>>>>> Michael <comtech....@gmail.com>
>>>>> on Mon, 12 Mar 2012 13:19:19 -0500 writes:
> The problem is: by default shouldn't it use "Huber's"?
> And it should be convex problem no?
> so when I do rlm(y~x) which is a single-beta fitting problem,
> shouldn't it always converge?
βIn theory, theory and practice are the same. In practice, they are not.β
β Albert Einstein
[according to http://www.goodreads.com/quotes/show/66864 ]
Theory says that convergence happens in an infinite number of
iterations, but then theory also says convergence means that the
coefficients don't change any more ;-)
[... etc.]
> --------------------
> Psi functions are supplied for the Huber, Hampel and Tukey bisquare
> proposals as psi.huber, psi.hampel and psi.bisquare. Huber's corresponds
to
> a convex optimization problem and gives a unique solution (up to
> collinearity). The other two will have multiple local minima, and a good
> starting point is desirable.
which also mentions "up to collinearity" (theory).
"Practice" would add "near-collinearity" and many other
practical border line issues that can happen.
As maintainer of the robustbase package,
I'm slightly intrigued by your example.
==>
1) Can you provide it reproducible dput(data) "cut & paste"
into your e-mail if small; available as mydata.rda after
save(., file="mydata.rda") for download
2) What's the result of using lmrob() {package 'robustbase'}
instead of rlm() {package 'MASS'} ?
Best regards,
Martin Maechler, ETH Zurich
> On Fri, Mar 9, 2012 at 1:21 PM, Berend Hasselman <b...@xs4all.nl> wrote:
>>
>> On 09-03-2012, at 20:00, Michael wrote:
>>
>> > Hi all,
>> >
>> > In using "rlm" I've got a bunch of warnings... "failed to converge in
20
>> > steps", etc.
>> >
>> > My question is:
>> >
>> > what are the results then after the failure?
>> >
>>
>> They haven't converged. So inaccurate. Maybe your model is badly
>> formulated or ill conditioned.
>>
>> > Will "rlm" automatically downgrade back to "lm" upon failure?
>> >
>> Help says nothing about that so most likely no.
>>
>> Why don't you try and raise maxit? Use maxit=40 in the call of rlm. And
>> see what happens.
>>
>> Berend
>>
>>
>>
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