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The interaction value can also be thought of as the relative contribution of the interaction between the two predictors towards the overall predictive performance of the individual model (the cv deviance value).
A mouse was rejected from its set based on an expected deviance value of below one-half.
* Scaled deviance value divided by its degrees of freedom = 1.03 $ Scaled deviance value divided by its degrees of freedom = 1.02 # Scaled deviance value divided by its degrees of freedom = 1.02 Twenty-nine (29%) patients reported using a sedating drug at or around bedtime while at home.
Therefore, in the following steps of model selection we kept the model including 'food.family' and 'family (4 VCs)' (its deviance value was indeed slightly better; 2774.0 vs. 2777.9).
We then fit for sensitivity parameters and set systematic negative error to zero (Sensitivity Model in Additional File 2) to see if this model outputs a deviance value significantly lower than the deviance measure from the Simple Model.
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AUC values and adjusted deviance values were not positively correlated.
However, the deviance values are significantly improved for several re-loessed datasets, most notably 10a-d.
Pairs of models were compared using the differences between their deviance values (ΔD).
The expected deviance values for all other physical measurements were above one-half showing two otherwise valid sets [ 18].
Note: Due to the standardization of model variables, regression coefficients and Δ deviance values are directly comparable.
Finally, the five deviance values, each from a different subset, were summed up to obtain the cross-validated partial deviance of the entire data set.
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