On Fri, 26 Mar 2010, Robert Ruser wrote:
2010/3/26 Charles C. Berry <cbe...@tajo.ucsd.edu>:
On Fri, 26 Mar 2010, Robert Ruser wrote:
So this is the generalized linear model with a poisson family, log link, and
a Gaussian random effect in the linear predictor.
Take a look at lme4, MASS (glmmPQL), and try searching CRAN packages for
'glm' and 'GLM' (there are a bunch and several promise to handle random
effects, but YMMV).
Thank you. But I'm wondering how to set random effect? I have the data
'my.data':
#n number count
1 0 252
2 1 163
3 2 120
4 3 95
............................
number | exp(lambda) ~poisson(exp(lambda))
exp(lambda) ~ normal(a,b)
probably I should use a formula:
model.est <- glmer(number ~ 1, family = poisson(link="log"), data = my.data)
but how to set random effect? I do not have predictors. Second I need
to remember that for example 0 occurred 252 times. How to do it - I
can do it using number = seq(number,times=count), but calculation will
last longer.
So you have no clusters with more than one observation??
In that case glmer will complain and quit.
But what makes you think the extra-Poisson variation is indeed
log-Normal??
If you accept that the extra-Poisson variation follows the Gamma
distribution you can use MASS:::glm.nb. On the data you showed it runs
'instantly' using
y2 <- rep(0:3,c(252,163,120,95))
grp2 <- factor(1:length(y2))
fit2 <- glm.nb(y2~1/grp2)
and it fits a two parameter distribution, which is BTW is asymptotically
log-normal (but I make no claim that those asymptotics apply here).
If you really need Poisson-logNormal, write a function that gives the
probabilities of 0,...,k with k big enough to have vanishing probability
for given (a,b) then minimize the difference between those values and the
observed proportions using the Kullback-Liebler distance or the Pearson
Chi-square w.r.t. (a,b).
HTH,
Chuck
I would appreciate any help.
Robert
Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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