|From||Richard Williams <Richard.A.Williams.email@example.com>|
|Subject||RE: st: Residuals in Logistic Regression|
|Date||Fri, 09 Apr 2004 16:58:30 -0500|
I never understood the rationale of this whole business. It has to do with whether you focus on individual observations (in which case your degrees of freedom equal your total sample size) or covariate patterns (in which case, the degrees of freedom are dependent on what covariates you include in the model). H&L prefer the latter. I think it leads to certain unintuitive situations. For example, suppose you fit a model with only an intercept. All the residuals will be 0 in this case (there's only a single covariate pattern, ie, the entire sample). Surely that cannot be right? Perfect fit? I don't think so.Thanks Constantine. Another intuitive oddity: I took some of the tied covariate patterns and added .0001 to the value of a variable in one case and subtracted .0001 from the value of a variable in another. It made no difference in the parameter estimates but it greatly changed the residuals. So, trivial differences in the covariate patterns can create huge differences in the residuals.