ctModelCoverage_check

Performs a coverage check analysis by generating data from a model, fitting it multiple times
with different fit arguments, and plotting the results.

Hierarchical continuous (and discrete) time state space modelling, for linear
and nonlinear systems measured by continuous variables, with limited support for
binary data. The subject specific dynamic system is modelled as a stochastic
differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models.
Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default.
Nonlinearities, (state dependent parameters) and random effects on all parameters
are possible, using either max likelihood / max a posteriori optimization
(with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling.
See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf>
for details. See <https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial.
Priors may be used. For the conceptual overview of the hierarchical Bayesian
linear SDE approach,
see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>.
Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> .
<https://cdriver.netlify.app/> contains some tutorial blog posts.

Charles Driver

ctsem

Continuous Time Structural Equation Modelling

Manuel Voelkle

Han Oud

Trustees of Columbia University 

ctModelCoverage_check function

<dl><dt>initialData</dt>
<dd>An initial dataset to fit to determine 'true' parameters for further generation.</dd>
<dt>fitting_model</dt>
<dd>A ctModel object used for fitting the data</dd>
<dt>niter</dt>
<dd>Number of iterations to run</dd>
<dt>fit_args</dt>
<dd>Named list of fit argument sets to test 
(e.g., list(boot = list(optimcontrol = list(bootstrapUncertainty = TRUE)), 
hess = list(optimcontrol = list(bootstrapUncertainty = FALSE))))</dd>
<dt>cores</dt>
<dd>Number of cores to use for parallel processing</dd>
<dt>plot_every</dt>
<dd>Print plots every n iterations (default = 10)</dd></dl>

Arguments

Coverage Check Function — ctModelCoverage_check

<dl>

<dt>initialData</dt>
<dd>An initial dataset to fit to determine 'true' parameters for further generation.</dd>


<dt>fitting_model</dt>
<dd>A ctModel object used for fitting the data</dd>


<dt>niter</dt>
<dd>Number of iterations to run</dd>


<dt>fit_args</dt>
<dd>Named list of fit argument sets to test 
(e.g., list(boot = list(optimcontrol = list(bootstrapUncertainty = TRUE)), 
hess = list(optimcontrol = list(bootstrapUncertainty = FALSE))))</dd>


<dt>cores</dt>
<dd>Number of cores to use for parallel processing</dd>


<dt>plot_every</dt>
<dd>Print plots every n iterations (default = 10)</dd>

</dl>

ctModelCoverage_check: Coverage Check Function

Description

Usage

Value

Arguments