[R] estimating rho of Poisson distributed data

[John Sorkin]

If you use 
fit1<-glm(y~x,offset=offset,family=poisson(link=log))
you will get values for the Null deviance and residual deviance along with degrees of freedom for these parameters. One of these deviances divided by its degrees of freedom might be what you are looking for. but I am not sure. Perhaps other list members will either confirm my suspicion, or correct my misconception.
John







John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)>>> Peter Ehlers <ehlers at ucalgary.ca> 1/15/2010 10:33 AM >>>


Titus von der Malsburg wrote:
> On Fri, Jan 15, 2010 at 09:19:23AM -0500, David Winsemius wrote:
>> On Jan 15, 2010, at 5:59 AM, Titus von der Malsburg wrote:
>>
>>> Mean and variance of Poisson distributed data are specified by \rho.
>>> How can I estimate \rho for a set of measurements in R?
>> rho <- mean(x)
> 
> Yeah, thanks :-)  I was looking for a general way to fit a
> distribution.  Should've made that clear.  I'm surprised that nobody
> is complaining because I called lambda rho!
> 
Why would anyone complain? You're free to call it 'applesauce'
if that suits you.

What do you mean by 'general way to fit a distribution'?
Maximum likelihood might be one way.

  -Peter Ehlers

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-- 
Peter Ehlers
University of Calgary
403.202.3921

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