On Wed, 13 Oct 2010, David Winsemius wrote:
On Oct 13, 2010, at 4:50 PM, Antonio Paredes wrote:
Hello everyone,
I wanted to ask if there is an R-package to fit the following Poisson
regression model
log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
i=1,\cdots,N (subjects)
j=0,1 (two levels)
k=0,1 (two levels)
treating the \phi_{i} as nuinsance parameters.
If I am reading this piece correctly there should be no difference between a
conditional treatment of phi_i in that model and results from the
unconditional model one would get from fitting with
glm(lambda ~ phi + alpha + beta ,family="poisson").
Right.
But if N is large, the model.matrix will be huge and there may be problems
with memory and elapsed time.
loglin() and loglm() will fit the same model without need for a
model.matrix (modulo having enough data to actually fit that model), and
large values of N are no big deal.
HTH,
Chuck
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.9679&rep=rep1&type=pdf
(But I am always looking for corrections to my errors.)
--
David Winsemius, MD
West Hartford, CT
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
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