ctStanKalman: Get Kalman filter estimates from a ctStanFit object

In ctsem: Continuous Time Structural Equation Modelling

Get Kalman filter estimates from a ctStanFit object

Description

Get Kalman filter estimates from a ctStanFit object

Usage

ctStanKalman(
  fit,
  nsamples = NA,
  pointest = TRUE,
  collapsefunc = NA,
  cores = 1,
  subjects = 1:max(fit$standata$subject),
  timestep = "asdata",
  timerange = "asdata",
  standardisederrors = FALSE,
  subjectpars = TRUE,
  tformsubjectpars = TRUE,
  indvarstates = FALSE,
  removeObs = F,
  ...
)

Arguments

fit	fit object from ctStanFit.
nsamples	either NA (to extract all) or a positive integer from 1 to maximum samples in the fit.
pointest	If TRUE, uses the posterior mode as the single sample.
collapsefunc	function to apply over samples, such as mean
cores	Integer number of cpu cores to use. Only needed if savescores was set to FALSE when fitting.
subjects	integer vector of subjects to compute for.
timestep	Either a positive numeric value, 'asdata' to use the times in the dataset, or 'auto' to select a timestep automatically (resulting in some interpolation but not excessive computation).
timerange	only relevant if timestep is not 'asdata'. Positive numeric vector of length 2 denoting time range for computations.
standardisederrors	If TRUE, computes standardised errors for prior, upd, smooth conditions.
subjectpars	if TRUE, state estimates are not returned, instead, predictions of each subjects parameters are returned, for parameters that had random effects specified.
tformsubjectpars	if FALSE, subject level parameters are returned in raw, pre transformation form.
indvarstates	if TRUE, do not remove indvarying states from output
removeObs	Logical or integer. If TRUE, observations (but not covariates) are set to NA, so only expectations based on parameters and covariates are returned. If a positive integer N, every N observations are retained while others are set NA for computing model expectations – useful for observing prediction performance forward further in time than one observation.
...	additional arguments to collpsefunc.

Value

list containing Kalman filter elements, each element in array of iterations, data row, variables. llrow is the log likelihood for each row of data.

Examples

k=ctStanKalman(ctstantestfit,subjectpars=TRUE,collapsefunc=mean)

ctsem documentation built on Sept. 11, 2024, 9:06 p.m.