ctStanKalman

fit object from <code><a rd-options="" href="/link/ctStanFit?package=ctsem&version=3.0.4" data-mini-rdoc="ctsem::ctStanFit">ctStanFit</a></code>.

either NA (to extract all) or a positive integer from 1 to maximum samples in the fit.

nsamples

function to apply over samples, such as <code>mean</code>

collapsefunc

Integer number of cpu cores to use. Only needed if savescores was set to FALSE when fitting.

cores

additional arguments to collpsefunc.

Get Kalman filter estimates from a ctStanFit object

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.
Using the original ctsem formulation based on OpenMx, described in the JSS paper
"Continuous Time Structural Equation Modeling with R Package ctsem", with updated version
as CRAN vignette <https://cran.r-project.org/web/packages/ctsem/vignettes/ctsem.pdf> ,
linear mixed effects SDE's estimated via maximum likelihood and optimization are possible.
Using the Stan based formulation, described in <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> ,
nonlinearity (state dependent parameters) and random effects on all parameters
are possible, using either optimization (with optional importance sampling) or
Stan's Hamiltonian Monte Carlo sampling. 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.

Charles Driver

ctsem

Continuous Time Structural Equation Modelling

Manuel Voelkle

Han Oud

Trustees of Columbia University 

ctStanKalman function

fit object from <code><a rd-options='' href='ctStanFit'>ctStanFit</a></code>.

ctStanKalman: Get Kalman filter estimates from a ctStanFit object

Description

Usage

Arguments

Value

Examples