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...contain my basis functions modelFrame<-as.data.frame( list( srcData=fakeData, model1=seq(0,1,length=10), model2=c(rep(0,5),rep(1,5)) ) ) ##Create all data to be fitted allData<-ldply(1:2,function(x){modelFrame$modelFactor<-x;return(modelFrame)}) ##Apply models by factor: models1<-lmList(srcData ~ model1 | modelFactor,data=allData) models2<-lmList(srcData ~ model2 | modelFactor,data=allData) ##Attempt to apply encomptest - it fails! encomptest(models1[[1]],models2[[1]]) ##Now, create two models by hand modelFrame1<-modelFrame modelFrame2<-modelFrame models1&lt...

...) ys = c(y1, y2, y3) # values of dependent variables are percentage with some zero cases xs = c(x1, x2, x3, x4, x5) # x1, x2, x3 continuous and x4 and x5 dummies dependent variables. #Note: The variables in both selection and level equations are mostly same. #Selection model models1 = 'w1 ~ 1 + zs' # Level model model1 = 'w1 ~ 1 + zs|y1 ~ 1 + xs' fit.fiml = sem(model1, data=MyRdata, estimator="Fiml") # not sure "ML" or "Fiml" summary(fit.fiml) I greatly appreciate your help. Advance thank you. Regards Champak Ishram [[altern...