Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm().
1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for the Chi-squared tests. When not supplying the dispersion argument, i.e. when anova.glm() uses the default scaled Pearson statistic from family=quasibinomial, both tests returns the p-values I'd expect. What am I doing wrong here and how can I make it work? > fit.1<-glm(y/n~host*variety,family=quasibinomial,weights=n) > dscale<-sum(residuals(fit.1,type="deviance")^2/fit.1$df.residual) > dscale [1] 1.957517 > anova(fit.1,test="F",dispersion=dscale) Analysis of Deviance Table Model: quasibinomial, link: logit Response: y/n Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 20 98.719 host 1 55.969 19 42.751 28.5916 8.937e-08 variety 1 3.065 18 39.686 1.5657 0.2108 host:variety 1 6.408 17 33.278 3.2736 0.0704 I expected: > 1-pf(3.2736,1,17) [1] 0.08812074 > anova(fit.1,test="Chisq",dispersion=dscale) Analysis of Deviance Table Model: quasibinomial, link: logit Response: y/n Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL 20 98.719 host 1 55.969 19 42.751 8.937e-08 variety 1 3.065 18 39.686 0.211 host:variety 1 6.408 17 33.278 0.070 As expected: > 1-pchisq(6.408/dscale,1) [1] 0.07040576 2. When using summary.glm() on a glm object fitted using family=quasibinomial the reported tests are t-tests. Why? Thanks, Henric ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help