ctKalman: ctKalman

In ctsem: Continuous Time Structural Equation Modelling

ctKalman

Description

Outputs predicted, updated, and smoothed estimates of manifest indicators and latent states, with covariances, for specific subjects from data fit with ctStanFit, based on either the mode (if optimized) or mean (if sampled) of parameter distribution.

Usage

ctKalman(
  fit,
  timerange = "asdata",
  timestep = "auto",
  subjects = fit$standata$idmap[1, 1],
  removeObs = FALSE,
  plot = FALSE,
  standardisederrors = FALSE,
  realid = TRUE,
  ...
)

Arguments

fit	fit object as generated by ctStanFit.
timerange	Either 'asdata' to just use the observed data range, or a numeric vector of length 2 denoting start and end of time range, allowing for estimates outside the range of observed data. Ranges smaller than the observed data are ignored.
timestep	Either 'asdata' to just use the observed data (which also requires 'asdata' for timerange) or a positive numeric value indicating the time step to use for interpolating values. Lower values give a more accurate / smooth representation, but take a little more time to calculate.
subjects	vector of integers denoting which subjects (from 1 to N) to plot predictions for.
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.
plot	Logical. If TRUE, plots output instead of returning it. See plot.ctKalmanDF (Stan based fit) for the possible arguments.
standardisederrors	if TRUE, also include standardised error output (based on covariance per time point).
realid	use original (not necessarily integer sequence) subject id's? Otherwise use integers 1:N.
...	additional arguments to pass to plot.ctKalmanDF.

Value

Returns a list containing matrix objects etaprior, etaupd, etasmooth, y, yprior, yupd, ysmooth, prederror, time, loglik, with values for each time point in each row. eta refers to latent states and y to manifest indicators - y itself is thus just the input data. Covariance matrices etapriorcov, etaupdcov, etasmoothcov, ypriorcov, yupdcov, ysmoothcov, are returned in a row * column * time array. Some outputs are unavailable for ctStan fits at present. If plot=TRUE, nothing is returned but a plot is generated.

Examples

#Basic
ctKalman(ctstantestfit, timerange=c(0,60), plot=TRUE)

#Multiple subjects, y and yprior, showing plot arguments
plot1<-ctKalman(ctstantestfit, timerange=c(0,60), timestep=.1, plot=TRUE,
  subjects=2:3, 
  kalmanvec=c('y','yprior'),
  errorvec=c(NA,'ypriorcov')) #'auto' would also have achieved this
  
 #modify plot as per normal with ggplot
 print(plot1+ggplot2::coord_cartesian(xlim=c(0,10)))
 
 #or generate custom plot from scratch:#'  
 k=ctKalman(ctstantestfit, timerange=c(0,60), timestep=.1, subjects=2:3)
 library(ggplot2)
 ggplot(k[k$Element %in% 'yprior',],
   aes(x=Time, y=value,colour=Subject,linetype=Row)) +
   geom_line() +
   theme_bw()

 

ctsem documentation built on June 30, 2025, 5:06 p.m.