Hi Adam,
first: I really don't know much about MANOVA, so I sadly can't help you
without learning about it an Pillai's V... which I would be glad to do,
but I really don't have the time right now. Sorry!
Second: you seem to be doing a kind of "post-hoc power analysis", "my
result isn't significant, perhaps that's due to low power? Let's look at
the power of my experiment!" My impression is that "post-hoc power
analysis" and its interpretation is, shall we say, not entirely accepted
within the statistical community, see:
Hoenig, J. M., & Heisey, D. M. (2001, February). The abuse of power: The
pervasive fallacy of power calculations for data analysis. The American
Statistician, 55 (1), 1-6
And this:
http://staff.pubhealth.ku.dk/~bxc/SDC-courses/power.pdf
However, I am sure that lots of people can discuss this more competently
than me...
Best wishes
Stephan
Adam D. I. Kramer schrieb:
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
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