Next, a saturated Cholesky decomposition of the monozygotic and dizygotic twin correlations among our 22 disorders was performed in Mx. A diagonally weighted least squares fit function was implemented in Mx (42) to maximize the agreement between the observed statistics and those predicted by the model. The squared deviations between observed and expected correlations were weighted by the inverse of the asymptotic covariances of each statistic; these weights were computed using Mplus. Because of the large number of variables in the model, we had to use limited-information diagonally weighted least squares instead of the more desirable full-information maximum-likelihood approach. A diagonally weighted least squares fit function was implemented in Mx to fit a two-group (monozygotic and dizygotic pairs) Cholesky model including additive genetic (A) and unique environmental (E) parameters to these estimated polychoric correlations and asymptotic weights. Because standard estimating functions could not be used, ordinary statistical indexes were not available to evaluate model-data fit and to compare nested models.
