Hi All, could someone please shed some light on the proper goodness-of-fit analysis for the GLM output based on Gamma distributions with the log-link?
My objective is to test the goodness-of-fit for the final model (and not the comparison of nested models). In particular, should the 'Residual Deviance' be compared with the Chi-Square distribution, or should the 'Scaled Residual Deviance', i.e. the Residual Deviance divided by the dispersion estimator, be compared with the Chi-Square distribution. Or alternatively, using (7.10) in Venables&Ripley (p.187 MASS 4th) and compare the scaled residual deviance with the appropriate F distribution. Below I have included a sample output from one model. And my concern is if the residual deviance should be divided by the dispersion parameter. Many thanks for any insight or comments. Fredrik Odegaard Null Deviance: 357.12 on 394 d.f. Residual Deviance: 333.51 on 391 d.f. Dispersion parameter: .5551945 Pearson X^2: 217.0827 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
