I am running a binomial glm with response variable the no of mites of two
species y->cbind(mitea,miteb) against two continuous variables (temperature
and predatory mites) - see below. My model shows overdispersion as the
residual deviance is 48.81  on 5  degrees of freedom. If I use quasibinomial
to account for overdispersion the dispersion parameter estimate is  2501139,
which seems unrealistic. Any ideas as to why I am getting such a huge
dispersion parameter?

> y<-cbind(psmno,wsmno)
> ldhours<-log(idhours+1)
> lwpm<-log(wpm2wkb+1)
> y
     psmno wsmno
[1,]     1     4
[2,]     0    54
[3,]     8     1
[4,]     0    63
[5,]     0    28
[6,]     4   291
[7,]    46     3
[8,]   117    85
> ldhours
[1] 0.000000 2.308567 5.078473 4.875035 2.339399 3.723039 5.572344 5.250384
> lwpm
[1] 0.6931472 2.1972246 0.0000000 0.6931472 2.3025851 0.0000000 0.0000000
[8] 0.0000000
> model<-glm(y~ldhours+lwpm,binomial)
> summary(model)

Call:
glm(formula = y ~ ldhours + lwpm, family = binomial)

Deviance Residuals:
         1           2           3           4           5           6
 5.5586025  -0.0007385   2.4925511  -1.4198734  -0.0004242  -0.9438916
         7           8
 2.7298663  -1.1576062

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -14.4029     1.3705 -10.509  < 2e-16 ***
ldhours       2.8357     0.2656  10.677  < 2e-16 ***
lwpm         -5.1188     1.4689  -3.485 0.000492 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 441.20  on 7  degrees of freedom
Residual deviance:  48.81  on 5  degrees of freedom
AIC: 70.398

Number of Fisher Scoring iterations: 8

> model2<-glm(y~ldhours+lwpm,quasibinomial)
> summary(model2)

Call:
glm(formula = y ~ ldhours + lwpm, family = quasibinomial)

Deviance Residuals:
         1           2           3           4           5           6
 5.5586025  -0.0007385   2.4925511  -1.4198734  -0.0004242  -0.9438916
         7           8
 2.7298663  -1.1576062

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -14.403   2167.435  -0.007    0.995
ldhours        2.836    420.015   0.007    0.995
lwpm          -5.119   2323.044  -0.002    0.998

(Dispersion parameter for quasibinomial family taken to be 2501139)

    Null deviance: 441.20  on 7  degrees of freedom
Residual deviance:  48.81  on 5  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 8

Thanks,
Mel

-- 
Menelaos Stavrinides
Ph.D. Candidate
Environmental Science, Policy and Management
137 Mulford Hall MC #3114
University of California
Berkeley, CA 94720-3114 USA
Tel: 510 717 5249

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