Table 7 Fitting nested deviance models. Fitting nested models to the data in order to get deviance scores. The difference in deviance between models is a better indicator of the significance of the associated effect (β1) when the logistic regression fits are near the boundary of the space, giving proportions close to zero.
From: Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Model 1: | Full Model | Deviance = 5.0742 | Â | |
|---|---|---|---|---|
Coefficients | Estimate | (s.e) | t-value | p-value |
β 0 | -11.494 | 13.518 | -0.06 | 0.9519 |
β 1 | 5.987 | 13.524 | -0.03 | 0.9750 |
Model 2: | Null Model | Deviance = 8.7541 | Â | |
Coefficients | Estimate | (s.e) | t-value | p-value |
β 0 | -5.794 | 0.392 | -14.772 | 6.05e-06 |