# 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
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.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))
#> 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:
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"
)
#> 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: