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(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. > > > >______________________________________________ > >R-help@stat.math.ethz.ch mailing list > >https://stat.ethz.ch/mailman/listinfo/r-help > >PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > > > > > > > > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html