Here are some examples from the archives:

http://tolstoy.newcastle.edu.au/R/e4/help/08/02/2499.html

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001790.html

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001819.html




On Fri, Jul 12, 2013 at 6:57 AM, Charles Determan Jr <deter088@umn.edu>wrote:

> Thank you Greg,
> However, would you be able to direct me to either an example or further
> information regarding simulations to measure power?
>
> Charles
>
>
> On Thu, Jul 11, 2013 at 4:56 PM, Greg Snow <538280@gmail.com> wrote:
>
>> If there were a canned function for power for a non-parametric test, I
>> would not trust it.  This is because there are many assumptions that would
>> need to be made and I would not know if those in a canned function were
>> reasonable for my study.
>>
>> I would compute power by simulation.  Simulate data sets that match what
>> you think the real data will/may look like, analyze the simulated datasets
>> and see what proportion give significant results (that will be your power).
>>  You can do this for different sets of assumptions to get a  feel for how
>> the different assumptions affect your results.  This way you know exactly
>> what assumptions you are making to get your power.
>>
>>
>> On Tue, Jul 9, 2013 at 2:18 PM, Charles Determan Jr <deter088@umn.edu>wrote:
>>
>>> Greetings,
>>>
>>> To calculate power for an ANOVA test I know I can use the
>>> pwr.anova.test()
>>> from the pwr package.  Is there a similar function for the nonparamentric
>>> equivalent, Kruskal-Wallis?  I have been searching but haven't come up
>>> with
>>> anything.
>>>
>>> Thanks,
>>>
>>> --
>>> Charles Determan
>>> Integrated Biosciences PhD Candidate
>>> University of Minnesota
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>>
>>
>> --
>> Gregory (Greg) L. Snow Ph.D.
>> 538280@gmail.com
>>
>
>
>
> --
> Charles Determan
> Integrated Biosciences PhD Candidate
> University of Minnesota
>



-- 
Gregory (Greg) L. Snow Ph.D.
538280@gmail.com

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