Description
ctsem is an R package for continuous time structural equation modelling of panel (N > 1)
and time series (N = 1) data, using either full information maximum likelihood (FIML) or the Kalman filter. Most
dynamic models for longitudinal data in the social and behavioural sciences are discrete time models. An assumption of
discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same
intervals. Violations of this assumption are regularly ignored due to the difficulty of accounting for varying time intervals,
therefore parameter estimates can be severely biased. By using stochastic differential equations and estimating an underlying
continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to a general purpose
SEM package (OpenMx), ctsem combines the flexible specification of structural equation models with the enhanced
data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over
time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations.
Within and between effects are estimated simultaneously by modelling both observed covariates and unobserved heterogeneity.
Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all
be simply modelled. To use ctsem, one first specifies a model using ctModel, fits this to wide format data using ctFit, then
plot (plot.ctsemFit) and summary (summary.ctsemFit) methods are available to analyse the fitted object.
ctMultigroupFit may be used in place of ctFit to specify a multi group model.
For examples, see ctFit. For more detailed information, see the vignette by running: vignette('ctsem')