For power studies you need to think about what the data will look like under
the alternative hypothesis. Is the data shifted over a certain amount? (the
most common assumption), or scaled? Or both? Or a completely different shape?
Etc.
My preferred method for power studies in this case is to use simulation:
1. decide what you data is likely to look like (based on previous data,
assumptions, ...)
2. decide how you will analyze the data (possibly iterate between 1 and 2)
3. write a function that simulates data under the alternative hypothesis, then
analyzes it (using decisions from 1 and 2) and returns the p-value or test
statistic. The function will often have a parameter for sample size and a
parameter for the size of the difference (scale, etc.).
4. use the replicate function to run your function a bunch of times.
5. the proportion of times that the above gives significant results is an
estimate of the power.
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Alon Ben-Ari
Sent: Tuesday, September 22, 2009 9:35 AM
To: r-help@r-project.org
Subject: [R] No parametric methods
Hello I am interested in finding out a method of power analysis
(effect
size and sample size calculation ) using R in non parametric methods?
I am running R 2.8.1 running on linux open SUSE
Any libraries or documentation , I was not bale to google up any.
Thanks in Advance,
Ben-Ari Alon, MD
University of Pittsburgh.
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