[R] [RsR] How does "rlm" in R decide its "w" weights for each IRLSiteration?

[S Ellison]

rlm includes an MM estimator.

S Ellison 

> -----Original Message-----
> From: Maheswaran Rohan [mailto:mrohan at doc.govt.nz] 
> Sent: 22 July 2012 23:08
> To: Valentin Todorov; S Ellison
> Cc: r-sig-robust at r-project.org; r-help
> Subject: RE: [RsR] How does "rlm" in R decide its "w" weights 
> for each IRLSiteration?
> 
> Hi Valentin,
> 
> If the contamination is mainly in the response direction, 
> M-estimator provides good estimates for parameters and rlm 
> can be used.
> 
> Rohan
> 
> -----Original Message-----
> From: r-sig-robust-bounces at r-project.org
> [mailto:r-sig-robust-bounces at r-project.org] On Behalf Of 
> Valentin Todorov
> Sent: Saturday, 21 July 2012 6:57 a.m.
> To: S Ellison
> Cc: r-sig-robust at r-project.org; r-help
> Subject: Re: [RsR] How does "rlm" in R decide its "w" weights 
> for each IRLSiteration?
> 
> Hi Michael, S Ellison,
> 
> I do not actually understand what you want to achieve with 
> the M estimates of rlm in MASS, but why you do not give a try 
> of lmrob in 'robustbase'. Please have a llok in the 
> references (?lmrob) about the advantages of MM estimators 
> over the M estimators.
> 
> Best regards,
> Valentin
> 
> 
> 
> 
> On Fri, Jul 20, 2012 at 5:11 PM, S Ellison <S.Ellison at lgcgroup.com>
> wrote:
> >
> >
> >> -----Original Message-----
> >> Subject: [RsR] How does "rlm" in R decide its "w" weights for each 
> >> IRLS iteration?
> >> I am also confused about the manual:
> >>
> >>            a.  The input arguments:
> >>
> >> wt.method are the weights case weights (giving the relative 
> >> importance of case, so a weight of 2 means there are two of
> >> these) or the inverse of the variances, so a weight of two 
> means this 
> >> error is half as variable?
> >
> > When you give rlm weights (called 'weights', not 'w' on 
> input, though
> you can abbreviate to 'w'), you need to tell it which of 
> these two possibilities you used.
> > If you gave it case numbers, say wt.method="case"; if you gave it
> inverse variance weights, say wt.method="inv.var".
> > The default is "inv.var".
> >
> >
> >> The input argument "w" is used for the initial values of 
> the rlm IRLS 
> >> weighting and the output value "w" is the converged "w".
> > There is no input argument 'w' for rlm (see above).
> > The output w are a  calculated using the psi function, so between 0
> and 1.
> > The effective weights for the final estimate would then be something
> like w*weights, using the full name of the input argument 
> (and if I haven't forgotten a square root somewhere). At 
> least, that would work for a simple location estimate (eg rlm(x~1)).
> >
> >> If my understanding above is correct, how does "rlm" 
> decide its "w" 
> >> for each IRLS iteration then?
> > It uses the given psi functions to calculate the iterative weights
> based on the scaled residuals.
> >
> >> Any pointers/tutorials/notes to the calculation of these "w"'s in 
> >> each IRLS iteration?
> > Read the cited references for a detailed guide. Or, of 
> course, MASS -
> the package is, after all, intended to support the book, not 
> replace it.
> >
> >
> >
> > S Ellison
> >
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