getIndFS: Derive Individual Factor Scores for Each Latent Variable...

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

Derive Individual Factor Scores for Each Latent Variable Included in Model

Description

This function computes individual factor scores for each latent variable in a given model. It supports three types of factor scores: maximum likelihood, weighted maximum likelihood, and regression.

Usage

getIndFS(model, FS_type = "Regression")

Arguments

model	A fitted mxModel object. Specifically, this should be the mxOutput slot from the result returned by one of the estimation functions provided by this package.
FS_type	A string specifying the type of factor scores to compute. Supported options include "ML" (for Maximum Likelihood), "WeightedML" (for Weighted Maximum Likelihood), and "Regression". Default is "Regression".

Value

An object of class FSOutput with two slots:

• scores_est: Contains the factor score estimates.

• scores_se: Contains the standard errors of the factor score estimates.

The content of these slots can be printed using the printTable() method for S4 objects.

References

• Estabrook, R. & Neale, M. C. (2013). A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence. Multivariate Behavioral Research, 48, 1-27. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2012.730072")}

• Priestley, M. & Subba Rao, T. (1975). The Estimation of Factor Scores and Kalman Filtering For Discrete Parameter Stationary Processes. International Journal of Control, 21, 971-975. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00207177508922050")}

Examples

mxOption(model = NULL, key = "Default optimizer", "CSOLNP", reset = FALSE)
# Load ECLS-K (2011) data
data("RMS_dat")
RMS_dat0 <- RMS_dat
# Re-baseline the data so that the estimated initial status is for the starting point of the study
baseT <- RMS_dat0$T1
RMS_dat0$T1 <- RMS_dat0$T1 - baseT
RMS_dat0$T2 <- RMS_dat0$T2 - baseT
RMS_dat0$T3 <- RMS_dat0$T3 - baseT
RMS_dat0$T4 <- RMS_dat0$T4 - baseT
RMS_dat0$T5 <- RMS_dat0$T5 - baseT
RMS_dat0$T6 <- RMS_dat0$T6 - baseT
RMS_dat0$T7 <- RMS_dat0$T7 - baseT
RMS_dat0$T8 <- RMS_dat0$T8 - baseT
RMS_dat0$T9 <- RMS_dat0$T9 - baseT
# Standardized time-invariant covariates
RMS_dat0$ex1 <- scale(RMS_dat0$Approach_to_Learning)
RMS_dat0$ex2 <- scale(RMS_dat0$Attention_focus)

# Fit bilinear spline latent growth curve model (fixed knots)
LIN_LGCM <- getLGCM(
  dat = RMS_dat0, t_var = "T", y_var = "M", curveFun = "linear",
  intrinsic = FALSE, records = 1:9, growth_TIC = NULL, res_scale = 0.1
)
getIndFS(model = LIN_LGCM@mxOutput, FS_type = "Regression")
# Fit bilinear spline latent growth curve model (random knots) with time-invariant covariates for
# mathematics development
## Fit the model
BLS_LGCM.TIC_f <- getLGCM(dat = RMS_dat0, t_var = "T", y_var = "M", curveFun = "BLS",
                          intrinsic = TRUE, records = 1:9, growth_TIC = c("ex1", "ex2"),
                          res_scale = 0.1)
getIndFS(model = BLS_LGCM.TIC_f@mxOutput, FS_type = "Regression")

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