ctLOO

K fold cross validation for ctStanFit objects

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. 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> .
Stan based functions are not available on 32 bit Windows systems at present.
<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 

ctLOO function

<dl><dt>fit</dt>
<dd>ctStanfit object</dd>
<dt>folds</dt>
<dd>Number of cross validation splits to use -- 10 folds implies that the 
model is re-fit 10 times, each time to a data set with 1/10 of the observations randomly removed.</dd>
<dt>cores</dt>
<dd>Number of processor cores to use.</dd>
<dt>parallelFolds</dt>
<dd>compute folds in parallel or use cores to finish single folds faster. 
parallelFolds will use folds times as much memory.</dd>
<dt>subjectwise</dt>
<dd>drop random subjects instead of data rows?</dd>
<dt>keepfirstobs</dt>
<dd>do not drop first observation (more stable estimates)</dd></dl>

Arguments

K fold cross validation for ctStanFit objects — ctLOO

<dl>

<dt>fit</dt>
<dd>ctStanfit object</dd>


<dt>folds</dt>
<dd>Number of cross validation splits to use -- 10 folds implies that the 
model is re-fit 10 times, each time to a data set with 1/10 of the observations randomly removed.</dd>


<dt>cores</dt>
<dd>Number of processor cores to use.</dd>


<dt>parallelFolds</dt>
<dd>compute folds in parallel or use cores to finish single folds faster. 
parallelFolds will use folds times as much memory.</dd>


<dt>subjectwise</dt>
<dd>drop random subjects instead of data rows?</dd>


<dt>keepfirstobs</dt>
<dd>do not drop first observation (more stable estimates)</dd>

</dl>

ctLOO: K fold cross validation for ctStanFit objects

Description

Usage

Value

Arguments

Examples