# NOT RUN {
R <- 160
wgtnames <- paste("repwgt", seq(0,R,by=1), sep="")
mwgtname=wgtnames[1]
repwgtnames=wgtnames[2:(R+1)]
fayfactor=0.5
model3 <- ' # outcome
numcg ~ u0*1 + c*workban + b1*sp_adltban + b2*sp_kidsban
# mediator
sp_adltban ~ u1*1 + a1*workban
sp_kidsban ~ u2*1 + a2*workban
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
fit <- lavaan::sem(model=model3, data=MedData, estimator='ML', test='standard')
chisq.BRR(model3,fit,MedData,mwgtname, repwgtnames)
#
# MedSurvey 1.1.0 Adjusted Model Fit Statistics using BRR
#
# chisq df pvalue CFI RMSEA SRMR AIC BIC
#
# 305.25 1 0.00000 0.40561 0.27852 0.07416 88699.43 88768.45
# }
# NOT RUN {
# }
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