Please use dput() to supply data and send in text format not html.
Thanks
John Kane
Kingston ON Canada
> -----Original Message-----
> From: mrahmankufmrt at gmail.com
> Sent: Wed, 24 Jul 2013 19:00:42 +0800
> To: r-help at r-project.org, r-help-request at r-project.org,
> r-help-owner at r-project.org
> Subject: [R] Paternity data analysis problem
>
> Dear 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
> anyone
> help me to give me some valuable suggesions to solve this problem. I'll
> really appreciate your kind assistance and will be grateful to you
> forever.
>
> With kind regards,
>
> Moshi
> mrahmankufmrt at 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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>
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