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The model with residual ancestral migration rate (m0∼10−10) and full replacement (δ = 1) clearly better fitted our data than any other model (Figure 3A, highest ψ1, the ψ1 of this model is significantly higher after correction for multiple testing when compared with the other ψ1 values, P<0.01).

Therefore, we examined whether the inclusion of polynomial terms better fits our data, using the Akaike information criterion (AIC) [(21), see Materials and Methods].

Overall, we did not find compelling evidence to suggest that the mixed models fit our data better than the fixed-effects model.

3 However, multicomponent models of this sophistication did not fit our data better than the models we derived.

A simpler model such as polytomous regression or predictive mean matching seems to fit our data better.

The model of a sex-linked locus for average sensillar response ratios fits our data better than the single autosomal codominant gene model found by earlier studies [ 11].

We found that soluble ICAM-1 levels correlated significantly with both plasma ANG-2 and hemoglobin, and that a model including only plasma ANG-2 and hemoglobin fit our data better than one including all three predictors.

Model M1a (nearly neutral), which hypothesizes a variable selective pressure among sites but not positive selection and M2a, which considers positive selection, fit our data better than the M0 model, with an ℓ = -6689.544 in both cases (Table 2).

The higher the value of R 2 and the lower the value of SSE, the better fitted the data.

Predictive equations better fitted the data after transforming strength values using a natural logarithmic function.

In order to assess whether a period or cohort effect model was a better fit for our data we employed the Akaike information criterion (AIC) to help us assess the quality of the model for our data [ 12].