inst/doc/getMGroup_examples.R

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

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ---- message = FALSE---------------------------------------------------------
library(nlpsem)
mxOption(model = NULL, key = "Default optimizer", "CSOLNP", reset = FALSE)

## ---- message = FALSE---------------------------------------------------------
load(system.file("extdata", "getMGroup_examples.RData", package = "nlpsem"))

## ---- message = FALSE, eval = 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
#  xstarts <- mean(baseT)

## ---- message = FALSE, eval = FALSE-------------------------------------------
#  MGroup_Math_BLS_LGCM_f <-  getMGroup(
#    dat = RMS_dat0, grp_var = "SEX", sub_Model = "LGCM", y_var = "M", t_var = "T",
#    records = 1:9, curveFun = "BLS", intrinsic = TRUE, res_scale = list(0.1, 0.1)
#  )

## -----------------------------------------------------------------------------
Figure1 <- getFigure(
  model = MGroup_Math_BLS_LGCM_f@mxOutput, nClass = 2, cluster_TIC = NULL, grp_var = "SEX", 
  sub_Model = "LGCM", y_var = "M", curveFun = "BLS", y_model = "LGCM", t_var = "T", 
  records = 1:9, m_var = NULL, x_var = NULL, x_type = NULL, xstarts = xstarts, 
  xlab = "Month", outcome = "Mathematics"
)
show(Figure1)