Term | Description |
---|---|
D_{E} | Error Deviance |
D_{T} | Total Deviance |
Term | Description |
---|---|
R^{2} | the deviance R^{2} |
p | the regression degrees of freedom |
Φ | 1, for binomial and Poisson models |
D_{T} | the total deviance |
While the calculations for adjusted deviance R^{2} can produce negative values, Minitab displays zero for these cases.
The log-likelihood functions are parameterized in terms of the means. The general form of the functions follow:
The general form of the individual contributions follows:
The specific form of the individual contributions depends on the model.
Model | l_{i} |
Binomial | |
Poisson |
Term | Description |
---|---|
p | the regression degrees of freedom |
L_{c} | the log-likelihood of the current model |
y_{i} | the number of events for the i^{th} row |
m_{i} | the number of trials for the i^{th} row |
the estimated mean response of the i^{th} row |
AICc is not calculated when .
Term | Description |
---|---|
p | the number of coefficients in the model, including the constant |
n | the number of rows in the data with no missing data |
Term | Description |
---|---|
p | the number of coefficients in the model, not counting the constant |
n | the number of rows in the data with no missing data |
where the following equation represents the error deviance:
Term | Description |
---|---|
N(Test) | the number of rows in the test data set |
the squared deviance residuals | |
y_{i} | the number of events for the i^{th} row in the test data set |
m_{i} | the number of trials for the i^{th} row in the test data set |
D_{E}(Test) | the error deviance for the test data set |
D_{T}(Test) | the total deviance for the test data set |
Where
and D_{T} is the total deviance.
Term | Description |
---|---|
K | number of folds |
n_{j} | sample size of fold j |
cross validated deviance residual for the i^{th} row of fold j |