On Thu, 5 Apr 2007, Ravi Varadhan wrote:
In your code, the variables x (which I assume is the observed data),
Tvec,
and flag are not passed to the function as arguments. This could be a
potential problem.
I think scoping will probably find them.
Another problem could be that you have to use "negative"
log-likelihood function as input to optim, since by default it
"minimizes"
the function, whereas you are interested in finding the argmax of
log-likelihood. So, in your function you should return (-ll) instead of
ll.
OR set fnscale. This is the most serious problem.
If the above strategies don't work, I would try different initial values
(it
would be best if you have a data-driven strategy for picking a starting
value) and different optimization methods (e.g. conjugate gradient with
"Polak-Ribiere" steplength option, Nelder-Mead, etc.).
It looks to me as if the calculations are very vulnerable to
overflow/underflow, as they use gamma and not lgamma. They could be
rearranged to be much stabler by computing the sum of logs for each
sub-expression.
There were over 50 warnings, which we were not shown. They probably
explained the problem.
Beyond that, the feasible region seems to be the interior of the
positive orthant, in which case transforming the parameters (e.g.
working with their logs) would be a good idea.
Finally, always supply analytical gradients when you can (as would be
easy here).
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
[EMAIL PROTECTED]
Sent: Thursday, April 05, 2007 6:12 AM
To: r-help@stat.math.ethz.ch
Subject: [R] Likelihood returning inf values to optim(L-BFGS-B) other
options?
Dear R-help list,
I am working on an optimization with R by evaluating a likelihood
function that contains lots of Gamma calculations (BGNBD: Hardie Fader
Lee 2005 Management Science). Since I am forced to implement lower
bounds for the four parameters included in the model, I chose the
optim() function mith L-BFGS-B as method. But the likelihood often
returns inf-values which L-BFGS-B can't deal with.
Are there any other options to implement an optimization algorithm
with R accounting for lower bounds and a four parameter-space?
Here is the error message I receive (german):
--
out=optim(c(.1,.1,.1,.1),fn,method="L-BFGS-B",lower=c(.0001,.0001,.0001,.000
1,.0001))
Fehler in optim(c(0.1, 0.1, 0.1, 0.1), fn, method = "L-BFGS-B", lower
= c(1e-04, :
L-BFGS-B benötigt endliche Werte von 'fn'
Zusätzlich: Es gab 50 oder mehr Warnungen (Anzeige der ersten 50 mit
warnings())
--
And this is the likelihood function:
--
fn<-function(p) {
A1=(gamma(p[1]+x)*p[2]^p[1])/(gamma(p[1]))
A2=(gamma(p[3]+p[4])*gamma(p[4]+x))/(gamma(p[4])*gamma(p[3]+p[4]+x))
A3=(1/(p[2]+Tvec))^(p[1]+x)
A4=(p[3]/(p[4]+x-1))*((1/(p[2]+t_x))^(p[1]+x))
ll=sum(log(A1*A2*(A3+flag*A4)))
return(ll)
}
Thank you very much for your help in advance!
Best regards,
Michael
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and provide commented, minimal, self-contained, reproducible code.
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
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