From: Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Model 1: | No overdispersion | V(Y i ) = n i p i (1 - p i ) | Â | |
---|---|---|---|---|
Coefficients | Estimate | (s.e) | z-value | p-value |
β 0 | -4.660 | 0.033 | -140.68 | < 2e-16 |
β 1 | -0.888 | 0.043 | -20.41 | < 2e-16 |
Model 2: | Quasilikelihood | V(Y i ) = n i p i (1 - p i ) | = 187.6 | |
Coefficients | Estimate | (s.e) | t-value | p-value |
β 0 | -4.660 | 0.454 | -10.261 | 5e - 05 |
β 1 | -0.888 | 0.595 | -1.489 | 0.187 |
Model 3: | Hierarchical | V(Y i ) = n i p i (1 - p i ) [1 + (n i - 1)φ] | = 3.4e - 03 | |
Coefficients | Estimate | (s.e) | t-value | p-value |
β 0 | -4.656 | 0.428 | -10.874 | 3.6e - 05 |
β 1 | -0.850 | 0.570 | -1.492 | 0.186 |