Usage
ctStanFit(datalong, ctstanmodel, stanmodeltext = NA, iter = 2000, kalman = TRUE, binomial = FALSE, esthyper = TRUE, fit = TRUE, stationary = FALSE, plot = FALSE, diffusionindices = "all", asymdiffusion = FALSE, optimize = FALSE, vb = FALSE, chains = 1, inits = NULL, initwithoptim = FALSE, control = list(adapt_delta = 0.9, adapt_init_buffer = 30, adapt_window = 2, max_treedepth = 10, stepsize = 0.001), verbose = FALSE, ...)
Arguments
datalong
long format data containing columns for subject id (numeric values, 1 to max subjects), manifest variables,
any time dependent (i.e. varying within subject) predictors,
and any time independent (not varying within subject) predictors.
ctstanmodel
model object as generated by ctModel with type='stanct' or 'standt', for continuous or discrete time
models respectively. stanmodeltext
already specified Stan model character string, generally leave NA unless modifying Stan model directly.
(Possible after modification of output from fit=FALSE)
iter
number of iterations, half of which will be devoted to warmup by default when sampling.
When optimizing, this is the maximum number of iterations to allow -- convergence hopefully occurs before this!
kalman
logical indicating whether or not to integrate over latent states using a Kalman filter.
Generally recommended to set TRUE unless using non-gaussian measurement model. If not using Kalman filter, experience
suggests that some models / datasets require a relatively high amount of very fast iterations before the sampler is
in the high density region. This can make it difficult to determine the number of iterations needed a priori - in such cases
setting initwithoptim=TRUE may be helpful.
binomial
logical indicating the use of binomial rather than Gaussian data, as with IRT analyses.
esthyper
Logical indicating whether to explictly estimate distributions for any individually varying
parameters, or to fix the distributions to maximum likelihood estimates conditional on subject parameters.
fit
If TRUE, fit specified model using Stan, if FALSE, return stan model object without fitting.
stationary
Logical. If TRUE, T0VAR and T0MEANS input matrices are ignored,
the parameters are instead fixed to long run expectations
plot
if TRUE, a Shiny program is launched upon fitting to interactively plot samples.
May struggle with many (e.g., > 5000) parameters, and may leave sample files in working directory if sampling is terminated.
diffusionindices
vector of integers denoting which latent variables are involved in covariance calculations.
latents involved only in deterministic trends or input effects can be removed from matrices, speeding up calculations.
If unsure, leave default of 'all' ! Ignored if kalman=FALSE.
asymdiffusion
if TRUE, increases fitting speed at cost of model flexibility - T0VAR in the model specification is ignored, the DIFFUSION matrix specification is used as the
asymptotic DIFFUSION matrix (Q*_inf in the vignette / paper) (making it difficult if not impossible to properly specify
higher order processes). The speed increases come about because the internal Kalman filter routine has many steps removed, and the
asymptotic diffusion parameters are less dependent on the DRIFT matrix.
optimize
if TRUE, use Stan's optimizer for maximum a posteriori estimates.
Setting this also sets esthyper=FALSE
vb
if TRUE, use Stan's variational approximation. Rudimentary testing suggests it is not accurate
for many ctsem models at present.
chains
number of chains to sample.
inits
vector of parameter start values, as returned by the rstan function unconstrain_pars for instance. initwithoptim
Logical. If TRUE, the model, with population standard deviations fixed to 1
(so approx 65
for the chains. This can help speed convergence and avoid problematic regions for certain problems.
control
List of arguments sent to stan control argument, regarding warmup / sampling behaviour. verbose
Logical. If TRUE, prints log probability at each iteration.
...
additional arguments to pass to stan function.