Fits a multivariate (joint) group-based trajectory model for multiple longitudinal outcomes.
trajeR_mult(
formulas,
ng,
models,
Method = "L",
Risk = NULL,
TCOV = NULL,
data = NULL,
degre.nu = NULL,
degre.phi = NULL,
ssigma = TRUE,
ymax = NULL,
ymin = NULL,
paraminit = NULL,
control = list(maxit = 100),
hessian = FALSE
)An object of class trajectory.mult containing the fitted model parameters for each outcome, the interaction parameters (psi), likelihood, and convergence status.
A list of formulas, one for each longitudinal outcome (e.g., list(Y1 ~ Time, Y2 ~ Time + I(Time^2))).
A list of integers specifying the number of latent groups for each outcome.
A character vector of distribution models for each outcome (e.g., c("CNORM", "LOGIT")). Supported models are "CNORM", "LOGIT", "ZIP", "BETA", "POIS".
The estimation method. "L" for Likelihood or "EM" for Expectation-Maximization.
An optional one-sided formula for global risk factors that influence group membership (e.g., ~ X1 + X2).
Formula or Matrix. Optional time-dependent covariates.
data.frame. The dataset containing all variables.
A list or vector specifying the polynomial degree for the zero-inflation part of "ZIP" models.
A list or vector specifying the polynomial degree for the precision parameter (phi) of "BETA" models.
Logical. If TRUE, assumes a common standard deviation across groups for "CNORM" models. Can be a vector of logicals, one for each CNORM model. Default is TRUE.
A list or vector of maximum values for censoring in "CNORM" models.
A list or vector of minimum values for censoring in "CNORM" models.
An optional list of numeric vectors for user-defined initial parameters. The list should have one element per model, plus an optional last element for the psi parameters.
A list of control parameters for the optimization algorithm (e.g., list(maxit=100)).
Logical. If TRUE, computes the Hessian matrix to estimate standard errors. Default is FALSE.