Gabor Grothendieck 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 combinati
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 <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> wrote:
> library(gtools)
> z <- combinations(nc
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 va
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,
