nlpsem (version 0.3)

getIndFS: 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")

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.

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".

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. 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. 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)
# \donttest{
# 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")
# }