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The principle of parsimony was used, in other words we do not reject the complex (saturated) model (e.g., ACE) until evidence in support of a more simplex model (e.g., AE) requires us to abandon it.
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Below we first present the standard simplex model.
The inclusion of GE covariance in the genetic simplex model can be achieved in different ways.
A simplex model (a straight line of causal effects) fit via χ, but that ordinary simplex model did not correspond to a comfortable or causally-understandable theory for these data.
When analyzing our data with a phenotypic simplex model, genetic innovations were significant (χ (5) = 41.30, p < 0.001).
We consider this in the context of the genetic simplex model as applied to longitudinal twin data.
In simultaneously subjecting these covariance matrices to the simplex model, the standard identification conditions hold (Jöreskog 1970).
Finally, fitting a genetic simplex model with transmission and innovation terms did not worsen the model fit (model 5a: χ (10) = 11.62, p = 0.311).
Given T = 4, the addition of the parameters αk and βk (k = 1,2,3), rendered the extended simplex model (see Fig. 2) unidentified.
We note that the ACE simplex model does not fit quite as well if the parameter βk is negative (i.e., βk = −.10).
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