Hi,
Does someone know how to include weights in the S-Plus rdl1.s algorithm
(the robust regression algorithm developed by Hubert & Rousseeuw)? Of
course, the algorithm already include a weighting scheme (based on
distances of x points w.r.t. a robust center of an ellipsoid) but I
want, before entering the procedure, to put more weights on some
x-points and less on some others. Does it make sense? If so, how can we
do that?
I considered using the lmRobMM function (the algorithm developped by
Yohai et al, also available in S-Plus) because it includes a "weights"
argument but my problem includes regressors that are continuous and
others that are binary and I don't know if the algorithm can handle such
categorical variables. Even if it's the case, the default number of
random subsamples drawn (and needed by the algorithm) is 4.6*2^ncol(x);
I have 10 continuous variables + 1 categorical with 20 levels (which
recoded gives 20 dummy vars), so the total is 30. Of course, I could
change this default number and set a more "reasonable" one but the
choice would be inevitably so small with regard to the default that I
seriously doubt about the validity of the result anyway.
Can someone help?
The exact references for the above cited papers are:
* Robust regression with both continuous and binary regressors, Mia
Hubert and Peter J. Rousseeuw.
http://win-www.uia.ac.be/u/statis/publicat/#j1990
* Yohai, V., Stahel, W. A., and Zamar, R. H. (1991). A procedure for
robust estimation and inference in linear regression, in Stahel, W.
A. and Weisberg, S. W., Eds., Directions in robust statistics and
diagnostics, Part II. Springer-Verlag.
Patrick
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