[.................]
MM> Yes; the reason for robustbase::lmrob's failure is basically the
MM> same as for MASS::rlm(., method="MM")'s one:
MM> The MM-method of both require a high-breakdown initial estimate,
MM> *and* the high-breakdown initial estimates are based algorithms
MM> that use resampling methods which are not at all adapted to this
MM> (and based on a theory of affine-invariance which does *not*
MM> apply in the case of factor-predictors...)
MM> Within the small community of experts this is well-known.
MM> For this reason, Maronna & Yohai (1999) proposed the so-called
MM> "S/M - estimate" which is supposedly implemented in Insightful's
MM> "Robust library" and consequently in the 'robust' R package.
MM> The 'robust.pdf' manual (of Insightful's original) says this on
MM> page 35:
> The Robust Library deals with this problem by using a new
> alternate S/M-estimate computing algorithm due to Maronna and
> Yohai (1999) for robustly fitting linear models which contain factor
> variables with possibly many levels. S-PLUS automatically uses the
> new S/M-estimate algorithm whenever your linear model data
> contains at least one factor variable. For further information see the
> Theoretical Details section Alternating S and M Initial Estimate.
MM> *AND* in last year's "robust stats in R" meeting in Banff,
MM> we had decided that this algorithm should really become an
MM> option in 'robustbase' ((but it seems we hadn't made good
MM> enough work assignments there ... ;-))
MM> Anyway, I've tried
MM> library(robust)
MM> mLMR. <- lmRob(fo, data = long)
MM> and unfortunately, that failed too, even though I'd have
MM> expected it would work thanks to using the S/M algorithm
MM> but *ALAS* it gets into problems as well..
Ok, I've debugged that.
It's from a somewhat embarrassing bug in package 'robust':
the internally used function splus.assign() has a bug
which only triggers when there is no intercept --- as in your case.
---> Ajay, you *can* fit by putting an intercept,
using the 'robust' package and lmRob()
or by patching the bug and re-installing 'robust' :
In function splus.assign, instead of the wrong
if(min(asgn) == 0)
term.labels <- c("(Intercept)", term.labels)
asgn <- asgn + 1
you must use
if(min(asgn) == 0) {
term.labels <- c("(Intercept)", term.labels)
asgn <- asgn + 1
}
---
Martin Maechler, ETH Zurich
MM> I'm interested in more feedback, particularly from other experts
MM> about S/M and/or its implementation in 'robust'.
MM> If you could guide me on what I'm doing wrong, that'll be great. How
MM> would I do the above specification as a robust regression? I googled
MM> around and I found a few others asking these same questions in the
MM> past, but it didn't look like there was a clear answer.
MM> In short: The "MM" method hasn't been well implemented for
MM> this situation,
MM> and you need to stick with the default
MM> rlm( ) for now.
MM> Martin Maechler, ETH Zurich
MM>
MM> --
MM> Ajay Shah
http://www.mayin.org/ajayshah
MM> [EMAIL PROTECTED]
http://ajayshahblog.blogspot.com
MM> _______________________________________________
MM> R-SIG-Robust@r-project.org mailing list
MM> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
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