For the record
residuals(model)
1 2 3 4 5
5.55860143 -0.00073852 2.49255235 -1.41987341 -0.00042425
6 7 8
-0.94389158 2.72987046 -1.15760836
residuals(model, "pearson")
1 2 3 4 5
3.5362e+03 -5.2222e-04 2.3366e+00 -1.0080e+00 -2.9999e-04
6 7 8
-8.8378e-01 2.4038e+00 -1.1646e+00
fitted(model)
1 2 3 4 5
1.5994e-08 5.0502e-09 4.9946e-01 1.5873e-02 3.2140e-09
6 7 8
2.0924e-02 8.0191e-01 6.1900e-01
so according to the model a very rare event occurred. That is what is
'unrealistic' (and Ben Bolker supposed correctly).
How dispersion should be estimated is a matter of some debate (see
e.g. McCullagh and Nelder), but the model here is simply inadequate.
On Mon, 2 Mar 2009, Menelaos Stavrinides wrote:
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
[[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
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