# Examples of Latent Change Score Models

## Load nlpsem package, dependent packages and set CSOLNP as the optimizer

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

## Load pre-computed models

    load(system.file("extdata", "getLCSM_examples.RData", package = "nlpsem"))

## Load example data and preprocess data

    # 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

## Example 1: Fit nonparametric LCSMs to assess the development of reading ability from Kindergarten to Grade 5, both with and without incorporating baseline teacher-reported approach to learning and attentional focus. The `getSummary()` function is used to generate a comprehensive summary table for these two models. Additionally, the visual representations of the growth rate and change from the baseline for both models.

    Read_LCSM_NonP <- getLCSM(
      dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "nonparametric",
      intrinsic = FALSE, records = 1:9, growth_TIC = NULL,
      paramOut = TRUE
      )
    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"),
      paramOut = TRUE
      )

    getSummary(model_list = list(Read_LCSM_NonP@mxOutput, Read_LCSM_NonP_TIC@mxOutput))
    #>    Model No_Params     -2ll      AIC      BIC Y_residuals
    #> 1 Model1        13 32288.39 32314.39 32369.18     45.1379
    #> 2 Model2        22 34575.48 34619.48 34712.20     45.1410
    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"
    )
    #> Treating first argument as an object that stores a character
    #> Treating first argument as an object that stores a character
    #> Treating first argument as an object that stores a character
    show(Figure1)
    #> figOutput Object
    #> --------------------
    #> Trajectories: 1
    #> Figure 1:
    #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

    #> Figure 2:

    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"
    )
    #> Treating first argument as an object that stores a character
    #> Treating first argument as an object that stores a character
    #> Treating first argument as an object that stores a character
    show(Figure2)
    #> figOutput Object
    #> --------------------
    #> Trajectories: 1
    #> Figure 1:
    #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

    #> Figure 2:

## Example 2: Fit LCSMs with quadratic, negative exponential and Jenss-Bayley functional forms. Additionally, the visual representations change from the baseline for three models.

    Read_LCSM_QUAD <- getLCSM(
      dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "quadratic", intrinsic = FALSE,
      records = 1:9, paramOut = TRUE
      )
    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, tries = 10, paramOut = TRUE
      )
    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, tries = 10, paramOut = TRUE
      )

    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"
    )
    #> Treating first argument as an object that stores a character
    show(Figure3)
    #> figOutput Object
    #> --------------------
    #> Trajectories: 1
    #> Figure 1:
    #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

    #> Figure 2:

    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"
    )
    #> Treating first argument as an object that stores a character
    show(Figure4)
    #> figOutput Object
    #> --------------------
    #> Trajectories: 1
    #> Figure 1:
    #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

    #> Figure 2:

    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"
    )
    #> Treating first argument as an object that stores a character
    show(Figure5)
    #> figOutput Object
    #> --------------------
    #> Trajectories: 1
    #> Figure 1:
    #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

    #> Figure 2: