Sentence examples for sum of deviance from inspiring English sources

The model with 108 genes had the best internal performance characteristics, measured by area-under-the-curve for misclassification errors, and sum of deviance.

The "misclassification rate" and "sum of deviance" were used to determine the number of genes in each model, as described in Additional file 1 (also see Additional file 2, Figure S1).

In tuning the model parameters, a data-driven empirical approach was taken to select the optimal number of features in each gene signature, using the sum of deviances as a metric of relative performance.

We propose using the "sum of deviances" between observed and predicted phenotypes, coupled with the "misclassification rate" under a leave-one-out process, to determine the optimal size of our BPR model (i.e., the number of genes to include in the regression equation).

Parameters for these models were estimated by minimising the sum of total deviance and lead time penalty (mean lead time × penalty).

The criteria used to end the search were: [a] simple parametric expression, [b] the corresponding function introduced as a transform in the Cox model satisfying the GAM linearity test, and [c] without deteriorating the model fit as assessed by the sum of squares of "deviance residuals" [ 8].

We repeated for all threshold values and finally plotted the sums of the deviance versus the threshold values.

We formally evaluated the goodness of fit of our models using the total residual deviance (posterior mean of the deviance under a given model minus the deviance for the saturated model) along with the deviance information criterion (sum of the posterior mean of the residual deviance and the effective number of parameters).

Because deviance is a measure of goodness of fit for the model, a possible threshold is the one corresponding to the minimum sum of the city-specific models' deviance.

For an overall comparison of the different models, we computed the sum of the city-specific differences in deviance, which again follows the chi-square distribution with degrees of freedom the sum of the city-specific difference in the degrees of freedom.

In LMs, R is defined from the residual sum of squares, and in GLMs the goodness of fit is evaluated in terms of the deviance: the percentage of deviance explained by the model.