extract_covariates

Takes a string including potentially model terms like <code>*</code> and <code>:</code> and
extracts out the individual variables

Implements standard and reference based multiple imputation methods for continuous
longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
this package supports deterministic conditional mean imputation and jackknifing as described
in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Isaac Gravestock

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

extract_covariates function

<dl><dt>x</dt>
<dd>string of variable names potentially including interaction terms</dd></dl>

Arguments

Extract Variables from string vector — extract_covariates

<dl>

<dt>x</dt>
<dd>string of variable names potentially including interaction terms</dd>

</dl>

State of Data and AI Literacy Report 2025

extract_covariates: Extract Variables from string vector

Description

Usage

Arguments

Details