getLRT function

Perform Bootstrap Likelihood Ratio Test for Comparing Full and Reduced Models

This function performs the likelihood ratio test (LRT) to compare a full model (an intrinsically nonlinear longitudinal model) with a corresponding parsimonious alternative (a non-intrinsically nonlinear longitudinal model). It also provides an option to perform bootstrapping for the comparison.

getLRT(full, reduced, boot = FALSE, rep = NA)

Arguments

• full: A fitted mxModel object for the full model. Specifically, this should be the mxOutput slot from the result returned by one of the estimation functions provided by this package.
• reduced: A fitted mxModel object for the reduced model. Specifically, this should be the mxOutput slot from the result returned by one of the estimation functions provided by this package.
• boot: A logical flag indicating whether to perform bootstrapping for the comparison. Default is FALSE.
• rep: An integer specifying the number of bootstrap replications if boot is TRUE. Default is NA.

Returns

A data frame containing the number of free parameters, estimated likelihood (-2ll), degrees of freedom, differences in log-likelihood and degrees of freedom, p-values, AIC, and BIC for both the full and reduced models.

Examples

mxOption(model = NULL, key = "Default optimizer", "CSOLNP", reset = 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$T1 - baseT
RMS_dat0$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

# Fit bilinear spline growth model with random knot (intrinsically nonlinear model)
BLS_LGCM_f <- getLGCM(dat = RMS_dat0, t_var = "T", y_var = "M", curveFun = "bilinear spline",
                      intrinsic = TRUE, records = 1:9, res_scale = 0.1)
# Fit bilinear spline growth model with fix knot (non-intrinsically nonlinear model)
BLS_LGCM_r <- getLGCM(dat = RMS_dat0, t_var = "T", y_var = "M", curveFun = "bilinear spline",
                      intrinsic = FALSE, records = 1:9, res_scale = 0.1)
# Likelihood ratio test
getLRT(full = BLS_LGCM_f@mxOutput, reduced = BLS_LGCM_r@mxOutput, boot = FALSE, rep = NA)