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?

Thanks!

--------------------

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.


On Fri, Mar 9, 2012 at 1:21 PM, Berend Hasselman <bhh@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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