Gabor Grothendieck <ggrothendieck <at> gmail.com> writes:

>

> That's basically what I already do but what I was wondering

> was if there were any other approaches such as connections

> with clustering, PCA, that have already been developed in

> R that might be applicable.

Have you considered finding the combination with maximum generalized variance of

three scaled variables (i.e. the maximum determinant of the correlation matrix

of three variables)?

You can vectorize this calculation as follows:

z <- combinations(ncol(DF), 3)

cormat <- cor(DF)

det3 <- function(ra,rb,rc) 1 - ra*ra - rb*rb - rc*rc + 2*ra*rb*rc

res <- det3( cormat[z[,1:2]],cormat[z[,c(1,3)]],cormat[z[,c(2,3)]] )

z[which.max(res),]

>

> On 3/1/06, Jacques VESLOT <jacques.veslot <at> cirad.fr> wrote:

> > library(gtools)

> > z <- combinations(ncol(DF), 3)

> > maxcor <- function(x) max(as.vector(as.dist(cor(DF[,x]))))

> > names(DF)[z[which.min(apply(z, 1, maxcor)),]]

> >

> >

> > Gabor Grothendieck a écrit :

> >

> > >Are there any R packages that relate to the

> > >following data reduction problem fo finding

> > >maximally independent variables?

> > >

[rest deleted]

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