simsem (version 0.2-8)

likRatioFit: Find the likelihood ratio (or Bayes factor) based on the bivariate distribution of fit indices

Description

Find the log-likelihood of the observed fit indices on Model 1 and 2 from the real data on the bivariate sampling distribution of fit indices fitting Model 1 and Model 2 by the datasets from the Model 1 and Model 2. Then, the likelihood ratio is computed (which may be interpreted as posterior odd). If the prior odd is 1 (by default), the likelihood ratio is equivalent to Bayes Factor.

Usage

likRatioFit(outMod1, outMod2, dat1Mod1, dat1Mod2, dat2Mod1, dat2Mod2, 
usedFit=NULL, prior=1)

Arguments

outMod1

SimModelOut that saves the analysis result of the first model from the target dataset

outMod2

SimModelOut that saves the analysis result of the second model from the target dataset

dat1Mod1

SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 1

dat1Mod2

SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 1

dat2Mod1

SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 2

dat2Mod2

SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 2

usedFit

Vector of names of fit indices that researchers wish to getCutoffs from. The default is to getCutoffs of all fit indices.

prior

The prior odds. The prior probability that Model 1 is correct over the prior probability that Model 2 is correct.

Value

• The likelihood ratio (Bayes Factor) in preference of Model 1 to Model 2. If the value is greater than 1, Model 1 is preferred. If the value is less than 1, Model 2 is preferred.

See Also

SimResult for a detail of simResult pValueNested for a nested model comparison by the difference in fit indices pValueNonNested for a nonnested model comparison by the difference in fit indices

Examples

library(lavaan)
loading <- matrix(0, 11, 3)
loading[1:3, 1] <- NA
loading[4:7, 2] <- NA
loading[8:11, 3] <- NA
path.A <- matrix(0, 3, 3)
path.A[2:3, 1] <- NA
path.A[3, 2] <- NA
param.A <- simParamSEM(LY=loading, BE=path.A)

model.A <- simModel(param.A, indLab=c(paste("x", 1:3, sep=""), paste("y", 1:8, sep="")))
out.A <- run(model.A, PoliticalDemocracy)

path.B <- matrix(0, 3, 3)
path.B[1:2, 3] <- NA
path.B[1, 2] <- NA
param.B <- simParamSEM(LY=loading, BE=path.B)

model.B <- simModel(param.B, indLab=c(paste("x", 1:3, sep=""), paste("y", 1:8, sep="")))
out.B <- run(model.B, PoliticalDemocracy)

u2 <- simUnif(-0.2, 0.2)
loading.mis <- matrix(NA, 11, 3)
loading.mis[is.na(loading)] <- 0
LY.mis <- simMatrix(loading.mis, "u2")
misspec <- simMisspecSEM(LY=LY.mis)

output.A.A <- runFit(model.A, PoliticalDemocracy, 5, misspec=misspec)
output.A.B <- runFit(model.A, PoliticalDemocracy, 5, misspec=misspec, analyzeModel=model.B)
output.B.A <- runFit(model.B, PoliticalDemocracy, 5, misspec=misspec, analyzeModel=model.A)
output.B.B <- runFit(model.B, PoliticalDemocracy, 5, misspec=misspec)

# The output may contain some warnings here. When the number of replications increases (e.g., 1000), the warnings should disappear.
likRatioFit(out.A, out.B, output.A.A, output.A.B, output.B.A, output.B.B)