Sentence examples for measures the deviance from inspiring English sources

We used two summary measures, the deviance and the Akaike information criterion (AIC), to compare different models.

Relative goodness of fit was measured by the deviance information criterion (DIC), following the usual rule of thumb that models with DIC within 2 of each other have no effective difference in goodness of fit, while a DIC difference of more than 10 indicates substantial evidence in favor of the better (i.e. lower) scoring model.

The significance of associations was measured using the deviance difference as an approximate chi-square statistic.

The OUV measure calculates the deviance between genomic oligonucleotide frequencies and approximated oligonucleotide frequencies using the considered oligonucleotide's mononucleotide frequencies.

The goodness of fit of a model is measured by both the deviance G and the Bayesian Information Criterion BICC).

The posterior mean residual deviance (Dres) was used to measure model fit and the deviance information criterion (DIC), a composite measure of model fit and model complexity, was used to choose between competing models [ 22].

In some other cases, statistical tests have been carried out in a way that is somewhat questionable in the sense that the determination of the critical values and the P-values is not adequately justified due to lack of a rigorous statistical distribution theory of the deviance measures used for such tests.

Model goodness of fit was assessed using the ratio of the Deviance measure to DF (degrees of freedom) [ 38].

The Sensitivity Model however did not substantially improve the model prediction from model 1, with a reduction of the deviance measure by 1.27 (Δdf = 1, p = 0.23).

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.

The Sensitivity-Error Model further added screening sensitivities as fitting parameters to the Systematic Error Model but did not improve the model prediction significantly (the deviance measure is 21.4 in Systematic Error Model vs. 21.38 in Sensitivity-Error Model).