R-beta: SEs for one-param MLE in R?
Martin Maechler
Martin Maechler <maechler@stat.math.ethz.ch>
Tue, 21 Apr 1998 11:51:28 +0200
[--- diverting from R-help to R-devel ... MM ---]
>>>>> "Jim" == Jim Lindsey <jlindsey@luc.ac.be> writes:
Jim> Just back from a week camping in the snow in the English Lakes,
Jim> and trying to catch up...
I hope the adventurous vacation was also relaxing in some ways..
MM> ....
MM> BTW: I am (we are) interested in the functions that you are writing
MM> for nlm(.) It certainly is worthwhile to have nlm(.) return a class
MM> "nlm" result and provide print.nlm(.) and summary.nlm(.) functions
MM> {{ Jim Lindsey already posted something like this, unfortunately
MM> using "nls" which we don't want as long as it is not very close to
MM> S' nls(.) function }}
Jim> I am afraid that I don't understand the logic of this requirement
Jim> of closeness to R for such functions. nlm() itself is not close
Jim> and even hist() has never been very similar.
Yes, there are some cases where we (the R-core team, or the "R-devel" group)
have decided that S-plus is so wrong that we don't want to emulate or stay
close to it. hist(.) is one such example.
With nls(.), this is quite different, I think.
nls(.) has several nice features, (your "nls" did too!).
Most notably, the calling syntax of nls(.) using model notation,
is something I would want before I'd call a function "nls".
At the moment, I think it would be better to add an "nlm" class and
corresponding print and summary methods to nlm(.), rather than calling
such a thing "nls".
Also, a few weeks ago, Ross said that he planned to add code to nlm
which would make use of gradient and hessian function when provided
(or also, using D(.) ?).
Jim> On the other hand,
Jim> remember that the nls() I sent was a very cut-down version of one
Jim> in one of my libraries. It had the above solution for the
Jim> inversion with one dimensional parameters. By the way, the
Jim> original Fortran for nlm that I ported to R printed out a warning
Jim> that the algorithm is very inefficient for one-dimensional
Jim> problems.
Yes, your "nls" function was useful!
and I agree that "nlm" should be pushed in the direction of what you had.
Jim> With respect to Bill's negative binomial that started another
Jim> discussion, one of the functions in my nonlinear regression
Jim> library does negative binomial nonlinear regression for both the
Jim> mean and the dispersion parameters, along with twenty odd other
Jim> distributions. I used it for beta-binomial regression in my paper
Jim> with Pat Altham in the latest Applied Statistics. (Also another
Jim> similar function for a finite mixture with these distributions,
Jim> for example for negative binomial with inflated zeroes.) In my
Jim> repeated measures library, there is a similar function for
Jim> regressions with the same collection of distributions, but having
Jim> a random intercept. Once R stabilizes...
well, isn't R stabilizing more and more?
Since 0.61, at least the way extension packages (formerly known as "libraries")
should be written is pretty stable.
Jim, I think several people really would be interested in your
``nonlinear regression library'' and the ``repeated measures library''.
If they don't work in R 0.61, they could be at least put into
CRAN/src/contrib/devel/.
Best regards!
Martin
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