lgj200306 lgj200306@... writes:
Thaks David very much, but how can I
improve my model? Should I change my likelihood function
or do some thing else?
Best wishes for all list members!
Your choices are:
(1) if the final result looks sensible, and none of the final
predicted values lead to NA/NaN results, you can *probably* ignore these
warnings
(2) depending on your model, you may be able to bound
some of the parameters (e.g. using method=L-BFGS-B
and specifying lower/upper values) *or* fit them on a different scale
(e.g. the log scale) to prevent zero/negative predicted
values of lambda (predicted values of zero will be OK
as long as they always go with observed values of zero --
otherwise you'll get infinite (negative) log-likelihood
values. There are some simple examples of bounded optimization
in the ?mle2 examples ...
At 2011-10-21 04:05:10,David Valentim Dias
dvdscripter at gmail.com wrote:
Should be a bad parameter like you get from dpois(1, -1)
2011/10/20 lgj200306lgj200306 at 163.com
Hi, all
I have a problem about the log maximum likelihood.
I want to estimate several parameters using
log maximum likelihood method (mle2() in package bbmle ),
and the likelihood fucton was based on poisson distribution.
When finished, there were some warnings said that:
In dpois(x, lambda, log) : NaNs produced
Will this situation influence my result of
parameters estimating or not? If my parameters estimated have
been influenced, how can I improve my R code
or data to achieve a exact estimating?
Thanks for you attention and best wishes for all of you!
Guojun Lin
South China Botanical Garden, Chinese Academy of Science, China
Department of Renewable Resources, University of Alberta, Canada
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