nlpsem (version 0.3)

getLRT: Perform Bootstrap Likelihood Ratio Test for Comparing Full and Reduced Models

Description

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.

Usage

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

Value

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.

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.

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

# \donttest{
# 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)
# }