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We calculated the percent deviance explained by season, day-of-week, and holiday patterns by estimating the deviance from a null GLM and a full GLM model with the season/temporal trend, day-of-week, or holiday term; the full deviance was subtracted from the null deviance and then divided by the null deviance.
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In this, the significance of factors was tested by individually subtracting each factor in turn from the full model and comparing models by estimating the difference in deviance (G) between the two models.
We explored goodness of model fit by estimating the ACF of each model's deviance residuals.
A dispersion parameter was estimated by the deviance method, because the data were over-dispersed; initial model fitting with a dispersion parameter of 1 (for binomial and poisson error structures) yielded residual deviance much larger than the residual degrees of freedom.
The model scale parameter was estimated using the deviance method to allow for over-dispersed data [ 14].
The GLM method was then used to estimate the coefficient and deviance for each gene.
The goodness of fit was estimated by evaluation of the deviance Δ.
Parameters were estimated by numerically minimising the deviance between the number of cases observed and the number of cases predicted by the models.
Diagnostics for model fit (Hosmer & Lemeshow) and accuracy (difference in deviance by estimated probability plot) were checked for each species.
The model parameters were estimated iteratively, and the deviance (based on observed and expected numbers aggregated over 5-year groups by age, date of birth and year of death) was used to assess overall goodness of fit.
Analyses of deviance were performed to estimate the effect of each factor in the selected models.
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CEO of Professional Science Editing for Scientists @ prosciediting.com