Sentence examples for deviance in number of from inspiring English sources

Indeed, GC content and genome size explained 94.9 percent of the deviance in number of random ORFs (Table 1A).

Model selection was based on Akaike information criterion (AIC), which attempts to find the simplest model that adequately explains data by trading-off reductions in residual deviance against the number of parameters used in a model (see [ 17] for a review of model selection approaches).

This deviance can now be tested against chi-square with degrees of freedom equal to the difference in numbers of parameters of the two models, at the desired level of significance (Hogg and Craig, 1994).

11 Assessment of model fit was based on the deviance information criterion (DIC) and comparison of residual deviance to number of unconstrained data points.

A positive ΔAIC indicates that the three-factor model gives a worse fit (in terms of deviance explained and number of parameters used) than the full model.

Outcomes were compared from the fixed and random effects models and reported estimates from the model with a better fit, which was based on the deviance information criterion and comparing the residual deviance with the number of unconstrained data points.

The p-value for each ANODEV test indicates the probability of achieving a reduction in deviance equal to the difference in model deviances for a number of degrees of freedom equal to the difference of model degrees of freedom when using one model nested in another model.

Assessment of model fit was based on comparison of residual deviance to the number of unconstrained data points and assessment of the deviance information criterion.

d, True-positive rates (for which a priori known essential genes are counted as positive) when considering the genes that are depleted (fitness genes) in at least m cell lines (blue curve) as predictions and the deviance of the number of these genes from expectations (computed using the randomized data shown in c) for all possible values of m (red curve).

Model statistics: Deviance = 301,365.943; number of estimated parameters = 110.

Over/under-dispersion was evaluated using the ratio of the deviance to the number of degrees of freedom.