Hi R-helps,
I did an experiment with FAs ['High' and 'Zero'(no w-3) quality; n=24 for
each group]. Then I did AI to see their sperm competitiveness based on
their paternity performance. My data is as below where Fish ID- Blind ID
for each fish; Group ID- Dietary group ID; Diet quality - High=1, zero=0;
Babies for paternity- total no. of babies got from females; Success -
Babies shared/paterned by focal male; Failure - Babies shared/paterned by
competitor, Proportion - Success/(Success+Failure).
Fish ID Group ID Diet quality Babies for paternity Success Failure
Proportion 1 High 1 9 5 4 0.556 12 High 1 7 5 2 0.714 15 High 1 7 4 3
0.571 20 High 1 6 5 1 0.833 32 High 1 7 2 5 0.286 37 High 1 3 1 2 0.333
48 High 1 4 1 3 0.25 53 High 1 10 0 10 0 65 High 1 3 3 0 1 70 High 1 4 4
0 1 77 High 1 7 2 5 0.286 82 High 1 6 6 0 1 96 High 1 8 2 6 0.25 104
High 1 12 10 2 0.833 111 High 1 4 3 1 0.75 123 High 1 6 5 1 0.833 128
High 1 8 8 0 1 133 High 1 6 5 1 0.833 144 High 1 12 6 6 0.5 152 High 1 13
11 2 0.846 159 High 1 8 1 7 0.125 164 High 1 4 1 3 0.25 169 High 1 6 2 4
0.333 5 Zero 0 9 4 5 0.444 10 Zero 0 7 2 5 0.286 17 Zero 0 7 3 4 0.429
22 Zero 0 6 1 5 0.167 36 Zero 0 7 5 2 0.714 39 Zero 0 3 2 1 0.667 44 Zero
0 4 3 1 0.75 51 Zero 0 10 10 0 1 63 Zero 0 3 0 3 0 68 Zero 0 4 0 4 0 73
Zero 0 7 5 2 0.714 84 Zero 0 6 0 6 0 94 Zero 0 8 6 2 0.75 106 Zero 0 12 2
10 0.167 109 Zero 0 4 1 3 0.25 121 Zero 0 6 1 5 0.167 132 Zero 0 8 0 8 0
137 Zero 0 6 1 5 0.167 142 Zero 0 12 6 6 0.5 154 Zero 0 13 2 11 0.154 157
Zero 0 8 7 1 0.875 168 Zero 0 4 3 1 0.75 173 Zero 0 6 4 2 0.667
I ran the following codes to have my results:
###Proportion estimate:
p-Data$Success/(Data$Success+Data$Failure)
plot(Data$Group.ID,p,ylab=Proportion of success)
###Response variable:
y-cbind(Data$Success,Data$Failure)
model1 - glm(y~Diet.quality, data=Data, family=binomial)
summary(model1)
plot(model1)# gives Q-Q plots
###The residual deviance is 152.66 on 44 d.f. so the model is quite badly
overdispersed:
#152.66/44 where The overdispersion factor is almost 3.46 (unbelievable).
## model with logit link functions and weights:
model2-glm(cbind(Success,Failure)~Group.ID,data=Data,
family=binomial(link=logit),weights=Success+Failure)
summary(model2)
###The residual deviance is 1196.1 on 46 d.f. so the model is quite badly
overdispersed:
#1192.1/44 where The overdispersion factor is almost 27.09 (unbelievable).
#The simplest way to take this into account is to use what is called an
#empirical scale parameter to reflect the fact that the errors are not
#binomial as we assumed, but were larger than this (overdispersed) by a
factor of 3.38.
model3-glm(y ~ Group.ID,data=Data,family=quasibinomial)
summary(model3)
###Note that the ratio of the residual deviance and the degrees of freedom
is still
#larger than 1, but that is no longer a problem as we now allow for
overdispersion.
Each models gives me different results with overdispersion. So, can any one
help me to give me some valuable suggesions to solve this problem. I'll
really appreciate your kind assistance and will grateful to you forever.
With kind regards,
Moshi
mrahmankuf...@gmail.com
--
MD. MOSHIUR RAHMAN
PhD Candidate
School of Animal Biology/Zoology (M092)
University of Western Australia
35 Stirling Hwy, Crawley, WA, 6009
Australia.
Mob.: 061-425205507
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