THANKS so very much for your help (previous and future!). I have a two
follow-up questions.
1) You say that dispersion = 1 by definition ....dispersion changes from 1
to 13.5 when I go from binomial to quasibinomial....does this suggest that
I should use the binomial? i.e., is the dispersion factor more important
that the
2) Is there a cutoff for too much overdispersion - mine seems to be
huge......Residual deviance: 1580.1 on 123 degrees of freedom
I do have some outliers - but they are legitimate (i.e., not typos).....
I included my data below....if it helps
summary(glm.D93)$dispersion ## 1 (by definition)
Call:
glm(formula = y ~ Pred1, family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-9.940 -2.778 -0.710 2.130 10.479
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.63942 0.07205 22.753 < 2e-16 ***
Pred1F -0.65228 0.11781 -5.537 3.08e-08 ***
Pred1O -3.03239 0.12782 -23.724 < 2e-16 ***
Pred1SN -3.60714 0.11057 -32.623 < 2e-16 ***
Pred1W -1.22131 0.10734 -11.378 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3506.7 on 127 degrees of freedom
Residual deviance: 1580.1 on 123 degrees of freedom
(1 observation deleted due to missingness)
AIC: 1863.1
Number of Fisher Scoring iterations: 5
> glm1<-glm(y~Pred1,"quasibinomial")
> summary(glm1)
Call:
glm(formula = y ~ Pred1, family = "quasibinomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-9.940 -2.778 -0.710 2.130 10.479
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6394 0.2646 6.197 7.96e-09 ***
Pred1F -0.6523 0.4326 -1.508 0.13415
Pred1O -3.0324 0.4693 -6.461 2.19e-09 ***
Pred1SN -3.6071 0.4060 -8.885 6.51e-15 ***
Pred1W -1.2213 0.3941 -3.099 0.00241 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 13.48239)
Null deviance: 3506.7 on 127 degrees of freedom
Residual deviance: 1580.1 on 123 degrees of freedom
(1 observation deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
DATA
Pred1 PercentSurvival
O 0.181818182
O 0
O 0
O 0
O 0
F 0.766666667
F 0.967741935
F 0.8
F 0.966666667
F 0.833333333
F 0.775
F 0.641025641
F 0.272727273
F 0.606060606
F 0.621621622
F 0.574468085
F 0.918918919
F 0.854166667
F 0.684210526
A 0.438596491
A 0.8
A 0.125
A 0.936507937
A 0.911764706
A 0.75
A 0
A 0.64
A 0.740740741
A 0.703703704
A 0.962962963
A 0.911111111
A 0.97260274
A 0.842105263
A 0.795454545
A 0.970588235
A 0.755555556
A 0.947368421
A 1
A 0.947368421
A 0.785714286
A 0.782178218
A 1
A 0.6
A 0.875
A 0.625
A 0.666666667
A 1
A 1
A 0.611111111
A 0.916666667
A 0.625
A 0.97826087
A 0.975
A 0.933333333
A 1
A 1
A 0.930232558
A 0.810810811
O 0
O 0
O 1
O 0
SN 0
SN 0
SN 0.696969697
SN 0
SN 0.533333333
SN 0
SN 0.027777778
SN 0.6
SN 0.052631579
SN 0
SN 0
SN 0
SN 0.619047619
SN 0
SN 0
SN 0
SN 0.08
SN 0
SN 0.090909091
SN 0
SN 0.5
SN 0
SN 0.78125
SN 0
SN 0
SN 0
SN 0
SN 0.542857143
SN 0
SN 0
SN 0
SN 0
SN 0.4
SN 0
SN 0
SN 0.433333333
O 0.655172414
O 0.238095238
O 0
O 0.409090909
O 0
O 0
O 0.090909091
O 0.310344828
O 0
O 0
O 0
O 0.783783784
W 0.928571429
W 0
W 0.651162791
W 0.3125
W 0.871794872
W 0.511627907
W 0.566666667
W 0.756756757
W 0
W 0.666666667
W 0.55
W 0.826086957
W 0.8
W 0.682926829
W 0.586206897
W 1
W 0.75
W 0.5625
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