inst/doc/getLCSM_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", "getLCSM_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$T1 - baseT)/12
#  RMS_dat0$T2 <- (RMS_dat0$T2 - baseT)/12
#  RMS_dat0$T3 <- (RMS_dat0$T3 - baseT)/12
#  RMS_dat0$T4 <- (RMS_dat0$T4 - baseT)/12
#  RMS_dat0$T5 <- (RMS_dat0$T5 - baseT)/12
#  RMS_dat0$T6 <- (RMS_dat0$T6 - baseT)/12
#  RMS_dat0$T7 <- (RMS_dat0$T7 - baseT)/12
#  RMS_dat0$T8 <- (RMS_dat0$T8 - baseT)/12
#  RMS_dat0$T9 <- (RMS_dat0$T9 - baseT)/12
#  # Standardize time-invariant covariates (TICs)
#  ## ex1 and ex2 are standardized growth TICs in models
#  RMS_dat0$ex1 <- scale(RMS_dat0$Approach_to_Learning)
#  RMS_dat0$ex2 <- scale(RMS_dat0$Attention_focus)
#  xstarts <- mean(baseT)/12

## ---- message = FALSE, eval = FALSE-------------------------------------------
#  paraNonP_LCSM <- c(
#    c("mueta0", "mueta1", paste0("psi", c("00", "01", "11")), paste0("rel_rate", 2:8),
#      "residuals", paste0("slp_val_est", 1:8), paste0("slp_var_est", 1:8),
#      paste0("chg_inv_val_est", 1:8), paste0("chg_inv_var_est", 1:8),
#      paste0("chg_bl_val_est", 1:8), paste0("chg_bl_var_est", 1:8))
#    )
#  Read_LCSM_NonP <- getLCSM(
#    dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "nonparametric",
#    intrinsic = FALSE, records = 1:9, growth_TIC = NULL, res_scale = 0.1,
#    paramOut = TRUE, names = paraNonP_LCSM
#    )
#  paraNonP_LCSM_TIC <- c(
#    c("alpha0", "alpha1", paste0("psi", c("00", "01", "11")), paste0("rel_rate", 2:8),
#      "residuals", paste0("beta1", c(0:1)), paste0("beta2", c(0:1)),
#      paste0("mux", 1:2), paste0("phi", c("11", "12", "22")), "mueta0", "mueta1",
#      paste0("slp_val_est", 1:8), paste0("slp_var_est", 1:8),
#      paste0("chg_inv_val_est", 1:8), paste0("chg_inv_var_est", 1:8),
#      paste0("chg_bl_val_est", 1:8), paste0("chg_bl_var_est", 1:8))
#    )
#  Read_LCSM_NonP_TIC <- getLCSM(
#    dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "nonparametric",
#    intrinsic = FALSE, records = 1:9, growth_TIC = c("ex1", "ex2"), res_scale = 0.1,
#    paramOut = TRUE, names = paraNonP_LCSM_TIC
#    )

## -----------------------------------------------------------------------------
getSummary(model_list = list(Read_LCSM_NonP@mxOutput, Read_LCSM_NonP_TIC@mxOutput))
Figure1 <- getFigure(
  model = Read_LCSM_NonP@mxOutput, sub_Model = "LCSM", y_var = "R", curveFun = "NonP", 
  y_model = "LCSM", t_var = "T", records = 1:9, xstarts = xstarts, xlab = "Year",
  outcome = "Reading"
)
show(Figure1)
Figure2 <- getFigure(
  model = Read_LCSM_NonP_TIC@mxOutput, sub_Model = "LCSM", y_var = "R", curveFun = "NonP", 
  y_model = "LCSM", t_var = "T", records = 1:9, xstarts = xstarts, xlab = "Year",
  outcome = "Reading"
)
show(Figure2)

## ---- message = FALSE, eval = FALSE-------------------------------------------
#  Read_LCSM_QUAD <- getLCSM(
#    dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "quadratic", intrinsic = FALSE,
#    records = 1:9, res_scale = 0.1
#    )
#  set.seed(20191029)
#  Read_LCSM_EXP_r <- getLCSM(
#    dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "negative exponential",
#    intrinsic = FALSE, records = 1:9, res_scale = 0.1, tries = 10
#    )
#  set.seed(20191029)
#  Read_LCSM_JB_r <- getLCSM(
#    dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "Jenss-Bayley",
#    intrinsic = FALSE, records = 1:9, res_scale = 0.1, tries = 10
#    )

## -----------------------------------------------------------------------------
Figure3 <- getFigure(
  model = Read_LCSM_QUAD@mxOutput, sub_Model = "LCSM", y_var = "R", curveFun = "QUAD", 
  y_model = "LCSM", t_var = "T", records = 1:9, xstarts = xstarts, xlab = "Year",
  outcome = "Reading"
)
show(Figure3)
Figure4 <- getFigure(
  model = Read_LCSM_EXP_r@mxOutput, sub_Model = "LCSM", y_var = "R", curveFun = "EXP", 
  y_model = "LCSM", t_var = "T", records = 1:9, xstarts = xstarts, xlab = "Year",
  outcome = "Reading"
)
show(Figure4)
Figure5 <- getFigure(
  model = Read_LCSM_JB_r@mxOutput, sub_Model = "LCSM", y_var = "R", curveFun = "JB", 
  y_model = "LCSM", t_var = "T", records = 1:9, xstarts = xstarts, xlab = "Year",
  outcome = "Reading"
)
show(Figure5)