Sentence examples for deviance from the model from inspiring English sources

The null-deviance is the deviance from the model with no covariates, where μi = n−1∑yi (the predicted T2D risk for each individual equals the average incidence of T2D in the population, independent of the characteristics of the individual).

Specifically, we estimated equation (1) for time at diagnosis (Z0) and for each lag time of interest (Z k ) and statistically tested the deviances from the models with a Z term and the deviance from the model with no Z term.

We tested the effect of different lag times on NHL risk through analysis of deviance (ANODEV), testing for significant differences between model deviances from the model with no smoothing term and from the five models with smoothing terms (one for time at diagnosis, four with lag times).

Deviance is a measure of deviation between the model and the data: the greater the deviance, the greater the deviation from the model.

Approximate 95% CIs for the level and the timing of the predicted peak in mesothelioma deaths were calculated by adjusting model parameters to produce a lower/earlier peak and a higher/later peak, corresponding to a change in the deviance from the optimal model equivalent to the 5% critical value of the χ distribution on the number of degrees of freedom in the model.

Deviance residuals from the model with clinical factors were plotted vs the HRQOL scores to explore the relationship between each HRQOL score and the remaining part of the hazard not already explained by clinical factors.

The deviance (that is minus twice the log likelihood) for the multivariable model presented was 520.6, whereas the deviance for the model with only an intercept (i.e., no effects of fever or time from onset to arrival) was 532.5.

Deviances from the models were scaled using the square-root of the ratio deviance/degrees of freedom, to correct for data over-dispersion.

The tested models, i.e., the Cox regression model containing the conventional markers alone and the three different feature selection models (the Lasso and Ridge regressions and the C-index boosting algorithm) were evaluated for their predictive accuracies (generalizabilities) using the deviance from the null model and iRBS.

Therefore significant deviation from the model fit was expected, so standard errors were scaled using the square root of the deviance-based dispersion.

Any variables that caused an insignificant increase in deviance on removal from the model were left out of the model while the variable that caused a significant increase in deviance on removal was retained in the model.