TWO COMMENTS:
1. DID YOU ASSIGN THE OUTPUT OF "optim" to an object, like "est <-
optim(...)"? If yes and if "optim" terminated normally, the 60,000+
paramters should be there as est$par. See the documentation on "optim".
2. WHAT PROBLEM ARE YOU TRYING TO SOLVE?
I hope you will forgive me for being blunt (or perhaps bigoted), but
I'm skeptical about anyone wanting to use optim to estimate 60,000+
parameters. With a situation like that, I think you would be wise to
recast the problem as one in which those 60,000+ parameters are sampled
from some hyperdistribution characterized by a small number of
hyperparameters. Then write a model where your observations are sampled
from distribution(s) controlled by these random parameters. Then
multiply the likelihood of the observations by the likelihood of the
hyperdistribution and integrate out the 60,000+ parameters, leaving only
a small number hyperparameters.
When everything is linear and all the random variables / random
effects and observation errors follow normal distributions, this is the
classic linear, mixed-effects situation that is routinely handled well
for most such situations by the nlme package, documented with in
companion book Pinhiero and Bates (2000) Mixed-Effects Models in S and
S-PLUS (Springer). If the models are nonlinear but with curvature that
is reasonably well behaved and the random variables / random effects and
observation errors are still normal, the nlme package and Pinhiero and
Bates still provide a great approach to most such situations, as far as
I know. When the observations are non-normally distributed, then the
best software I know is the lme4 package. I have not used it recently,
but it was written and being maintained by some of the leading experts
in this area as far as I know.
CONCLUSION:
If you are short on time and "1" will work for you, do that.
Obviously, you will need to do some further analysis to understand the
60,000+ parameters you estimated -- which implies by itself that you
really should be using approach "2". However, if I'm short on time and
need an answer, then I'd ignore "2" and hope to get something by
plotting and doing other things with the 60,000+ parameters that should
be in "est$par" if "optim" actually ended normally.
However, if the problem is sufficiently important to justify more
work, then I'd want to cast it as some kind if mixed-effects model, per
"2" -- perhaps using an analysis of "1" as a first step towards "2".
Hope this helps.
Spencer
On 2021-03-12 20:53, J C Nash wrote:
optim() has no method really suitable for very large numbers of parameters.
- CG as set up has never worked very well in any of its implementations
(I wrote it, so am allowed to say so!). Rcgmin in optimx package works
better, as does Rtnmin. Neither are really intended for 60K parameters
however.
- optim::L-BFGS-B is reasonable, but my experience is that it still is not
intended for more than a couple of hundred parameters.
JN
On 2021-03-12 9:31 p.m., Jeff Newmiller wrote:
Calculate fewer of them?
If you don't setup your code to save intermediate results, then you cannot see
intermediate results.
On March 11, 2021 8:32:17 PM PST, "毕芳妮 via R-help" <r-help@r-project.org> wrote:
Dear list,
I am using optim() to estimate over 60 thousans of parameters, and use
the server to run the program.But it took me 5 hours and there was just
no result coming out.How could I do to show some results that have been
calculated by optim()?
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