[R] robust mlm in R?

[Michael Friendly]

I'm looking for something in R to fit a multivariate linear model 
robustly, using
an M-estimator or any of the myriad of other robust methods for linear 
models
implemented in robustbase or methods based on MCD or MVE covariance
estimation (package rrcov).

E.g., one can fit an mlm for the iris data as:
iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, 
Petal.Width) ~ Species, data=iris)

What I'd like is something like rlm() in MASS, but handling an mlm, e.g.,
iris.mod <- rmlm(cbind(Sepal.Length, Sepal.Width, Petal.Length, 
Petal.Width) ~ Species, data=iris)
and returning a vector of observation weights in its result.

There's a burgeoning literature on this topic, but I haven't yet found 
computational methods.
Any pointers or suggestions would be appreciated.

-Michael


-- 
Michael Friendly     Email: friendly AT yorku DOT ca 
Professor, Psychology Dept.
York University      Voice: 416 736-5115 x66249 Fax: 416 736-5814
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