Thanks to all who responded to my query. Rick Ditmars provided me with the
most straightforward answer:
The function simulate.mv.matrix() in Environmental Stats for SPlus will
generate correlated data using a matrix of rank correlation coefficients.
Theoretical and/or empirical distributions can be used as well.
For those who don't have the Environmental Stats module and have the desire
to write their own function, the algorithms are from Iman, R.L., and W.J.
Conover. (1982). A Distribution-Free Approach to Inducing Rank Correlation
Among Input Variables. Communications in Statistics--Volume B, Simulation
and Computation, 11(3), 311-334.
One curiosity of the Splus programming environment emerged during my quest
that I had not paid attention to in the past. Modules attach in position 2
of the search list, by default. Libraries attach at the bottom of the
search list, by default. This didn't matter to me except that
"Environmental Stats for SPlus" has several changed versions of Splus
functions that have identical names. As an example, the rgamma function in
Splus is different from the rgamma function in Environmental Stats for
SPlus. Depending on whether you use module() or library() the outcome is
different. Also, using "?" or help() will give the help file associated
with the first version of the function encountered in the search
list. Using the help index will give the help file for the Splus function,
regardless of the search list.
I would be happier with unique function names, such as rgamma and
rgamma.alt with help files that cross reference each other.
One final note, my slap shot is about like my ability to pose
questions. So, I'm the "other" Mark Tardiff.
Mark Tardiff
Neptune and Company, Inc.
1505 15 th Street Suite B
Los Alamos, NM 87544
Phone: 505-662-0730, ext 12
Fax: 505-662-0500
mtardiff@neptuneandco.com
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