latent_covariates

<p>Simulated dataset for use in examples and testing with a latent covariate
interacting with an observed covariate.</p>

datasets

Estimates generalized additive latent and
mixed models using maximum marginal likelihood,
as defined in Sorensen et al. (2023)
<doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
Skrondal (2004)'s unifying framework for multilevel latent variable
modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse
matrix methods, Laplace approximation, and automatic differentiation. The
framework includes generalized multilevel models with heteroscedastic
residuals, mixed response types, factor loadings, smoothing splines,
crossed random effects, and combinations thereof. Syntax for model
formulation is close to 'lme4' (Bates et al. (2015)
<doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019)
<doi:10.1080/00273171.2018.1516541>).

Oystein Sorensen

galamm

Generalized Additive Latent and Mixed Models

Øystein Sørensen

Douglas Bates

Ben Bolker

Martin Maechler

Allan Leal

Fabian Scheipl

Steven Walker

Simon Wood

latent_covariates function


<div class="section" id="latent-covariates-a-data-frame-with-rows-and-columns-">
<h3><code>latent_covariates</code> A data frame with 600 rows and 5 columns:</h3><p></p><p>
<dl>
<dt>id</dt>
<dd><p>Subject ID.</p></dd></dl></p><p><dt>type</dt>
<dd><p>Type of observation in the <code>y</code> variable.
If it equals <code>"measurement1"</code> or <code>"measurement2"</code> then the
observation is a measurement of the latent variable. If it equals
<code>"response"</code>, then the observation is the actual response.</p></dd></p><p><dt>x</dt>
<dd><p>Explanatory variable.</p></dd></p><p><dt>y</dt>
<dd><p>Observed response. Note, this includes both the actual response,
and the measurements of the latent variable, since mathematically they
are all treated as responses.</p></dd></p><p><dt>response</dt>
<dd><p>Dummy variable indicating whether the given row is a
response or not.</p></dd></p><p>
</p><p></p></div>


Format

Simulated Data with Latent and Observed Covariates Interaction — latent_covariates


<div class='section' id='latent-covariates-a-data-frame-with-rows-and-columns-'>
<h3><code>latent_covariates</code> A data frame with 600 rows and 5 columns:</h3>


<dl>
<dt>id</dt>
<dd><p>Subject ID.</p></dd>

<dt>type</dt>
<dd><p>Type of observation in the <code>y</code> variable.
If it equals <code>"measurement1"</code> or <code>"measurement2"</code> then the
observation is a measurement of the latent variable. If it equals
<code>"response"</code>, then the observation is the actual response.</p></dd>

<dt>x</dt>
<dd><p>Explanatory variable.</p></dd>

<dt>y</dt>
<dd><p>Observed response. Note, this includes both the actual response,
and the measurements of the latent variable, since mathematically they
are all treated as responses.</p></dd>

<dt>response</dt>
<dd><p>Dummy variable indicating whether the given row is a
response or not.</p></dd>


</dl>

</div>


Simulated Data with Latent and Observed Covariates Interaction

latent_covariates: Simulated Data with Latent and Observed Covariates Interaction

Description

Usage

Arguments

Format

`latent_covariates` A data frame with 600 rows and 5 columns:

See Also

Description

Usage

Arguments

Format

latent_covariates A data frame with 600 rows and 5 columns:

See Also

`latent_covariates` A data frame with 600 rows and 5 columns: