[R-sig-ME] [R] Question on overdispersion

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Fri Nov 19 10:59:11 CET 2010


Dear Jarrod,

Thanks for the information 

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
  

> -----Oorspronkelijk bericht-----
> Van: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk] 
> Verzonden: vrijdag 19 november 2010 10:52
> Aan: ONKELINX, Thierry
> CC: cct663; r-help at r-project.org; r-sig-mixed-models at r-project.org
> Onderwerp: Re: [R-sig-ME] [R] Question on overdispersion
> 
> Hi Thierry + nameless,
> 
> It is not necessary to expand the binomial into Bernoulli 
> trials (nor advisable if n and/or the binomial size are 
> large). You can just fit observation-level random effects:
> 
> dataset$resid<-as.factor(1:dim(dataset)[1])
> 
> fit3 <- glmer(cbind(male_chick_no, female_chick_no) ~ 
> 1+(1|FemaleID)+ (1|resid), data = dataset, family = binomial)
> 
> gives the same answer as fit2
> 
> Cheers,
> 
> Jarrod
> 
> 
> 
> 
> 
> 
> Quoting "ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be>:
> 
> > Dear Nameless,
> >
> > The quasi distribution can no longer be used in lme4 because a) the 
> > results were not very reliable b) there is an alternative to model 
> > overdispersion.
> >
> > The alternative is to expand your dataset to bernoulli trials. Then 
> > add a random effect with one level per observation. This 
> random effect 
> > will model additive overdisperion. The quasi distributions model 
> > overdisperion multiplicative.
> >
> > In the example below, the random effect of RowID has 0 
> variance. Hence 
> > no overdispersion.
> >
> > dataset <- data.frame(
> > 	male_chick_no = c(2,4,1,0,3,5,2),
> > 	female_chick_no=c(1,0,3,3,1,0,2),
> > 	FemaleID=c("A","A","B","B","C","D","E"))
> >
> > longFormat <- do.call(rbind, lapply(seq_len(nrow(dataset)), 
> function(i){
> > 	with(dataset, data.frame(Sex = c(rep("M", 
> male_chick_no[i]), rep("F", 
> > female_chick_no[i])), FemaleID = FemaleID[i]))
> > }))
> > longFormat$FemaleID <- factor(longFormat$FemaleID) 
> longFormat$RowID <- 
> > factor(seq_len(nrow(longFormat))) longFormat$Male <- 
> longFormat$Sex == 
> > "M"
> >
> > library(lme4)
> > fit1 <- glmer(Male ~ (1|FemaleID), data = longFormat, family = 
> > binomial)
> > fit2 <- glmer(Male ~ (1|FemaleID) + (1|RowID), data = longFormat, 
> > family = binomial) anova(fit1, fit2)
> >
> > Best regards,
> >
> > Thierry
> >
> > PS sig-mixed-models is a better mailinglist for this kind 
> of questions.
> >
> > 
> ----------------------------------------------------------------------
> > --
> > ----
> > ir. Thierry Onkelinx
> > Instituut voor natuur- en bosonderzoek team Biometrie & 
> Kwaliteitszorg 
> > Gaverstraat 4 9500 Geraardsbergen Belgium
> >
> > Research Institute for Nature and Forest team Biometrics & Quality 
> > Assurance Gaverstraat 4 9500 Geraardsbergen Belgium
> >
> > tel. + 32 54/436 185
> > Thierry.Onkelinx at inbo.be
> > www.inbo.be
> >
> > To call in the statistician after the experiment is done may be no 
> > more than asking him to perform a post-mortem examination: 
> he may be 
> > able to say what the experiment died of.
> > ~ Sir Ronald Aylmer Fisher
> >
> > The plural of anecdote is not data.
> > ~ Roger Brinner
> >
> > The combination of some data and an aching desire for an 
> answer does 
> > not ensure that a reasonable answer can be extracted from a 
> given body 
> > of data.
> > ~ John Tukey
> >
> >
> >> -----Oorspronkelijk bericht-----
> >> Van: r-help-bounces at r-project.org
> >> [mailto:r-help-bounces at r-project.org] Namens cct663
> >> Verzonden: vrijdag 19 november 2010 5:39
> >> Aan: r-help at r-project.org
> >> Onderwerp: [R] Question on overdispersion
> >>
> >>
> >> I have a few questions relating to overdispersion in a sex 
> ratio data 
> >> set that I am working with (note that I already have an 
> analysis with 
> >> GLMMs for fixed effects, this is just to estimate dispersion). The 
> >> response variable is binomial because nestlings can only 
> be male or 
> >> female. I have samples of
> >> 1-5 nestlings from each nest (individuals within a nest are not 
> >> independent, so the response variable is the ratio of sons to 
> >> daughters) and some females have multiple nests in the 
> data set (so I 
> >> need to include female identity as a random effect).
> >>
> >> Here is an example of what the three vectors used in the 
> model look 
> >> like (the real data set is much bigger, just to illustrate 
> what I'm 
> >> talking
> >> about):
> >>
> >> male_chick_no=c(2,4,1,0,3,5,2)
> >> female_chick_no=c(1,0,3,3,1,0,2)
> >> FemaleID=c(A,A,B,B,C,D,E)
> >>
> >> My first question relates to coding the test in R. I received this 
> >> suggested R syntax from a reviewer:
> >>
> >> SexRatio = cbind(male_chick_no, female_chick_no)
> >>
> >> Model <- lmer(SexRatio ~ 1 +(1|FemaleID), family = quasibinomial)
> >>
> >> But when I try to use this in R I get the error: "Error in 
> >> glmer(formula = ratio ~ 1 + (1 | femid), family =
> >> quasibinomial) : "quasi" families cannot be used in glmer".
> >>
> >> I've tried playing around with some other mixed model 
> functions but 
> >> can't seem to find one that will provide an estimate of dispersion 
> >> and allow me to include my random effect.
> >>
> >> Is there some other function I should be using? Or is there a 
> >> different syntax that I should use for lmer?
> >>
> >> My second question is more general: I understand that with 
> binomial 
> >> data overdispersion suggests that the observed data have a greater 
> >> variance than expected given binomial errors (in my case 
> this means 
> >> that more nests would be all male/all female than expected 
> if sex is 
> >> random). So with binomial errors the expected estimate of 
> dispersion 
> >> is 1, if I find that dispersion is > 1 it suggests that my 
> data are 
> >> overdispersed. My question is, how much greater than 1 should that 
> >> number be to conclude that the data are overdispersed?
> >> Is there a rule of thumb or does it just depend on the dataset?
> >>
> >> I was thinking of doing a randomization test with the same 
> structure 
> >> (nest size and female id) as my real data set but with sex 
> ratio of 
> >> each nest randomized with a 50:50 chance of individuals 
> being sons or 
> >> daughters and comparing my observed dispersion to the 
> distribution of 
> >> dispersions from the randomization test. Would this be a 
> valid way to 
> >> ask whether my data are overdispersed? Is it even necessary?
> >>
> >> Any help/advice that you can provide would be greatly 
> appreciated. I 
> >> am relatively new to R so explicit instructions (i.e. easy 
> to follow) 
> >> would be wonderful.
> >>
> >> Thanks.
> >>
> >> --
> >> View this message in context:
> >> http://r.789695.n4.nabble.com/Question-on-overdispersion-tp304
> >> 9898p3049898.html
> >> Sent from the R help mailing list archive at Nabble.com.
> >>
> >> ______________________________________________
> >> R-help at 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.
> >>
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list 
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
> 
> 
> 
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