Tree models are generated by repeated binary partitioning of a population, based on a set of predictor variables, such that the cases in the descendant subsets are increasingly more similar within those subsets, so that the overall tree deviance becomes smaller with each successive split.
Deviance is a measure of deviation between the model and the data: the greater the deviance, the greater the deviation from the model.
Similar to analysis of variance in ordinary linear regression, we examined the overall significance of location using the difference in deviance of the complete model (equation 1) and the reduced model omitting the smoothing term.
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).
The overall fit of the models was assessed using the change in the deviance score compared with an intercept-only model with the degrees of freedom equal to number of parameters in the model.
Second, we used likelihood-ratio tests to compare differences in the overall fit for each model versus previous models, to test for statistically significant changes in the model deviance.
The objective of the overall study was to determine the effect of the RTLS in combination with a behaviour change approach called 'positive deviance' on HAIs.
There was no statistically significant effect on the RR or improvement in the overall quality of fit (deviance/degree of freedom) upon consideration of most interaction subgroups, and we did not see any evidence of significant interactions (p > 0.05 based on deviance statistics).
An overall χ deviance test comparing the fitted model with a saturated model with k = n parameters indicated that deviance for the fitted model was not significantly larger than for the saturated model.
Then, alternative splicing scores (FIRMA scores) are calculated for each individual exon in each individual sample, as the deviance between the exon level and corresponding overall gene level expression measures.
