Appendix C. Method of model selection for the logistic and Poisson regressions.
The “complete” logistic regression model for each mortality factor took the general form
,
|
(C.1) |
where pj = probability an individual died due to factor j, X1 = density of moth stage specific to factor j, X2 = patch size, and X3 = local patch density. The fecundity data were analyzed by multiple Poisson regression (SAS 2001, PROC Genmod). The complete model took the form
,
|
(C.2) |
where
= number of eggs per female. For each mortality factor and fecundity, Atkinson’s Q was calculated for the complete model plus all seven possible subsets of explanatory variables, including the null model.
,
|
(C.3) |
where a = 4 (constant), q = number of explanatory variables in the model being tested,
= scaling parameter for the complete model. Only observations with no missing data for all three explanatory variables were used. The “best” model among the eight most closely fit two criteria: (1) it minimized Q and (2) the individual plots of mortality against each explanatory variable retained in step 1 confirmed a strong relationship. If none of the models fit both criteria, the null model was selected as the best model.
LITERATURE CITED
SAS Institute. 2001. The SAS System. Version 8.