[R-sig-ME] Logit mixed model power analysis

Paul Johnson paul.johnson at glasgow.ac.uk
Tue Sep 15 18:21:00 CEST 2015

Hi Evenlien,

I second what Thierry said about the pointlessness of post-hoc power analysis when using the observed effect size.

This tutorial by Ben Bolker shows how to do power analysis for a binomial GLMM:
NB you don’t need the first 3 lines any more (assuming you have a reasonably up-to-date version of lme4).

I also recommend Chapter 5 of Ben’s book Ecological Models and Data in R as an introduction to simulation, including power analysis.

Our paper on simulation-based power analysis for GLMMs 
which Paul Debes mentioned, has a supplementary online R tutorial, including a binomial example. 

Best wishes,

> On 15 Sep 2015, at 16:32, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
> Dear Evenlien,
> Post-hoc power tests are not very informative. You will get a high
> power when the signal is significant and low power when not
> significant.
> You can always use a brute force approach to estimate the power.
> Simulate a dataset with know effect size. Analyse that dataset with
> your model and store the relevant p-values. Repeat this so you get N
> simulated datasets for that specific effect size. Then power = mean(p
> < alpha).
> Best regards,
> 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
> 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
> 2015-09-14 9:58 GMT+02:00 Evelien Heyselaar <ev.heys at gmail.com>:
>> Hi,
>> Firstly, I'm sorry if this question has been asked (and answered) before
>> (although I couldn't find it). I'm doing my analysis using logit mixed
>> models (family = binomial(link = "logit"), glmer model) and I was wondering
>> if there was a way to calculate power for my final model. I have looked
>> over the web and all I can find are programs and simulations for linear
>> mixed models with a continuous outcome, but not if the outcome is only 0's
>> and 1's. Is there a way for me to calculate power for this model?
>> Thank you very much,
>> Evelien
>>        [[alternative HTML version deleted]]
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