On 04/12/2012 03:50 AM, Bradley Carlson wrote:
I'm performing an analysis of behavioral variation among individual
tadpoles, using individual ID as a random effect and time as a continuous
fixed covariate in the lmer function in lmer4 package. I'm really
interested in making inferences about the random effect (i.e. the extent of
variation among individuals). I'd like to do two things that I can't seem
to find straightforward answers about and I'm hoping someone can help or
point me to a good resource.
1) The intraclass correlation coefficient is of particular interest to me,
as it describes the proportion of variation that occurs among individuals.
Ideally I'd like to report a confidence interval of the ICC but I can't
find any way to calculate one, other than a function in the psychometric
package that appears to only work when there are no covariates in the model
(random effect only).
MCMC has already been mentioned and lme4 still has its mcmcsamp()
function. Failing that, you could try a parametric bootstrap, which
requires a little bit of coding but simulate() makes it much easier.
2) A reviewer requested a power analysis of the ability to detect a
significant random effect. Any tips on how to approach that?
Report the random effect and confidence intervals. Retrospective power
analyses are pretty pointless (e.g. see
http://beheco.oxfordjournals.org/content/14/3/446.full), unless you're
planning to repeat the experiment.
Bob
--
Bob O'Hara
Biodiversity and Climate Research Centre
Senckenberganlage 25
D-60325 Frankfurt am Main,
Germany
Tel: +49 69 798 40226
Mobile: +49 1515 888 5440
WWW: http://www.bik-f.de/root/index.php?page_id=219
Blog: http://blogs.nature.com/boboh
Journal of Negative Results - EEB: www.jnr-eeb.org
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