[R-sig-ME] Question on calculating the conflidence limits on the intraclass correlation in a nested binomial mixed model
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Tue Sep 17 11:11:23 CEST 2013
Dear Tom,
A random effect with only two levels is not a good idea. You will get very unreliable variance estimates at best. Note the profiled confidence interval goes from 0 to Inf... Therefore I recommend fitting group as a random effect.
Furthermore you profile the variance of random effects, but not the total variance. I think you should profile both, since they are correlated. Or calculate a profiled Vtot on the profile of the sigmas.
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
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-----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 16 september 2013 18:46
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] Question on calculating the conflidence limits on the intraclass correlation in a nested binomial mixed model
Dear all,
I calculated the intraclass correlations at two nested levels in a binomial mixed model from the random effect variances, and tried to calculate the 95% confidence limits on them from the confidence limits on the variance components, obtained by profiling the likelihood:
(I have 2 groups and ca 10 subgroups per group)
fit = glmer(trait ~ 1 + (1|group/subgroup), family = binomial(link="logit"),data=mydata)
vcomps = as.numeric(VarCorr(fit))
V.link = (pi^2)/3
V.tot= vcomps [1] + vcomps [2] + V.link
ICC.group = results[2]/V.tot
ICC.subgroup = results[1]/V.tot
icc=c(ICC.group,ICC.subgroup)
pr=profile(fit,signames=F)
prclims=(confint(pr,level=level)^2)/V.tot
low=prclims[2:1,1] # upper and lower CIs on the ICCs
up=prclims[2:1,2]
results = round(data.frame(icc, low, up),3)
row.names(results) = c("group", "subgroup")
results
# icc low up
# group 0.000 0.000 Inf
# subgroup 0.095 0.006 0.303
My question is whether this approach would be OK? I only consider the uncertainty in the estimation of the among-group and among-subgroup variances here, keeping the total variance constant. Is that OK? And what is the implicit assumption in calculating the confidence limits this way - e.g. what sources of uncertainty are considered then? (I also came across this reference, https://stuiterproxy.kuleuven.be/doi/abs/10.1080/,DanaInfo=www.tandfonline.com+03610910903324834, about calculating the conf lims on the ICC using likelihood profiling, not sure if that is relevant here).
I also tried calculating the confidence limits using parametric bootstrapping (using bootMer), but there I ran into problems with non convergence in the fits, owing to the small size of my dataset. And then I tried nonparametric cluster bootstrapping, whereby I resampled over subgroups (resampling the same nr of subgroups pers groups as I had initially), but I am not sure that that is entirely correct either (e.g. it doesn't take into account the binomial scatter per subgroup cluster, and also resampling within clusters biases my estimate, so I can't do that). Or does anyone perhaps happen to have other ideas about how to calculate confidence limits on my estimated intraclass correlation?
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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