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Table 2 shows that the APC model fit better than other models for both males and females because the deviance in this model was the closest to the degree of freedom.
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Other variables, such as smoking, physical activity, alcohol consumption, education, and heart rate, were included as variables in early analyses, but because they only marginally decreased the deviance in the multivariate analyses they were excluded in the final analysis.
Our final model explained 78% of the deviance in spatially explicit woody cover trends.
The body size-only model accounted for only about 1% of the deviance in threat risk.
However, the model only explained 2% of the total deviance in the data.
The scaling parameter was adjusted using the deviance method in each GEE.
The deviance parameter in the GLZ-model was rescaled to correct for over dispersion.
The model was able to explain 72% of the total deviance in herbivore density.
Table 1 shows the changes in deviance in the sequential building of the model.
This final model included five predictors and explained 34% of the total deviance in woody cover change.
Tip: Look for the positive deviance in a situation and see how to spread it.
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