You could try a 0 truncated poisson [divide likelihood of poisson by
1prob(y)=0] with NLMIXED:
proc nlmixed DATA=vt ;
parms intrc .1 _r .5 _b .5 _e .5;
eta = intrc + _r*x1 + _b*x2 + _e*x3;
mean = exp(eta);
loglike= y*LOG(mean) mean  lgamma(y+1)  log(1  EXP(mean));
model y ~ general(loglike);
run;
Could do the same for negative binomial, though If # of offspring not much
larger than indicated, may not offer an improvement.
Robin High
UNMC
From:
Andrew Cox <wacox@MIZZOU.EDU>
To:
SASL@LISTSERV.UGA.EDU
Date:
11/30/2011 10:22 AM
Subject:
Underdispersion in Poisson/negative binomial regression (PROC GENMOD)
Sent by:
"SAS(r) Discussion" <SASL@LISTSERV.UGA.EDU>
Hi,
I am working on a project where we are assessing the number of offspring
produced as a function
of a number of covariates. We are only doing this for adults who produced
>=1 young, as the
question of failing to produce any young is distinct from how many young
are produced per
successful reproduction attempt. As such, a histogram of our data looks
something like:
*
*
* *
* * *
* * * *
* * * * *
1 2 3 4 5
That is the general shape, not a reflection of our actual sample size.
Anyway, when I run the
following code:
proc genmod data=test2;
model numyoung=explanvariable / dist=poisson link=log;
run;
I get very low deviance (0.2) and Pearson Chisquare (0.18) scores, which
indicate either model
misspecification or underdispersion. Frankly, I am not sure what I can do
to address this. I have
tried a negative binomial model with similar results. I see that you can
adjust the scale parameter
using either Scale=Pearson or Scale=deviance, which adjusts the calculated
standard errors, but I
am not statistically knowledgable enough to know whether this would
adequately address my
problem, and if so, why.
Any help is much appreciated.
