Peter is absolutely correct: The "correlation" I used was for a
hidden normal process, not for the resultant correlated uniforms. This
is similar to but different from "tetrachoric corrrelations", about
which there is a substantial literature (including an R package
"polycor").
Why do you want correlated uniforms? What do they represent
physically? Does it matter if you can match exactly a particular
correlation coefficient, or is it enough to say that they are uniformily
distributed random variables such that their normal scores have a
specified correlation coefficient? There is so much known about the
multivariate normal distribution and so little about correlated uniforms
that it might be more useful to know the correlations of latent normals,
for which your uniforms are what are measured.
spencer graves
Peter Dalgaard wrote:
> "Jim Brennan" <[EMAIL PROTECTED]> writes:
>
>
>>Yes you are right I guess this works only for normal data. Free advice
>>sometimes comes with too little consideration :-)
>
>
> Worth every cent...
>
>
>>Sorry about that and thanks to Spencer for the correct way.
>
>
> Hmm, but is it? Or rather, what is the relation between the
> correlation of the normals and that of the transformed variables?
> Looks nontrivial to me.
>
> Incidentally, here's a way that satisfies the criteria, but in a
> rather weird way:
>
> N <- 10000
> rho <- .6
> x <- runif(N, -.5,.5)
> y <- x * sample(c(1,-1), N, replace=T, prob=c((1+rho)/2,(1-rho)/2))
>
--
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA
[EMAIL PROTECTED]
www.pdf.com <http://www.pdf.com>
Tel: 408-938-4420
Fax: 408-280-7915
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
