[R] Problem with MASS::fitdistr().

Mark Leeds m@rk|eed@2 @end|ng |rom gm@||@com
Mon Apr 27 03:18:27 CEST 2020

it's been a looooooooong time but I vaguely remember Rvmminb computing
gradients ( and possibly hessians )
subject to constraints. John can say more about this but, if one is going
to go through the anguish of
creating a fitdstr2, then you may want to have it call Rvmminb instead of
whatever is currently
being called.

On Sun, Apr 26, 2020 at 8:55 PM Abby Spurdle <spurdle.a using gmail.com> wrote:

> I thought about this some more and realized my last suggestion is
> unlikely to work.
> Another possibility would be to create a new function to compute the
> Hessian with a smaller step size, but I suspect there will be more
> problems.
> Possibly a much simpler approach would be to:
> Modify the source for fitdistr.
> (Copy the source and create a new function, say fitdistr2).
> Modify it not compute the Hessian in the optim call.
> Then after the optim call, test the parameter estimates.
> If they're very close to the boundaries (here zero), then they're
> flagged as near-boundary cases and the fitdistr2 function returns the
> parameter estimates without the Hessian and related info.
> (Possibly generating a warning).
> If they're sufficiently distant, the Hessian and related info can be
> computed in separate steps and returned.
> (Equivalent to what it does currently).
> I note that there's at least one R package (numDeriv), and maybe more,
> for computing the Hessian, numerically.
> On Mon, Apr 27, 2020 at 9:31 AM Abby Spurdle <spurdle.a using gmail.com> wrote:
> >
> > > Dear Ms. Spurdle
> >
> > I usually refer to myself as "He".
> > (But then, that's not the whole story...)
> >
> > I'm not an expert on maximum likelihood approaches.
> > So, I apologize if the following suggestion is a poor one.
> >
> > Does your likelihood function have a limit, as alpha approaches zero
> (say zero)?
> > If so, the limit of the log-likelihood would be -Inf, would it not.
> >
> > You could create a function representing the likelihood or
> > log-likelihood by wrapping your density function.
> > The function could allow alpha/beta values equal to or below zero, and
> > return the limit.
> > This is mathematically incorrect, but may be sufficient for
> > permissible estimates of the second-order partial derivatives.
> > Depending on the shape of the likelihood function these
> > pseudo-likelihoods maybe able to be improved...?
> >
> > You could then do a small modification on the source code for
> > MASS::fitdistr, such that the user specifies the likelihood function
> > or log-likelihood function, rather than the density...
> >
> > The fitdistr function is relatively complex, however, you would only
> > need to modify a couple of lines, the lines that create the fn
> > function...
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