[R-sig-ME] GLMM for underdispersed count data: Conway-Maxwell-Poisson and Ordinal
Simone Santoro
santoro at ebd.csic.es
Tue Dec 6 14:41:19 CET 2016
Dear all,
I am trying to find an appropriate GLMM (with temporal and individual
crossed random effects) to model underdispersed count data (clutch
size). I have found several possible ways of doing that. A good
distribution for data like this would seem to be the
Conway-Maxwell-Poisson but I have not found a way of using it within a
GLMM in R (I have asked here
<http://stats.stackexchange.com/questions/249738/how-to-define-the-nu-parameter-of-conway-maxwell-poisson-in-spamm-package>
and here
<http://stats.stackexchange.com/questions/249798/conway-maxwell-poisson-with-crossed-random-effects-in-r>).
I have seen that Ben Bolker suggested (here
<https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q1/021945.html>and
here
<http://stats.stackexchange.com/questions/92156/how-to-handle-underdispersion-in-glmm-binomial-outcome-variable>)
to use an ordinal model in cases like this(e.g. _ordinal:clmm_). I have
tried this solution and the results I obtain makes (biological) sense to
me. However, I wonder why but I cannot put all the three crossed random
effects I have in the clmm model (_Error: no. random effects (=1254) >=
no. observations (=854)_) whereas it is not a problem for the glmer
model (the no. of levels of each single random effect does not exceed 854)*.
Beyond that, and that's what I would like to ask you, *I cannot find a
reference to justify I used the ordinal model* to deal with
underdispersed count data (referee will ask it for sure).
Best,
Simone
* FMglmer<- glmer(fledges ~ habitatF * (areaPatchFath + poligF01 +
StdLayingDate + ageFath1 + ageMoth1) + (1|year) + (1|ringMoth) +
(1|ringFath), data = datiDRS)
FMclmm<- glmer(as.factor(fledges)~ habitatF * (areaPatchFath +
poligF01 + StdLayingDate + ageFath1 + ageMoth1) + (1|year) +
(1|ringMoth) + (1|ringFath), data = datiDRS)
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