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 ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================