Are you interested in turning that into a monitor, processing each day's data sequentially or even each entry as it arrived? If yes, you may wish to evaluate the "Foundations of Monitoring" documents downloadable from "www.prodsyse.com". If you have any questions about that, I might be able to help.

hope this helps. spencer graves

Lourens Olivier Walters wrote:

Thanks for the help, the wrapper function was very useful. I managed to
solve the problem using Spencer Graves' suggestion. I am analyzing the
interarrival times between HTTP packets on a campus network. The dataset
actually has more than 14 Million entries! It represents the traffic
generated by approximately 3000 users browsing the web for 30 days. I
have to be careful to always remove unused objects from my workspace,
but otherwise I have so far managed to cope with 512Mb of memory on a
Pentium 600Mhz.


Lourens

On Tue, 2003-09-30 at 23:25, Ben Bolker wrote:


PS. 11 MILLION entries??

On Tue, 30 Sep 2003, Ben Bolker wrote:



Spencer Graves's suggestion of using shape and scale parameters on a log scale is a good one.

To do specifically what you want (check values for which the objective function is called and see what happens) you can do the following (untested!), which makes a local copy of dgamma that you can mess with:

dgamma.old <- dgamma
dgamma <- function(x,shape,rate,...) {
d <- dgamma.old(x,shape,rate,...)
cat(shape,rate,d,"\n")
return(d)
}



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