[R] Power analysis for MANOVA?

[Adam D. I. Kramer]

On Mon, 26 Jan 2009, Charles C. Berry wrote:

>
> If you know what a 'general linear hypothesis test' is see
>
> 	http://cran.r-project.org/src/contrib/Archive/hpower/hpower_0.1-0.tar.gz
>

I do, and am quite interested, however this package will not install on R
2.8.1: First, it said that there was no "maintainer" in the description, so
I added one (figuring that the 1991 date of the package was to blame),
however it still will not compile:

parmesan:tmp$ sudo R CMD INSTALL hpower/
* Installing to library '/usr/local/lib/R/library'
* Installing *source* package 'hpower' ...
** R
** preparing package for lazy loading
Error in parse(n = -1, file = file) : unexpected '{' at
5: ##
6: pfnc_function(q,df1,df2,lm,iprec=c(6)) {
Calls: <Anonymous> -> code2LazyLoadDB -> sys.source -> parse
Execution halted
ERROR: lazy loading failed for package 'hpower'
** Removing '/usr/local/lib/R/library/hpower'
parmesan:tmp$

...any tips?

--Adam

> HTH,
>
> Chuck
>
> On Mon, 26 Jan 2009, Adam D. I. Kramer wrote:
>
>> 
>> On Mon, 26 Jan 2009, Stephan Kolassa wrote:
>>
>>>  My (and, judging from previous traffic on R-help about power analyses,
>>>  also some other people's) preferred approach is to simply simulate an
>>>  effect size you would like to detect a couple of thousand times, run your
>>>  proposed analysis and look how often you get significance.  In your 
>>> simple
>>>  case, this should be quite easy.
>> 
>> I actually don't have much experience running monte-carlo designs like
>> this...so while I'd certainly prefer a bootstrapping method like this one,
>> simulating the effect size given my constraints isn't something I've done
>> before.
>> 
>> The MANOVA procedure takes 5 dependent variables, and determines what
>> combination of the variables best discriminates the two levels of my
>> independent variable...then the discrimination rate is represented in the
>> statistic (Pillai's V=.00019), which is then tested (F[5,18653] = 0.71). 
>> So
>> coming up with a set of constraints that would produce V=.00019 given my
>> data set doesn't quite sound trivial...so I'll go for the "par" library
>> reference mentioned earlier before I try this.  That said, if anyone can
>> refer me to a tool that will help me out (or an instruction manual for 
>> RNG),
>> I'd also be much obliged.
>> 
>> Many thanks,
>> Adam
>> 
>> 
>>>
>>>  HTH,
>>>  Stephan
>>> 
>>>
>>>  Adam D. I. Kramer schrieb:
>>> >  Hello,
>>> > >      I have searched and failed for a program or script or method to
>>> >  conduct a power analysis for a MANOVA. My interest is a fairly simple > 
>>> case
>>> >  of 5 dependent variables and a single two-level categorical predictor
>>> >  (though the categories aren't balanced).
>>> > >      If anybody happens to know of a script that will do this in R, 
>>> I'd
>>> >  love to know of it! Otherwise, I'll see about writing one myself.
>>> > >      What I currently see is this, from help.search("power"):
>>> > >  stats::power.anova.test
>>> >                          Power calculations for balanced one-way
>>> >                          analysis of variance tests
>>> >  stats::power.prop.test
>>> >                          Power calculations two sample test for
>>> >                          proportions
>>> >  stats::power.t.test     Power calculations for one and two sample t
>>> >                          tests
>>> > >      Any references on power in MANOVA would also be helpful, though 
>>> of
>>> >  course I will do my own lit search for them myself.
>>> > >  Cordially,
>>> >  Adam D. I. Kramer
>>> > >  ______________________________________________
>>> >  R-help at 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.
>>> > 
>>> 
>> 
>> ______________________________________________
>> R-help at 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.
>> 
>> 
>
> Charles C. Berry                            (858) 534-2098
>                                            Dept of Family/Preventive 
> Medicine
> E mailto:cberry at tajo.ucsd.edu	            UC San Diego
> http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901
>
>
>