getMULTI.loadings: Get Factor Loadings for a Multivariate Longitudinal Outcomes...

In nlpsem: Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Get Factor Loadings for a Multivariate Longitudinal Outcomes with Specified Functional Curves

Description

This function specifies the factor loadings for a multivariate longitudinal outcomes with a given functional form. The longitudinal outcomes are fit by Latent Growth Curve Models or a Latent Change Score Models.

Usage

getMULTI.loadings(y_model, t_var, y_var, curveFun, intrinsic, records)

Arguments

y_model	A string specifying how to fit the longitudinal outcome. Supported values are "LGCM" and "LCSM". It takes the value passed from getMGM().
t_var	A vector of strings, with each element representing the prefix for column names related to the time variable for the corresponding outcome variable at each study wave. It takes the value passed from getMGM().
y_var	A vector of strings, with each element representing the prefix for column names corresponding to a particular outcome variable at each study wave. It takes the value passed from getMGM().
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 getMGM().
intrinsic	A logical flag indicating whether to build an intrinsically nonlinear longitudinal model. It takes the value passed from getMGM().
records	A list of numeric vectors, with each vector specifying the indices of the observed study waves for the corresponding outcome variable. It takes the value passed from getMGM().

Value

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 multivariate longitudinal outcomes.

nlpsem documentation built on Sept. 13, 2023, 1:06 a.m.