Examples of Multivariate Longitudinal Models

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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", "getMGM_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
xstarts <- mean(baseT)

Example 1: Fit multivariate bilinear spline LGCMs fixed knots to evaluate the development of reading and mathematics ability from Kindergarten to Grade 5.

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"
  )
RM_PLGCM.r <- getMGM(
  dat = RMS_dat0, t_var = c("T", "T"), y_var = c("R", "M"), curveFun = "BLS",
  intrinsic = FALSE, records = list(1:9, 1:9), y_model = "LGCM", res_scale = c(0.1, 0.1),
  res_cor = 0.3, paramOut = TRUE, names = paraBLS_PLGCM.r
  )

Figure1 <- getFigure(
  model = RM_PLGCM.r@mxOutput, sub_Model = "MGM", y_var = c("R", "M"), curveFun = "BLS", 
  y_model = "LGCM", t_var = c("T", "T"), records = list(1:9, 1:9), 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
show(Figure1)
#> 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")'

Example 2: Fit multivariate bilinear spline LGCMs with random knots to evaluate the development of reading and mathematics ability from Kindergarten to Grade 5.

paraBLS_PLGCM_f <- c(
  "Y_mueta0", "Y_mueta1", "Y_mueta2", "Y_knot", 
  paste0("Y_psi", c("00", "01", "02", "0g", "11", "12", "1g", "22", "2g", "gg")), "Y_res",
  "Z_mueta0", "Z_mueta1", "Z_mueta2", "Z_knot", 
  paste0("Z_psi", c("00", "01", "02", "0g", "11", "12", "1g", "22", "2g", "gg")), "Z_res",
  paste0("YZ_psi", c(c("00", "10", "20", "g0", "01", "11", "21", "g1",
                       "02", "12", "22", "g2", "0g", "1g", "2g", "gg"))),
  "YZ_res"
  )
RM_PLGCM.f <- getMGM(
  dat = RMS_dat0, t_var = c("T", "T"), y_var = c("R", "M"), curveFun = "BLS",
  intrinsic = TRUE, records = list(1:9, 1:9), y_model = "LGCM", res_scale = c(0.1, 0.1),
  res_cor = 0.3, paramOut = TRUE, names = paraBLS_PLGCM_f
  )

Figure2 <- getFigure(
  model = RM_PLGCM.f@mxOutput, sub_Model = "MGM", y_var = c("R", "M"), curveFun = "BLS", 
  y_model = "LGCM", t_var = c("T", "T"), records = list(1:9, 1:9), 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
show(Figure2)
#> 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.