h_construct_model_frame_inputs

Input formulas are converted from mmrm-style to a style compatible
with default <code><a href="/link/stats%3A%3Amodel.frame()?package=mmrm&version=0.3.14" data-mini-rdoc="mmrm::stats::model.frame()">stats::model.frame()</a></code> and <code><a href="/link/stats%3A%3Amodel.matrix()?package=mmrm&version=0.3.14" data-mini-rdoc="mmrm::stats::model.matrix()">stats::model.matrix()</a></code> methods.
The full formula is returned so we can construct, for example, the
<code><a href="/link/model.frame()?package=mmrm&version=0.3.14" data-mini-rdoc="mmrm::model.frame()">model.frame()</a></code> including all columns as well as the requested subset.
The full set is used to identify rows to include in the reduced model frame.

internal

Mixed models for repeated measures (MMRM) are a popular
choice for analyzing longitudinal continuous outcomes in randomized
clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
<doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso
(2008) <doi:10.1177/009286150804200402> for a review. This package
implements MMRM based on the marginal linear model without random
effects using Template Model Builder ('TMB') which enables fast and
robust model fitting. Users can specify a variety of covariance
matrices, weight observations, fit models with restricted or standard
maximum likelihood inference, perform hypothesis testing with
Satterthwaite or Kenward-Roger adjustment, and extract least square
means estimates by using 'emmeans'.

Daniel Sabanes Bove

mmrm

Mixed Models for Repeated Measures

Liming Li

Julia Dedic

Doug Kelkhoff

Kevin Kunzmann

Brian Matthew Lang

Christian Stock

Ya Wang

Craig Gower-Page

Dan James

Jonathan Sidi

Daniel Leibovitz

Daniel D. Sjoberg

Lukas A. Widmer

Boehringer Ingelheim Ltd. 

Gilead Sciences, Inc. 

F. Hoffmann-La Roche AG 

Merck Sharp & Dohme, Inc. 

AstraZeneca plc 

inferential.biostatistics GmbH 

h_construct_model_frame_inputs function

<dl><dt>formula</dt>
<dd>(<code>mmrm</code>) mmrm fit object.</dd>
<dt>data</dt>
<dd>optional data frame that will be
passed to <code><a href="/link/model.frame()?package=mmrm&version=0.3.14" data-mini-rdoc="mmrm::model.frame()">model.frame()</a></code> or <code><a href="/link/model.matrix()?package=mmrm&version=0.3.14" data-mini-rdoc="mmrm::model.matrix()">model.matrix()</a></code></dd>
<dt>include</dt>
<dd>(<code>character</code>) names of variable to include</dd>
<dt>full</dt>
<dd>(<code>flag</code>) indicator whether to return full model frame (deprecated).</dd></dl>

Arguments

Input formulas are converted from mmrm-style to a style compatible
with default <code><a href='https://rdrr.io/r/stats/model.frame.html'>stats::model.frame()</a></code> and <code><a href='https://rdrr.io/r/stats/model.matrix.html'>stats::model.matrix()</a></code> methods.
The full formula is returned so we can construct, for example, the
<code><a href='https://rdrr.io/r/stats/model.frame.html'>model.frame()</a></code> including all columns as well as the requested subset.
The full set is used to identify rows to include in the reduced model frame.

Construction of Model Frame Formula and Data Inputs — h_construct_model_frame_inputs

<dl>

<dt>formula</dt>
<dd>(<code>mmrm</code>) mmrm fit object.</dd>


<dt>data</dt>
<dd>optional data frame that will be
passed to <code><a href='https://rdrr.io/r/stats/model.frame.html'>model.frame()</a></code> or <code><a href='https://rdrr.io/r/stats/model.matrix.html'>model.matrix()</a></code></dd>


<dt>include</dt>
<dd>(<code>character</code>) names of variable to include</dd>


<dt>full</dt>
<dd>(<code>flag</code>) indicator whether to return full model frame (deprecated).</dd>

</dl>

Construction of Model Frame Formula and Data Inputs

h_construct_model_frame_inputs: Construction of Model Frame Formula and Data Inputs

Description

Usage

Value

Arguments