My question concerns choosing the best fitting model. Given the twin correlations in a given phenotype (rMZ = 0.50, rDZ = 0.34), it seems that the model to be tested should be the ACEmodel (because rMZ < 2rDZ).
Runing the ACE model has given the following output.
> mxCompare( fitACE, fitAE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneACEc <NA> 4 4824.870 532 3760.870 NA NA NA 2 oneACEc oneAEc 3 4827.013 533 3761.013 2.143052 1 0.1432167 lbound estimate ubound oneAEc.h2[1,1] 0.3833 0.4961 0.5925 oneAEc.c2[1,1] 0.0000 0.0000 0.0000 oneAEc.e2[1,1] 0.4075 0.5039 0.6167 > mxCompare( fitACE, fitCE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneACEc <NA> 4 4824.870 532 3760.870 NA NA NA 2 oneACEc oneCEc 3 4826.179 533 3760.179 1.308865 1 0.2526002 lbound estimate ubound oneCEc.h2[1,1] NA 0.0000 NA oneCEc.c2[1,1] 0.3308 0.4331 0.5253 oneCEc.e2[1,1] 0.4747 0.5669 0.6692 > mxCompare( fitAE, fitE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneAEc <NA> 3 4827.013 533 3761.013 NA NA NA 2 oneAEc oneEc 2 4881.842 534 3813.842 54.82935 1 1.314631e-13 lbound estimate ubound oneEc.h2[1,1] 0 0 0 oneEc.c2[1,1] 0 0 0 oneEc.e2[1,1] 1 1 1
Based on minimizing the AIC and parsimony, I would choose the CE as the best fitting model.
However, I have also run the ADE model and the results confused me.
> mxCompare( fitADE, fitAE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneADEc <NA> 4 4827.013 532 3763.013 NA NA NA 2 oneADEc oneAEc 3 4827.013 533 3761.013 -2.692104e-10 1 1 lbound estimate ubound oneAEc.h2[1,1] 0.3833 0.4961 0.5925 oneAEc.d2[1,1] NA 0.0000 NA oneAEc.e2[1,1] 0.4075 0.5039 0.6167 > mxCompare( fitADE, fitDE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneADEc <NA> 4 4827.013 532 3763.013 NA NA NA 2 oneADEc oneDEc 3 4881.842 533 3815.842 54.82935 1 1.314631e-13 lbound estimate ubound oneDEc.h2[1,1] NA 0 NA oneDEc.d2[1,1] NA 0 NA oneDEc.e2[1,1] NA 1 NA > mxCompare( fitAE, fitE ) base comparison ep minus2LL df AIC diffLL diffdf p 1 oneAEc <NA> 3 4827.013 533 3761.013 NA NA NA 2 oneAEc oneEc 2 4881.842 534 3813.842 54.82935 1 1.314631e-13 lbound estimate ubound oneEc.h2[1,1] NA 0 NA oneEc.d2[1,1] NA 0 NA oneEc.e2[1,1] NA 1 NA
Here it would seem that the AE model provides the best fit, and that removing A significantly worsens the model fit, therefore, A should not be dropped.
By choosing the CE model from the ACEmodel, I would be dropping exactly A.
Should I take into account the results from the ADEmodel, or just go ahead with applying the rule rMZ < 2rDZ and test ACEmodel?
I worry about not reporting misleading results.
Thank you,
Mirela