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
> > ______________________________________________
> [email protected] 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.
>
______________________________________________
[email protected] 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:[email protected] UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
______________________________________________
[email protected] 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.
