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. 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]]
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