Hello,
I have a couple of questions regarding comparing ACE and ADE models.
- Is there a quick method of comparing the fit of both of these models? As of right now I am running the scripts for each model, then comparing the two models using mxcompare:
mxCompare( fitADENOTW , fitACENOTW )
I know typically when we compare nested, we run something to the effect of...
#Run ACE model without Twin Effect modelACENOTW <- mxModel( fitACE, name="oneACENOTWba" ) modelACENOTW <- omxSetParameters( modelACENOTW, labels="tw11", free=FALSE, values=0 ) fitACENOTW <- mxTryHard( modelACENOTW, intervals=F ) mxCompare( fitACE, fitACENOTW )
But I am unsure how to incorporate the change from the ACE model:
covDZ <- mxAlgebra( expression= 0.5%x%A+ C+Tw, name="cDZ" ) covSIB <- mxAlgebra( expression= 0.5%x%A+ C, name="cSIB" )
to the ADE model:
covDZ <- mxAlgebra( expression= 0.5%x%A+ 0.25%x%D+Tw, name="cDZ" ) covSIB <- mxAlgebra( expression= 0.5%x%A+ 0.25%x%D, name="cSIB" )
in a short, correct manner that would preclude me from running two separate scrips.
- Even when I run these models and compare their fit, I am unable to test whether differences in their fit are statistically significant. Notice the P value indicates NA. I know I can reference the AIC descriptives, with the ADE fitting better than ACE in this instance, but I also want to include the p value, if possible.
< mxCompare( fitADENOTW , fitACENOTW ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneADENOTWba <NA> 8 4879.663 1740 1399.663 NA NA NA 2 oneADENOTWba oneACENOTWba 8 4882.399 1740 1402.399 2.735366 0 NA
This may have something to do with the similar degrees of freedom, since chi-square fit statistics uses df. However, I have used chi-square tests of fitness before (SEM) and, if I am not mistaken, it yielded a p value.
Any help would be greatly appreciated.