Hi Russell,
You might join in on the discussion on the R-sig-ME mixed effects listserv (see e.g. Doug Bates' message today (Sept 16 2008) titled Re: [R-sig-ME] glmer and overdispersed Poisson models) There Prof. Bates suggests that older versions of lmer() may be doing things more appropriately (though maybe not yet optimally). (He also asks for input on how this might be addressed - I'm not yet quasi-specialized enough to figure it out but maybe someone reading this will be.) Here's what I get with a slightly older version of lmer - note that my quasi-binomial t-value is not as extreme as yours. Note also that for the output below, the ratio of the standard deviation estimates for the intercept estimate > 0.1219/0.07441 [1] 1.638221 equals the residual standard deviation, so it appears that for the quasibinomial case lmer or some lower level routine estimates the scale parameter phi using the residual standard deviation, though I have not delved into the source code to verify this. So some attempt to modify scale appears to be in place for this version of lmer. library(lme4) set.seed(12) eta=rnorm(50) p=exp(eta)/(1+exp(eta)) y=rbinom(50,20,p)/20 #IID overdispersed binomial-normal proportions #y=rbinom(50,20,0.5)/20 #IID binomial(20,0.5) Group=rep(c("A","B","C","D","E"),c(10,10,10,10,10)) lmer(y~1+(1|Group),weights=rep(20,50),family="binomial") lmer(y~1+(1|Group),weights=rep(20,50),family="quasibinomial") sessionInfo() > library(lme4) > set.seed(12) > eta=rnorm(50) > p=exp(eta)/(1+exp(eta)) > y=rbinom(50,20,p)/20 #IID overdispersed binomial-normal proportions > #y=rbinom(50,20,0.5)/20 #IID binomial(20,0.5) > Group=rep(c("A","B","C","D","E"),c(10,10,10,10,10)) > lmer(y~1+(1|Group),weights=rep(20,50),family="binomial") Generalized linear mixed model fit using Laplace Formula: y ~ 1 + (1 | Group) Family: binomial(logit link) AIC BIC logLik deviance 149.7 153.6 -72.87 145.7 Random effects: Groups Name Variance Std.Dev. Group (Intercept) 0.0075745 0.087032 number of obs: 50, groups: Group, 5 Estimated scale (compare to 1 ) 1.638542 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.13739 0.07441 -1.847 0.0648 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > lmer(y~1+(1|Group),weights=rep(20,50),family="quasibinomial") Generalized linear mixed model fit using Laplace Formula: y ~ 1 + (1 | Group) Family: quasibinomial(logit link) AIC BIC logLik deviance 149.7 153.6 -72.87 145.7 Random effects: Groups Name Variance Std.Dev. Group (Intercept) 0.020336 0.14261 Residual 2.684818 1.63854 number of obs: 50, groups: Group, 5 Fixed effects: Estimate Std. Error t value (Intercept) -0.1374 0.1219 -1.127 > sessionInfo() R version 2.7.1 Patched (2008-06-25 r45988) powerpc-apple-darwin8.11.1 locale: C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] RMySQL_0.6-0 DBI_0.2-4 RODBC_1.2-3 lme4_0.99875-9 [5] Matrix_0.999375-9 lattice_0.17-10 loaded via a namespace (and not attached): [1] grid_2.7.1 > Steven McKinney Statistician Molecular Oncology and Breast Cancer Program British Columbia Cancer Research Centre email: smckinney +at+ bccrc +dot+ ca tel: 604-675-8000 x7561 BCCRC Molecular Oncology 675 West 10th Ave, Floor 4 Vancouver B.C. V5Z 1L3 Canada -----Original Message----- From: [EMAIL PROTECTED] on behalf of [EMAIL PROTECTED] Sent: Tue 9/16/2008 3:18 PM To: r-help@r-project.org Subject: [R] Using quasibinomial family in lmer Dear R-Users, I can't understand the behaviour of quasibinomial in lmer. It doesn't appear to be calculating a scaling parameter, and looks to be reducing the standard errors of fixed effects estimates when overdispersion is present (and when it is not present also)! A simple demo of what I'm seeing is given below. Comments appreciated? Thanks, Russell Millar Dept of Stat U. Auckland PS. I'm using the latest version of lme4 (0.999375-26) with R 2.7.2. > eta=rnorm(50) > p=exp(eta)/(1+exp(eta)) > y=rbinom(50,20,p)/20 #IID overdispersed binomial-normal proportions > #y=rbinom(50,20,0.5)/20 #IID binomial(20,0.5) > > Group=rep(c("A","B","C","D","E"),c(10,10,10,10,10)) > > #library(lme4) > > lmer(y~1+(1|Group),weights=rep(20,50),family="binomial") Generalized linear mixed model fit by the Laplace approximation Formula: y ~ 1 + (1 | Group) AIC BIC logLik deviance 211 214.8 -103.5 207 Random effects: Groups Name Variance Std.Dev. Group (Intercept) 0.072891 0.26998 Number of obs: 50, groups: Group, 5 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.2194 0.1367 1.605 0.108 > > lmer(y~1+(1|Group),weights=rep(20,50),family="quasibinomial") Generalized linear mixed model fit by the Laplace approximation Formula: y ~ 1 + (1 | Group) AIC BIC logLik deviance 213 218.7 -103.5 207 Random effects: Groups Name Variance Std.Dev. Group (Intercept) 0.0032632 0.057125 Residual 0.0447685 0.211586 Number of obs: 50, groups: Group, 5 Fixed effects: Estimate Std. Error t value (Intercept) 0.21940 0.02892 7.586 > ______________________________________________ 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. ______________________________________________ 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.