-----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
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
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
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"
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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