# [R] Maximally independent variables

Gabor Grothendieck ggrothendieck at gmail.com
Wed Mar 1 18:07:27 CET 2006

```In case others are interested I did get a reply offlist
regarding the escouf function in the pastecs package.
See:

library(pastecs)
?escouf

Also see pages 47-52 of
system.file("doc/pastecs.pdf", package = "pastecs")
(in French).

On 3/1/06, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
> 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.
>
> 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?
> > >
> > >Currently what I am doing is solving the following
> > >minimax problem:  Suppose we want to find the
> > >three maximally independent variables.  From the
> > >full n by n correlation matrix, C, of all n variables
> > >chooose three variables and form their 3 by 3 correlation
> > >submatrix, C1, finding the offdiagonal entry of C1
> > >which is largest in absolute value.  Call that z.  Thus for
> > >each set of 3 variables we can associate such a z.
> > >Now for each possible set of three variables find the one for
> > >which its value of z is least.
> > >
> > >I only give the above formulation because that is
> > >what I am doing now but I would be happy to
> > >consider other different formulations.
> > >
> > >______________________________________________
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