Examples of Longitudinal Mixture Models

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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", "getMIX_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$T1 - baseT
RMS_dat0$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)

Example 1: Fit bilinear spline LGCMs with 1-, 2-, and 3- latent classes to examine the heterogeneity in the development of mathematics skills. The enumeration process is conducted using the `getSummary()` function, with `HetModels = TRUE` specified.

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"
)
#> 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")'

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"
)
#> 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")'

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"
)
#> 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(Figure3)
#> figOutput Object
#> --------------------
#> Trajectories: 1 
#> Figure 1:
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

getSummary(model_list = list(Math_BLS_LGCM1@mxOutput, Math_BLS_LGCM2@mxOutput, Math_BLS_LGCM3@mxOutput),
           HetModels = TRUE)
#> # A tibble: 3 × 11
#>   Model  No_Params `-2ll`    AIC    BIC Y_res_c1 Y_res_c2 Y_res_c3 `%Class1`
#>   <chr>      <int>  <dbl>  <dbl>  <dbl>    <dbl>    <dbl>    <dbl> <chr>    
#> 1 Model1        11 31347. 31369. 31416.     34.0     NA       NA   100%     
#> 2 Model2        23 31135. 31181. 31278.     34.2     31.6     NA   67.4%    
#> 3 Model3        35 31008. 31078. 31226.     29.6     31.0     31.5 22.4%    
#> # ℹ 2 more variables: `%Class2` <chr>, `%Class3` <chr>

Example 2: Fit reduced bilinear spline bivariate LGCMs with three latent classes to analyze the heterogeneity in the co-development of reading and mathematics skills.

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")
)
#> 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
#> 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(Figure4)
#> figOutput Object
#> --------------------
#> Trajectories: 2 
#> 
#> Trajectory 1 :
#>   Figure 1:
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

#> 
#> Trajectory 2 :
#>   Figure 1:
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.

Examples of Longitudinal Mixture Models

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

Load pre-computed models

Load example data and preprocess data

Example 1: Fit bilinear spline LGCMs with 1-, 2-, and 3- latent classes to examine the heterogeneity in the development of mathematics skills. The enumeration process is conducted using the getSummary() function, with HetModels = TRUE specified.

Example 2: Fit reduced bilinear spline bivariate LGCMs with three latent classes to analyze the heterogeneity in the co-development of reading and mathematics skills.

Example 1: Fit bilinear spline LGCMs with 1-, 2-, and 3- latent classes to examine the heterogeneity in the development of mathematics skills. The enumeration process is conducted using the `getSummary()` function, with `HetModels = TRUE` specified.