cognition

Simulated dataset mimicking the measurement of abilities in three cognitive
domains. The latent traits (cognitive ability in a given domain) are based on
the functions in <code><a href="/link/mgcv%3A%3AgamSim?package=galamm&version=0.2.3" data-mini-rdoc="galamm::mgcv::gamSim">mgcv::gamSim</a></code>
woodGeneralizedAdditiveModels2017galamm, and depend on the
explanatory variable x.

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

cognition function

<div class="section" id="cognition-a-data-frame-with-rows-and-columns-">
<h3><code>cognition</code> A data frame with 14400 rows and 7 columns:</h3>
<dl>
<dt>id</dt>
<dd>Subject ID.</dd></dl><dt>domain</dt>
<dd>Factor variable denoting the cognitive domain.</dd><dt>x</dt>
<dd>Explanatory variable.</dd><dt>timepoint</dt>
<dd>Factor variable denoting the timepoint.</dd><dt>item</dt>
<dd>Factor variable denoting the item within the tests of each
cognitive domain.</dd><dt>trials</dt>
<dd>Number of trials, if applicable.</dd><dt>y</dt>
<dd>Response variable. For domain 1 a real number, for domain 2 a
binomially distributed variable based on a single trial, for
domain 3 a real number.</dd>
</div>

Format

Simulated dataset mimicking the measurement of abilities in three cognitive
domains. The latent traits (cognitive ability in a given domain) are based on
the functions in <code><a href='https://rdrr.io/pkg/mgcv/man/gamSim.html'>mgcv::gamSim</a></code>
woodGeneralizedAdditiveModels2017galamm, and depend on the
explanatory variable x.

Simulated Data with Measurements of Cognitive Abilities — cognition

<div class='section' id='cognition-a-data-frame-with-rows-and-columns-'>
<h3><code>cognition</code> A data frame with 14400 rows and 7 columns:</h3>


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

<dt>domain</dt>
<dd>Factor variable denoting the cognitive domain.</dd>

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

<dt>timepoint</dt>
<dd>Factor variable denoting the timepoint.</dd>

<dt>item</dt>
<dd>Factor variable denoting the item within the tests of each
cognitive domain.</dd>

<dt>trials</dt>
<dd>Number of trials, if applicable.</dd>

<dt>y</dt>
<dd>Response variable. For domain 1 a real number, for domain 2 a
binomially distributed variable based on a single trial, for
domain 3 a real number.</dd>


</dl>

</div>

cognition: Simulated Data with Measurements of Cognitive Abilities

Description

Usage

Arguments

Format

`cognition` A data frame with 14400 rows and 7 columns:

References

See Also

Description

Usage

Arguments

Format

cognition A data frame with 14400 rows and 7 columns:

References

See Also

`cognition` A data frame with 14400 rows and 7 columns: