ctLOO

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.

folds

cores

compute folds in parallel or use cores to finish single folds faster.

parallelFolds

drop random subjects instead of data rows?

subjectwise

do not drop first observation (more stable estimates)

keepfirstobs

K fold cross validation for ctStanFit objects

Hierarchical continuous 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), 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.

ctLOO: K fold cross validation for ctStanFit objects

Description

Usage

Arguments

Value

Examples