Sentence examples for model deviance and from inspiring English sources

Model selection was based on Akaike's Information Criterion AICC); the model with the lowest AIC value was considered as the best compromise between model deviance and model parameters [39].

For model comparisons, we evaluated the differences in model deviance and the related P values from goodness of fit tests at a 5% significance level using the difference in the observed deviance and the degrees of freedom between a set of nested models.

Lags were evaluated by separately fitting the single variable models for bronchiolitis and ILI for the 5 lags from -2 and +2 weeks to determine which had the best model fit according to model deviance and the strength of the parameter estimate.

These statistics account for both model fit (deviance) and model complexity by penalizing models with a larger number of parameters.

For both the distance model and spatially-explicit model, deviance goodness-of-fit tests and overdispersion parameters (values ≠1 conflict with the assumption of binomial distribution) were used to determine whether the sample data followed a binomial distribution, and corrections for overdispersion were applied where needed [31].

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].

Model fit was assessed by inspection of the overdispersion parameter, the model deviance as well as patterns and magnitude of the residuals.

to minimise linear model Deviance for the given cancer site and gender.

This is indicated by non-significant reductions in model deviance in all cases: 1) p = 0.12 and 0.93 between GLM models, and 2) p = 0.14 and 0.66 between GAM models.

Superimposed on this broader environmental constraint, conifer cover displaces aspen cover and shows a positive correlation with model deviance (r = 0.344).

Variables associated with early death at the 0.2 level by univariate analysis were introduced in the multivariate model and subsequently selected in order to improve model deviance.