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
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