create_z

Create complete-case outcome, weights, and Z

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

create_z function

<dl><dt>Y</dt>
<dd>the outcome</dd>
<dt>C</dt>
<dd>indicator of missing or observed</dd>
<dt>Z</dt>
<dd>the covariates observed in phase 1 and 2 data</dd>
<dt>X</dt>
<dd>all covariates</dd>
<dt>ipc_weights</dt>
<dd>the weights</dd></dl>

Arguments

Create complete-case outcome, weights, and Z — create_z

<dl>

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


<dt>C</dt>
<dd>indicator of missing or observed</dd>


<dt>Z</dt>
<dd>the covariates observed in phase 1 and 2 data</dd>


<dt>X</dt>
<dd>all covariates</dd>


<dt>ipc_weights</dt>
<dd>the weights</dd>

</dl>

create_z: Create complete-case outcome, weights, and Z

Description

Usage

Value

Arguments