ade4 has the 'testdim' function which implements a recent method for
estimating the number of dimension for PCA on correlation matrix. Paper
describing the approach is available at
http://pbil.univ-lyon1.fr/members/dray/articles/SD805.php
William Revelle wrote:
At 12:08 PM +0000 4/20/09, Jari Oksanen wrote:
justin bem <justin_bem <at> yahoo.fr> writes:
See ade4 or mva package.
Justin BEM
BP 1917 Yaoundé
I guess the problem was not to find PCA (which is easy to find), but
finding an automatic method of selecting ("determining" sounds like
that selection would be correct in some objective sense) numbers of
components to be retained. I thin neither ade4 nor mva give much support
here (in particular the latter which does not exist any more).
The usual place to look at is multivariate task view:
http://cran.r-project.org/web/views/Multivariate.html
Under the heading "Projection methods" and there under
"Principal components" the taskview mentions packages
nFactors and paran that help in selecting the number
of components to retain.
Are these Task Views really so invisible in R that people don't find
them? Usually they are the first place to look at when you need
something you don't have. In statistics, I mean. If they are invisible,
could they be made more visible?
Cheers, Jari Oksanen
________________________________
De : nikolay12 <nikolay12 <at> gmail.com>
À : r-help <at> r-project.org
Envoyé le : Lundi, 20 Avril 2009, 4h37mn 41s
Objet : [R] PCA and automatic determination of the number of
components
Hi all,
I have relatively small dataset on which I would like to perform a
PCA. I am
interested about a package that would also combine a method for
determining
the number of components (I know there are plenty of approaches to
this
problem). Any suggestions about a package/function?
thanks,
Nick
___
Henry Kaiser once commented that the "Solving the number of factors
problem is easy, I do it everyday before breakfast. But knowing the
right solution is harder"
The psych package includes a number of ways to determine the number of
components. Parallel analysis (comparing your solution to random
ones), Minimum Absolute Partial correlations, Very Simple Structure
are three of the better ways. Try functions fa.parallel and VSS.
Bill
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