According to pp 197-198 of MASS 4, the Hauck-Donner phenomenon refers to cases when the Wald approximations and the likelihood ratio tests have different p values because of the former underestimating the the change in log-likelihood on setting \beta_i = 0.
This seems quite different to me than the phenomenon that Daniel is reporting, and that I believe I saw previously (post: http://tolstoy.newcastle.edu.au/R/e2/help/06/12/6903.html ) I tried an earlier version of R, on a different platform, and got quite different results. Sadly, the *earlier* results are the ones that make sense. eg: > sessionInfo() R version 2.4.1 Patched (2006-12-30 r40330) i386-unknown-freebsd6.1 locale: C attached base packages: [1] "stats" "graphics" "grDevices" "utils" "datasets" "methods" [7] "base" other attached packages: lme4 Matrix lattice "0.9975-10" "0.9975-6" "0.14-16" > ranef(lmer(Reaction ~ Days + (1|Subject), sleepstudy, family=Gamma(link="log"))) An object of class "ranef.lmer" [[1]] (Intercept) 308 6.817268e-10 309 -1.369242e-09 310 -1.122033e-09 330 1.164825e-10 331 2.096848e-10 332 1.494418e-10 333 3.042078e-10 334 -6.276876e-11 335 -7.556428e-10 337 1.263863e-09 349 -3.984973e-10 350 2.107439e-10 351 -1.230185e-10 352 6.409427e-10 369 1.224258e-10 370 -1.528146e-10 371 -5.310404e-11 372 3.228682e-10 > sessionInfo() Version 2.3.1 (2006-06-01) i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" [7] "base" other attached packages: lme4 Matrix lattice "0.995-2" "0.995-20" "0.13-8" > ranef(lmer(Reaction ~ Days + (1|Subject), sleepstudy, + family=Gamma(link="log"))) An object of class "ranef.lmer" [[1]] (Intercept) 308 0.128473227 309 -0.294234827 310 -0.232009186 330 0.029091372 331 0.046196655 332 0.035400265 333 0.063273674 334 -0.004238362 335 -0.147285458 337 0.220381662 349 -0.070565390 350 0.046822487 351 -0.015994000 352 0.121461879 369 0.030457370 370 -0.021387277 371 -0.002494534 372 0.066650443 > Cheers, Andrew On Mon, Jan 01, 2007 at 06:09:12PM +0000, Dieter Menne wrote: > Daniel Ezra Johnson <johnson4 <at> babel.ling.upenn.edu> writes: > > > > > I am fitting models to the responses to a questionnaire that has > > seven yes/no questions (Item). For each combination of Subject and > > Item, the variable Response is coded as 0 or 1. > > > > I want to include random effects for both Subject and Item. While I > > understand that the datasets are fairly small, and there are a lot of > > invariant subjects, I do not understand something that is happening > > here, and in comparing other subsets of the data. > > > > In the data below, which has been adjusted to show this phenomenon > > clearly, the Subject random effect variance is comparable for A > > (1.63) and B (1.712), but the Item random effect variance comes out > > as 0.109 for B and essentially zero for A (5.00e-10). > ... > > Check the list archives for quite a few postings of Professor Brian Ripley on > the subject of Hauk-Donner. > > > Dieter > > ______________________________________________ > R-help@stat.math.ethz.ch 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. -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 http://www.ms.unimelb.edu.au/~andrewpr http://blogs.mbs.edu/fishing-in-the-bay/ ______________________________________________ R-help@stat.math.ethz.ch 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.
