Yes, that is what I meant. It is not a species but some products and I
have various parameters measured for each product. But basically I thought
that ecological data are quite similar.
So I would be glad to be able to try your code.
Thank you
Petr Pikal
petr.pikal at precheza.cz
Gavin Simpson <gavin.simpson at ucl.ac.uk> napsal dne 15.11.2007 18:32:08:
> On Thu, 2007-11-15 at 16:07 +0100, Petr PIKAL wrote:
> > Dear all
> >
> > I would like to show my audience that some variables are homogenous
inside > > groups but different outside. I can use by with summary for all
variables> >
> > by(iris[,1:4], iris$Species, summary)
> >
> > what can be quite messy in case of more than few variables and about 8
> > groups
> >
> > or densityplot for one variable
> >
> > densityplot(~Petal.Length | Species, iris)
> >
> > I have two questions:
> >
> > 1. Is there any other plot to show all variables at once?
Something > > like
> >
> > densityplot(~iris[,1:4] | Species, iris)
> >
> > 2. Is it possible to evaluate homogenity of many (20-30)
variables > > inside groups by some other function/table/graph?
>
> Hi Petr,
>
> I haven't replied-all by the way, in case I've misunderstood,
but...
>
> If you mean that you have a data set with say 10 samples split into 2
> groups, and for each sample you have measured many variables (say
> species in a quadrat or lots of morphological parameters on individual
> plants), then one way might be to look at the work of Marti Anderson
> [*]. She has developed a method that calculates the multivariate
> distance between each sample in a group and that group's multivariate
> centroid. You then take these distances to group centroid and do an
> ANOVA on them, the general point being that this is a multivariate
> analogue of something like a Levene's test and if groups variances are
> heterogeneous then one or more groups will have a higher/lower mean
> distance to centroid than the other groups.
>
> groups <- something.that.gives.groups.as.a.factor()
> dis <- something.that.gives.distances.centroids(my_data, groups)
> anova(lm(dis ~ groups))
>
> If this is the case, then I have some code that I'm currently working
on
> which does this, which works (!) and which I can send to you. Marti
> tests for homogeneity using a permutation test. I have that as well, but
> currently it doesn't give the same results as Marti's PERMDISP2
> programme (standalone Fortran, source not available), though I can't
see
> what I'm doing wrong, if anything - and my permutation p-value closely
> matches the ANOVA p-value for tests where the data don't violate ANOVA
> assumptions too grossly - Levene's test is quite robust in this regard.
>
> Let me know if this is what you meant and if the code will be useful and
> I'll send a reply to the list for the archives and send you my code.
>
> All the best,
>
> G
>
> [*] Anderson, M.J. (2006) Distance-based tests for homogeneity of
> multivariate dispersions. Biometrics 62, 245--253
>
> http://www.stat.auckland.ac.nz/~mja/Programs.htm
>
> >
> > Thank you
> >
> > Petr Pikal
> > petr.pikal at precheza.cz
> >
> > ______________________________________________
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> > 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.
> --
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> ECRC, UCL Geography, [f] +44 (0)20 7679 0565
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