[R] quasipoisson, glm.nb and AIC values
Vicente Piorno
vpiorno at uvigo.es
Wed Mar 12 19:18:28 CET 2003
Dear R users,
I am having problems trying to fit quasipoisson and negative binomials glm.
My data set
contains abundance (counts) of a species under different management regimens.
First, I tried to fit a poisson glm:
> summary(model.p<-glm(abund~mgmtcat,poisson))
Call:
glm(formula = abund ~ mgmtcat, family = poisson)
.
.
.
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 1904.7 on 19 degrees of freedom
Residual deviance: 1154.3 on 16 degrees of freedom
AIC: 1275.4
Number of Fisher Scoring iterations: 4
Wich suggests the existence of STRONG overdispersion, so I tried:
> summary(model.qp<-glm(abund~mgmtcat,quasipoisson))
Call:
glm(formula = abund ~ mgmtcat, family = quasipoisson)
.
.
.
(Dispersion parameter for quasipoisson family taken to be 73.51596)
Null deviance: 1904.7 on 19 degrees of freedom
Residual deviance: 1154.3 on 16 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 4
Here I found the first problem: AIC is not available.
I know that count data for the studied species usually show aggregation.
So, I fitted
a negative binomial glm with the glm.nb in MASS:
> summary.negbin(model.nb<-glm.nb(abund~mgmtcat))
Call: glm.nb(formula = abund ~ mgmtcat, init.theta =
1.23560100958978, link = log)
.
.
.
(Dispersion parameter for Negative Binomial(1.2356) family taken to
be 1)
Null deviance: 33.173 on 19 degrees of freedom
Residual deviance: 22.316 on 16 degrees of freedom
AIC: -15948
Number of Fisher Scoring iterations: 1
Correlation of Coefficients:
(Intercept) mgmtcat1 mgmtcat2
mgmtcat1 -0.7052
mgmtcat2 -0.7053 0.4974
mgmtcat3 -0.7005 0.4940 0.494
Theta: 1.236
Std. Err.: 0.362
2 x log-likelihood: -211.079
And now, I am getting a negative AIC value! I have seen that this problem
have been discused in the S-news list.
Much of the discussion there is far beyond my statistical and R knowledge.
One of the solutions proposed there
was adding - lgamma(y +1) to the internal function loglik in glm.nb, but I
have seen that the current version of
MASS contains that term.
My problem is that I want to compare the quasipoisson and negative binomial
models, and I have a NA value and a negative one.
Can I obtain an AIC for the quasipoisson model? What about the negative
AIC? Can I use it or do you think that anything is wrong?
Thanks in advance,
--
Vicente Piorno
Departamento de Ecologia y Biologia Animal - Universidad de Vigo
EUIT Forestal - Campus Universitario
36005 Pontevedra SPAIN
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