Table 7 |
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|
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. |
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|
Model 1: |
Full Model |
Deviance = 5.0742 |
||
|
|
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|
Coefficients |
Estimate |
(s.e) |
t-value |
p-value |
|
β0 |
-11.494 |
13.518 |
-0.06 |
0.9519 |
|
β1 |
5.987 |
13.524 |
-0.03 |
0.9750 |
|
|
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|
Model 2: |
Null Model |
Deviance = 8.7541 |
||
|
|
||||
|
Coefficients |
Estimate |
(s.e) |
t-value |
p-value |
|
β0 |
-5.794 |
0.392 |
-14.772 |
6.05e-06 |
|
|
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|
Baggerly et al. BMC Bioinformatics 2004 5:144 doi:10.1186/1471-2105-5-144 |
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