thanks for the answer! yes, indeed, type and fragment should be factors but it was no artificial data!
2011/6/14 Prof Brian Ripley <rip...@stats.ox.ac.uk> > I presume you intended 'type' and 'fragment' to be factors (see below). > Such a model would fit exactly. The additive model > > > model <- glm(y ~ fragment+type, binomial) >> > > is only modestly over-dispersed, and shows that 'fragment' has zero effect. > Not 'a negligible effect', but no effect. So something really odd is going > on: is this an exercise with artificial data? > Otherwise you need to explain the exact balance between the two 'fragments' > (each fragment has exactly 1/4 success) and your assumption of independent > binomial sampling cannot be true. > > Using a quasibinomial model does not change the deviance (see e.g. > McCullagh and Nelder for the definitions, including of 'scaled deviance')), > but it does change the standard errors. > > > On Mon, 13 Jun 2011, Anna Mill wrote: > > Dear all, >> >> I am new to R and my question may be trivial to you... >> I am doing a GLM with binomial errors to compare proportions of species in >> different categories of seed sizes (4 categories) between 2 sites. >> > > You have types and fragments but no species and no sites. At least 'sites' > should be a factor, as should 'categories of seed sizes'. > > In the model summary the residual deviance is much higher than the degree >> of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even >> after correcting for overdispersion by using a quasibinomial error >> structure >> instead of binomial the residual deviance does not change. Is this a data >> problem and I cannot use this statistic or is it because I do something >> wrong with R (see models attached)? >> >> Thanks a lot for your help! >> Anna >> >> >> first model with binomial error structure: >> >> success<-c(14,43,44,1,13,28,56,8) >>> failure<-c(88,59,58,101,92,77,49,97) >>> "fragment"<-c(1,1,1,1,2,2,2,2) >>> "type"<-c(1,2,3,4,1,2,3,4) >>> y<-cbind(success,failure) >>> model<-glm(y~fragment*type,binomial) >>> summary(model) >>> >> Call: >> glm(formula = y ~ fragment * type, family = binomial) >> >> Deviance Residuals: >> 1 2 3 4 5 6 7 8 >> -4.0175 3.3716 4.5052 -6.0071 -2.8063 0.5449 6.0414 -5.0184 >> >> Coefficients: >> Estimate Std. Error z value Pr(>|z|) >> (Intercept) 0.04433 0.61072 0.073 0.9421 >> fragment -0.65477 0.39001 -1.679 0.0932 . >> type -0.46664 0.23027 -2.027 0.0427 * >> fragment:type 0.26636 0.14455 1.843 0.0654 . >> --- >> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 >> >> (Dispersion parameter for binomial family taken to be 1) >> >> Null deviance: 157.96 on 7 degrees of freedom >> Residual deviance: 153.74 on 4 degrees of freedom >> AIC: 196.31 >> >> Number of Fisher Scoring iterations: 5 >> >> second model with quasibinomial error structure: >> >>> summary(model2) >>> >> >> Call: >> glm(formula = y ~ fragment * type, family = quasibinomial) >> >> Deviance Residuals: >> 1 2 3 4 5 6 7 8 >> -4.0175 3.3716 4.5052 -6.0071 -2.8063 0.5449 6.0414 -5.0184 >> >> Coefficients: >> Estimate Std. Error t value Pr(>|t|) >> (Intercept) 0.04433 3.63550 0.012 0.991 >> fragment -0.65477 2.32169 -0.282 0.792 >> type -0.46664 1.37073 -0.340 0.751 >> fragment:type 0.26636 0.86048 0.310 0.772 >> >> (Dispersion parameter for quasibinomial family taken to be 35.43628) >> >> Null deviance: 157.96 on 7 degrees of freedom >> Residual deviance: 153.74 on 4 degrees of freedom >> AIC: NA >> >> Number of Fisher Scoring iterations: 5 >> >> [[alternative HTML version deleted]] >> >> >> > -- > Brian D. Ripley, rip...@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 > [[alternative HTML version deleted]] ______________________________________________ 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.
