Maarten and Nick,
Thanks for your comments. Just now, I came across the "constraints"
command which can be used to specify linear constraints on the
parameters. Since my constraints are linear, I can probably use this
too.
Deepankar
On Fri, 2006-05-26 at 10:28 +0100, Nick Cox wrote:
> Maarten's answer is deeper than mine. However,
> if your model is at all appropriate this trick
> might work.
>
> Nick
> [email protected]
>
> Nick Cox
>
> > I don't know about "best", particularly in general.
> >
> > But one way to do it is to parameterise in terms
> > of e.g. a, b, 1 - (a + b). If you are lucky this
> > will be enough.
> >
> > You could still have problems if the algorithm
> > is happiest at e.g. a = 1.1, b = -0.9, c = 0.8.
>
> Deepankar Basu
>
> > > I am trying to do a maximum likelihood estimation where some of the
> > > parameters of the model are probabilities; hence they
> > should add up to
> > > unity.
> > >
> > > What is the best way to put restrictions on the relevant
> > > parameters and
> > > constrain them to add up to unity?
>
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