Please use dput() to supply data and send in text format not html. Thanks
John Kane Kingston ON Canada > -----Original Message----- > From: mrahmankuf...@gmail.com > Sent: Wed, 24 Jul 2013 19:00:42 +0800 > To: r-help@r-project.org, r-help-requ...@r-project.org, > r-help-ow...@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 > 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ____________________________________________________________ FREE 3D MARINE AQUARIUM SCREENSAVER - Watch dolphins, sharks & orcas on your desktop! ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
