ctsem (version 3.0.4)

ctKalman: 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 medians of parameter distribution.

Usage

ctKalman(fit, datalong = NULL, timerange = "asdata",
  timestep = "asdata", subjects = 1, plot = FALSE, ...)

Arguments

fit

fit object as generated by ctStanFit.

datalong

Optional long format data object as used by ctStanFit. If not included, data from fit will used.

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. Currently unused for ctStan fits.

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. Currently unavailable for ctStan fits.

subjects

vector of integers denoting which subjects (from 1 to N) to plot predictions for.

plot

Logical. If TRUE, plots output instead of returning it. See plot.ctKalman for the possible arguments.

...

additional arguments to pass to plot.ctKalman.

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

# NOT RUN {
#Basic
ctKalman(ctstantestfit, timerange=c(0,60), timestep=.5, plot=TRUE)

#Multiple subjects, y and yprior, showing plot arguments
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
  ltyvec="auto",
  colvec='auto', 
  lwdvec='auto', 
  subsetindices=2, #Only plotting 2nd dimension of y and yprior
  pchvec='auto', typevec='auto',grid=TRUE,legend=TRUE,
  plotcontrol=list(xlim=c(0,55),main='Observations and priors'),
  polygoncontrol=list(steps=5))
  
# }