get_conditional_parameters

Takes parameters for a multivariate normal distribution and observed values
to calculate the conditional distribution for the unobserved values.

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 

get_conditional_parameters function

<dl><dt>pars</dt>
<dd>a <code>list</code> with elements <code>mu</code> and <code>sigma</code> defining the mean vector and
covariance matrix respectively.</dd>
<dt>values</dt>
<dd>a vector of observed values to condition on, must be same length as <code>pars$mu</code>.
Missing values must be represented by an <code>NA</code>.</dd></dl>

Arguments

Derive conditional multivariate normal parameters — get_conditional_parameters

<dl>

<dt>pars</dt>
<dd>a <code>list</code> with elements <code>mu</code> and <code>sigma</code> defining the mean vector and
covariance matrix respectively.</dd>


<dt>values</dt>
<dd>a vector of observed values to condition on, must be same length as <code>pars$mu</code>.
Missing values must be represented by an <code>NA</code>.</dd>

</dl>

get_conditional_parameters: Derive conditional multivariate normal parameters

Description

Usage

Value

Arguments