Sentence examples for mean deviance from inspiring English sources

Exact(18)

Model 2 found that shark sex (LRT12 = 35.893, p = 0.0003) had only a small effect (mean deviance ∼1) relative to patch effect (Table 3).

The first fit of the log linear model (Model 1) found that patch contributed the greatest effect (LRT6 = 1891.405, mean deviance >1; p<0.0001), but a large proportion of the residual variance remained unexplained (Table 2).

To account for overdispersion in the data quasi-F tests were performed by calculating ratios of mean deviance changes [34].

The overdispersion parameter (c-hat) was calculated as the ratio between the mean deviance of simulated models and the deviance of the observed model [18].

We used the posterior mean deviance as a Bayesian measure of fit or model adequacy as defined by Spiegelhalter et al. [20].

Overall, while the variations in mean deviance (Figure 2b) seen between model variants were much smaller than for the models conditioned on the first infection (Figure 2a), the interaction model allowing for time varying cattle infectivity gave the most adequate fit (measured by both mean deviance and DIC, see Table 2).

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Similar(42)

Analogous to Analysis of Variance, mean deviances (MD = 2.log-likelihood ratio/DF) were used to calculate quasi- F values.

This gives feminists an interest in the value of "ontological heterogeneity"—using categories that permit the observation of within-group variation, and that resist the representation of difference from the group mean as deviance.

The smaller the mean posterior deviance, the better the corresponding model fits the data.

Meaningful differences were observed in posterior mean residual deviance and deviance information criterion values, suggesting that, for the full network, the inconsistency model is preferred over the consistency model (appendix 6, table 7).

30 If the inconsistency model had the smallest posterior mean residual deviance, heterogeneity, or deviance information criterion value then this indicates potential inconsistency in the data.

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