getMIX.mxModel function

Construct An Object of mxModel for Longitudinal Mixture Models To Be Evaluated

This function builds up an object of mxModel for a mixture model with user-specified number of latent classes, class- specific models and functional form (including whether intrinsically nonlinear).

getMIX.mxModel(
  dat,
  nClass,
  sub_Model,
  cluster_TIC,
  t_var,
  y_var,
  curveFun,
  intrinsic,
  records,
  y_model,
  m_var,
  x_var,
  x_type,
  TVC,
  decompose,
  growth_TIC,
  starts,
  res_cor
)

Arguments

• dat: A wide-format data frame, with each row corresponding to a unique ID. It contains the observed variables with repeated measurements and occasions for each longitudinal process, and time-invariant covariates (TICs) if any. It takes the value passed from getMIX().

• nClass: An integer specifying the number of latent classes for the mixture model. It takes the value passed from getMIX().

• sub_Model: A string that specifies the sub-model for latent classes. Supported sub-models include "LGCM" (for latent growth curve models), "LCSM" (for latent change score models), "TVC" (for latent growth curve models or latent change score models with a time-varying covariate), "MGM" (for multivariate latent growth curve models or latent change score models), and "MED" (for longitudinal mediation models). It takes the value passed from getMIX().

• cluster_TIC: A string or character vector representing the column name(s) for time-invariant covariate(s) indicating cluster formations. It takes the value passed from getMIX().

• t_var: A string specifying the prefix of the column names corresponding to the time variable for each study wave. This applies when sub_Model is "LGCM", "LCSM" or "TVC". For sub_Model being "MGM" or "MED", t_var should be a string vector where each element corresponds to the time variable prefix for each respective longitudinal process. It takes the value passed from getMIX().

• y_var: A string defining the prefix of the column names corresponding to the outcome variable for each study wave. This is applicable when sub_Model is not "MGM". For sub_Model being "MGM", y_var should be a string vector where each element corresponds to the prefix of the column names for each outcome variable across the study waves. It takes the value passed from getMIX().

• curveFun: A string specifying the functional form of the growth curve. Supported options for y_model = "LGCM" include: "linear" (or "LIN"), "quadratic" (or "QUAD"), "negative exponential"

  (or "EXP"), "Jenss-Bayley" (or "JB"), and "bilinear spline" (or "BLS"). Supported options for y_model = "LCSM" include: "quadratic" (or "QUAD"), "negative exponential" (or "EXP"), "Jenss-Bayley" (or "JB"), and "nonparametric" (or "NonP"). It takes the value passed from getMIX().

• intrinsic: A logical flag indicating whether to build an intrinsically nonlinear longitudinal model. It takes the value passed from getMIX().

• records: A numeric vector denoting the indices of the observed study waves. This applies when sub_Model is "LGCM", "LCSM" or "TVC". For sub_Model being "MGM" or "MED", records should be a list of numeric vectors, where each vector provides the indices of the observed study waves for each longitudinal process. It takes the value passed from getMIX().

• y_model: A string that specifies how to fit longitudinal outcomes. Supported values are "LGCM" and "LCSM". It takes the value passed from getMIX().

• m_var: A string that specifies the prefix of the column names corresponding to the mediator variable at each study wave. It takes the value passed from getMIX().

• x_var: A string specifying the baseline predictor if x_type = "baseline", or the prefix of the column names corresponding to the predictor variable at each study wave if x_type = "longitudinal". It takes the value passed from getMIX().

• x_type: A string indicating the type of predictor variable used in the model. Supported values are "baseline"

  and "longitudinal". It takes the value passed from getMIX().

• TVC: A string that specifies the prefix of the column names corresponding to the time-varying covariate at each time point. It takes the value passed from getMIX().

• decompose: An integer specifying the decomposition option for temporal states. Supported values include 0 (no decomposition), 1 (decomposition with interval-specific slopes as temporal states), 2 (decomposition with interval- specific changes as temporal states), and 3 (decomposition with change-from-baseline as temporal states). It takes the value passed from getMIX().

• growth_TIC: A string or character vector of column names of time-invariant covariate(s) accounting for the variability of growth factors if any. It takes the value passed from getMIX().

• starts: A list of initial values for the parameters, either takes the value passed from getMIX() or derived by the helper function getMIX.initial().

• res_cor: A list where each element is a (vector of) numeric initial value(s) for residual correlation in each class. It needs to be specified if the sub_Model is "TVC" (when decompose != 0), "MGM", or "MED". It takes the value passed from getMIX().

Returns

A pre-optimized mxModel for a longitudinal mixture model.