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
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