inst/doc/getMIX_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", "getMIX_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$T9 - baseT
#  # Standardize time-invariant covariates (TICs)
#  ## ex1 and ex2 are standardized growth TICs in models
#  RMS_dat0$ex1 <- scale(RMS_dat0$Approach_to_Learning)
#  RMS_dat0$ex2 <- scale(RMS_dat0$Attention_focus)
#  ## gx1 and gx2 are standardized cluster TICs in models
#  RMS_dat0$gx1 <- scale(RMS_dat0$INCOME)
#  RMS_dat0$gx2 <- scale(RMS_dat0$EDU)
#  xstarts <- mean(baseT)

## ---- message = FALSE, eval = FALSE-------------------------------------------
#  Math_BLS_LGCM1 <- getLGCM(
#    dat = RMS_dat0, t_var = "T", y_var = "M", curveFun = "BLS", intrinsic = FALSE,
#    records = 1:9, res_scale = 0.1
#    )
#  Math_BLS_LGCM2 <- getMIX(
#    dat = RMS_dat0, prop_starts = c(0.45, 0.55), sub_Model = "LGCM", y_var = "M",
#    t_var = "T", records = 1:9, curveFun = "BLS", intrinsic = FALSE,
#    res_scale = list(0.3, 0.3)
#  )
#  set.seed(20191029)
#  Math_BLS_LGCM3 <- getMIX(
#    dat = RMS_dat0, prop_starts = c(0.33, 0.34, 0.33), sub_Model = "LGCM", y_var = "M",
#    t_var = "T", records = 1:9, curveFun = "BLS", intrinsic = FALSE,
#    res_scale = list(0.3, 0.3, 0.3), tries = 10
#  )

## -----------------------------------------------------------------------------
Figure1 <- getFigure(
  model = Math_BLS_LGCM1@mxOutput, nClass = NULL, cluster_TIC = NULL, 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)
Figure2 <- getFigure(
  model = Math_BLS_LGCM2@mxOutput, nClass = 2, cluster_TIC = NULL, 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(Figure2)
Figure3 <- getFigure(
  model = Math_BLS_LGCM3@mxOutput, nClass = 3, cluster_TIC = NULL, 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(Figure3)
getSummary(model_list = list(Math_BLS_LGCM1@mxOutput, Math_BLS_LGCM2@mxOutput, Math_BLS_LGCM3@mxOutput),
           HetModels = TRUE)

## ---- message = FALSE, eval = FALSE-------------------------------------------
#  paraBLS_PLGCM.r <- c(
#    "Y_mueta0", "Y_mueta1", "Y_mueta2", "Y_knot",
#    paste0("Y_psi", c("00", "01", "02", "11", "12", "22")), "Y_res",
#    "Z_mueta0", "Z_mueta1", "Z_mueta2", "Z_knot",
#    paste0("Z_psi", c("00", "01", "02", "11", "12", "22")), "Z_res",
#    paste0("YZ_psi", c("00", "10", "20", "01", "11", "21", "02", "12", "22")),
#    "YZ_res"
#    )
#  set.seed(20191029)
#  RM_BLS_PLGCM3 <- getMIX(
#    dat = RMS_dat0, prop_starts = c(0.33, 0.34, 0.33), sub_Model = "MGM",
#    cluster_TIC = c("gx1", "gx2"), t_var = c("T", "T"), y_var = c("R", "M"),
#    curveFun = "BLS", intrinsic = FALSE, records = list(1:9, 1:9),
#    res_scale = list(c(0.3, 0.3), c(0.3, 0.3), c(0.3, 0.3)),
#    res_cor = list(0.3, 0.3, 0.3), y_model = "LGCM", tries = 10, paramOut = TRUE,
#    names = paraBLS_PLGCM.r
#    )

## -----------------------------------------------------------------------------
Figure4 <- getFigure(
  model = RM_BLS_PLGCM3@mxOutput, nClass = 3, cluster_TIC = c("gx1", "gx2"), 
  sub_Model = "MGM", y_var = c("R", "M"), curveFun = "BLS", y_model = "LGCM",
  t_var = c("T", "T"), records = list(1:9, 1:9), m_var = NULL, x_var = NULL, 
  x_type = NULL, xstarts = xstarts, xlab = "Month", 
  outcome = c("Reading", "Mathematics")
)
show(Figure4)