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Low counts

1 message · Nicholas Lewin-Koh

Original
Nicholas Lewin-Koh
# 
Hi Tore,
What your seeing in the residuals may just be due to the "discreetness"
of count data.
Gordon Smyth has a nice paper on this topic (and code in the statmod
package):
Dunn, P. K., and Smyth, G. K. (1996). Randomized quantile residuals.
Journal of Computational and Graphical Statistics 5, 236-244. 
In general the "stars at night" is what you want to see in residuals,
but often,
especially in the case of small counts the Poisson or binomial may be
just fine,
but the residuals will have that striping effect because you have a very
small mean
and hence not much variation to spread the data out. Before you go
running
to a zero inflated model, or a glmm, squint your eyes and look at the
results
you are getting from the simple glm. Is the model adequate? does it
describe the 
data sufficiently? Is the improvement in the likelihood huge between the
Poisson and
the quasipoisson? Especially if you don't have a lot of data, all these
fancy
models may not be worth the cost of estimating all the extra parameters.
But at
the end of the day, since we can't see the data, you need to make the
call.

Cheers
Nicholas