ls_design

ls_design_equal

ls_design_counterfactual

ls_design_proportional

Calculates the design vector as required to generate the lsmean
and standard error. <code>ls_design_equal</code> calculates it by
applying an equal weight per covariate combination whilst
<code>ls_design_proportional</code> applies weighting proportional
to the frequency in which the covariate combination occurred
in the actual dataset.

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 

ls_design function

<dl><dt>data</dt>
<dd>A data.frame</dd>
<dt>frm</dt>
<dd>Formula used to fit the original model</dd>
<dt>fix</dt>
<dd>A named list of variables with fixed values</dd></dl>

Arguments

Calculate design vector for the lsmeans — ls_design

<dl>

<dt>data</dt>
<dd>A data.frame</dd>


<dt>frm</dt>
<dd>Formula used to fit the original model</dd>


<dt>fix</dt>
<dd>A named list of variables with fixed values</dd>

</dl>

Calculate design vector for the lsmeans

ls_design: Calculate design vector for the lsmeans

Description

Usage

Arguments