getMIX_UNI.loadings function

Get Factor Loadings for a Mixture Model or Multiple Group Model with Univariate Longitudinal Outcome

This function specifies the factor loadings for a mixture model with a univariate longitudinal outcome. The longitudinal outcome is fit by a Latent Growth Curve Model or a Latent Change Score Model.

getMIX_UNI.loadings(
  nClass,
  y_model,
  t_var,
  records,
  y_var,
  curveFun,
  intrinsic
)

Arguments

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

• y_model: A string specifying how to fit the longitudinal outcome. Supported values are "LGCM" and "LCSM". It takes the value passed from getMIX() or getMGroup().

• t_var: A string specifying the prefix of the column names corresponding to the time variable at each study wave. It takes the value passed from getMIX() or getMGroup().

• records: A numeric vector specifying indices of the study waves. It takes the value passed from getMIX() or getMGroup().

• y_var: A string specifying the prefix of the column names corresponding to the outcome variable at each study wave. It takes the value passed from getMIX() or getMGroup().

• 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() or getMGroup().

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

Returns

A list containing the specification of definition variables (i.e., individual time points for the latent growth curve models, and individual time points and individual time lags (intervals) between adjacent time points for latent change score models) and factor loadings of a univariate longitudinal outcome.