[R-sig-ME] Testing significance of deviation of sex ratio (prop of females) from a priori predicted proportion

[ONKELINX, Thierry]

Dear Tom,

I would add the null hypothesis as an offset. Then the intercept would be the deviation from that null hypothesis.

Fit <- glmer(cbind(females, males) ~ offset(qlogis(0.75)) + (1|colony), family = binomial, data = data)
library(multcomp)
confint(glht(Fit))

Addingspacesmakescodemuchmorereadable.

Best regards,

Thierry

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op 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-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Tom Wenseleers
Verzonden: maandag 20 januari 2014 0:54
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] Testing significance of deviation of sex ratio (prop of females) from a priori predicted proportion

Dear all,
I have counts of males and females produced by different colonies of a species of social insects.
I fitted the model
Fit=glmer(cbind(females,names)~1+(1|colony),family=binomial,data=data)

However, how can I test within such a framework if the overall average sex ratio deviates from an a priori predicted value (e.g. half females, or ¾ females)?
I presume this would have to be done based on the fitted intercept. But how does one do this? Also, what would be the best way to get 95% conf lims on the estimate? Using likelihood profiling, or parametric bootstrap? Does anybody happen to have any example calculation?

Cheers,
Tom


_______________________________________________________________________________________

Prof. Tom Wenseleers
*      Lab. of Socioecology and Social Evolution
           Dept. of Biology
           Zoological Institute
           K.U.Leuven
           Naamsestraat 59, box 2466
           B-3000 Leuven
           Belgium
* +32 (0)16 32 39 64 / +32 (0)472 40 45 96
* tom.wenseleers op bio.kuleuven.be
http://bio.kuleuven.be/ento/wenseleers/twenseleers.htm



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