduced set of covariates</dd>


<dt>indx</dt>
<dd>the index or indices of the covariate(s) of interest</dd>

</dl>

check_inputs: Check inputs to a call to vim, cv_vim, or sp_vim

Description

Usage

Value

Arguments

Details