different F test in drop1 and anova
Yes, in essence although it is much easier to describe in words. anova uses the Chisquared-based estimate of dispersion unless it is known. drop1 uses the deviance-based estimate of dispersion unless it is known. If the F tests are going to be approximately valid the dispersion estimators should be pretty similar, and when they are not the first _may_ be closer to chi-square-distributed. However, as I recall it, when I learnt analysis of deviance using GLIM3, the drop1 approach was used.
On Thu, 20 Oct 2005, Tom Van Dooren wrote:
Hi Brian, well I wanted a test based on quasibinomial... Does it work like this then?: x<-gl(3,2) y<-c(0,1,0,0,1,1) # quasibinomial models # ######################## qb1<-glm(y~x,quasibinomial) qb2<-glm(y~1,quasibinomial) qbdev<-(qb2$dev-qb1$dev) qbdev # deviance I qbdev/(qb2$df.res-qb1$df.res)/(qb1$dev /qb1$df.res) # deviance ratio II qbdev/summary(qb1)$disp # scaled deviance III qbdev/(qb2$df.res-qb1$df.res)/summary(qb1)$disp # scaled deviance IV anova(qb2,qb1,test="Chisq") # Chisq test based on I drop1(qb1,test="F") # F test, based on II drop1(qb1,test="Chisq") # Chisq test, based on III anova(qb2,qb1,test="F") # F test, based on IV # binomial models # ################### b1<-glm(y~x,binomial) b2<-glm(y~1,binomial) bdev<-(b2$dev-b1$dev) bdev # deviance I bdev/(b2$df.res-b1$df.res)/(b1$dev /b1$df.res) # deviance ratio II drop1(b1,test="Chisq") # Chisq test, based on I anova(b2,b1,test="Chisq") # Chisq test based on I anova(b2,b1,test="F") # Chisq test, based on I drop1(b1,test="F") # F test, based on II Cheers, Tom PS: thanks Tord ;)
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595