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The model scale parameter was estimated using the deviance method to allow for over-dispersed data [ 14].
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The manual of the ADMIXTURE code proposes choosing model complexity that minimizes the prediction error on held-out data estimated using the mean deviance residuals reported by the algorithm (K c v ∗ ).
The significance of the increase in deviance resulting from the deletion of a variable in the model was estimated using the chi-squared deletion test [44].
We evaluated absolute model fit of the best model using deviance and evaluated the significance of the parameter estimates using the difference in deviance between the maximal model (model with all explanatory variables included) and the model without a given variable [40].
We also assessed whether the (t) functions, estimated using expression (7), fit the data well by using the goodness-of-fit deviance statistic.
For models estimated using maximum likelihood (such as probit), deviance based definitions of residuals are recommended [55].
The significance of associations was measured using the deviance difference as an approximate chi-square statistic.
The scaling parameter was adjusted using the deviance method in each GEE.
The models were compared using the Deviance Information Criteria (DIC) [20].
Model fit was assessed using the deviance information criterion.
Regression trees are constructed using the "deviance" criterion.
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CEO of Professional Science Editing for Scientists @ prosciediting.com