ctModel: Define a ctsem model

In ctsem: Continuous Time Structural Equation Modelling

Define a ctsem model

Description

This function is used to specify a continuous time structural equation model, which can then be fit to data with function ctFit.

Usage

ctModel(
  LAMBDA,
  type = "ct",
  n.manifest = "auto",
  n.latent = "auto",
  Tpoints = NULL,
  manifestNames = "auto",
  manifesttype = rep(0, nrow(LAMBDA)),
  latentNames = "auto",
  id = "id",
  time = "time",
  silent = FALSE,
  T0VAR = "auto",
  T0MEANS = "auto",
  MANIFESTMEANS = "auto",
  MANIFESTVAR = "diag",
  DRIFT = "auto",
  CINT = 0,
  DIFFUSION = "auto",
  n.TDpred = "auto",
  TDpredNames = "auto",
  TDPREDEFFECT = "auto",
  TDPREDMEANS = "auto",
  TDPREDVAR = "auto",
  n.TIpred = "auto",
  TIpredNames = "auto",
  tipredDefault = TRUE,
  PARS = NULL
)

Arguments

LAMBDA	n.manifest*n.latent loading matrix relating latent to manifest variables, with latent processes 1:n.latent along the columns, and manifest variables 1:n.manifest in the rows.
type	character string. Use 'ct' (continuous time) or 'dt' (discrete time) for Stan-based fitting with ctFit. These return a modern ctsem model object whose pars data frame can also be edited through the pars-backed matrices view. For legacy workflows, 'omx' returns a matrix-list model object that can be edited directly and then converted via ctModelConvertOMX.
n.manifest	Number of manifest indicators per individual at each measurement occasion / time point. Manifest variables are included as the first element of the wide data matrix, with all the 1:n.manifest manifest variables at time 1 followed by those of time 2, and so on.
n.latent	Number of latent processes.
Tpoints	Number of time points, or measurement occasions, in the data. This will generally be the maximum number of time points for a single individual, but may be one extra if sample relative time intervals are used, see ctIntervalise.
manifestNames	n.manifest length vector of manifest variable names as they appear in the data structure, without any _Tx time point suffix that may be present in wide data. Defaults to Y1, Y2, etc.
manifesttype	n.manifest length vector of manifest variable types,defaults to 0 for continuous vars, 1 for binary vars is also possible.
latentNames	n.latent length vector of latent variable names (used for naming parameters, defaults to eta1, eta2, etc).
id	character string denoting column name containing subject identification variables. id data may be of any form, though will be coerced internally to an integer sequence rising from 1.
time	character string denoting column name containing timing data. Timing data must be numeric.
silent	Suppress all output to console.
T0VAR	lower triangular n.latent*n.latent cholesky matrix of latent process initial variance / covariance. "auto" freely estimates all parameters.
T0MEANS	n.latent*1 matrix of latent process means at first time point, T0. "auto" freely estimates all parameters.
MANIFESTMEANS	n.manifest*1 matrix of manifest intercept parameters. "auto" frees all parameters.
MANIFESTVAR	lower triangular n.manifest*n.manifest cholesky matrix of variance / covariance between manifests at each measurement occasion (i.e. measurement error / residual). "auto" freely estimates variance parameters, and fixes covariances between manifests to 0. "free" frees all values, including covariances.
DRIFT	n.latent*n.latent DRIFT matrix of continuous auto and cross effects, relating the processes over time. "auto" freely estimates all parameters.
CINT	n.latent * 1 matrix of latent process intercepts, allowing for non 0 asymptotic levels of the latent processes. Generally only necessary for additional trends and more complex dynamics.
DIFFUSION	lower triangular n.latent*n.latent cholesky matrix of diffusion process variance and covariance (latent error / dynamic innovation). "auto" freely estimates all parameters.
n.TDpred	Number of time dependent predictor variables in the dataset.
TDpredNames	n.TDpred length vector of time dependent predictor variable names, as they appear in the data structure, without any _Tx time point suffix that may appear in wide data. Default names are TD1, TD2, etc.
TDPREDEFFECT	n.latent*n.TDpred matrix of effects from time dependent predictors to latent processes. Effects from 1:n.TDpred columns TDpredictors go to 1:n.latent rows of latent processes. "auto" freely estimates all parameters.
TDPREDMEANS	Legacy argument for time-dependent predictor means, mainly to support data generation workflows. Expected dimensions are (n.TDpred * Tpoints) rows and 1 column. "auto" creates a free-label matrix.
TDPREDVAR	Legacy argument for time-dependent predictor covariance structure, mainly to support data generation workflows. Expected dimensions are (n.TDpred * Tpoints) square. "auto" creates a free-label matrix.
n.TIpred	Number of time independent predictors. Each TIpredictor is inserted at the right of the data matrix, after the time intervals.
TIpredNames	n.TIpred length vector of time independent predictor variable names, as they appear in the data structure. Default names are TI1, TI2, etc.
tipredDefault	Logical. TRUE sets any parameters with unspecified time independent predictor effects to have effects estimated, FALSE fixes the effect to zero unless individually specified.
PARS	for types 'ct' and 'dt' only. May be of any structure, only needed to contain extra parameters for certain non-linear models.

Examples

 ### Frequentist example:
 ### impulse and level change time dependent predictor 
 ### example from Driver, Oud, Voelkle (2015)
 data('ctExample2')
 tdpredmodel <- ctModel(type='omx', n.manifest = 2, n.latent = 3, n.TDpred = 1, 
 Tpoints = 8, manifestNames = c('LeisureTime', 'Happiness'), 
 TDpredNames = 'MoneyInt', 
 latentNames = c('LeisureTime', 'Happiness', 'MoneyIntLatent'),
 LAMBDA = matrix(c(1,0, 0,1, 0,0), ncol = 3))

 tdpredmodel$DIFFUSION[, 3] <- 0
 tdpredmodel$DIFFUSION[3, ] <- 0
 tdpredmodel$T0VAR[3, ] <- 0
 tdpredmodel$T0VAR[, 3] <- 0
 tdpredmodel$CINT[3] <- 0
 tdpredmodel$T0MEANS[3] <- 0
 tdpredmodel$DRIFT[3, ] <- 0
 
 
###Bayesian example:
model<-ctModel(type='ct',
n.latent=2, latentNames=c('eta1','eta2'),
n.manifest=2, manifestNames=c('Y1','Y2'),
n.TDpred=1, TDpredNames='TD1', 
n.TIpred=3, TIpredNames=c('TI1','TI2','TI3'),
LAMBDA=diag(2))

ctsem documentation built on June 30, 2026, 5:07 p.m.