[R] Anova function and glm.nb
Nelson, Gary (FWE)
Gary.Nelson at state.ma.us
Mon Apr 7 21:20:44 CEST 2008
Hi All,
I am using the glm.nb function from the MASS package (current version)
to fit and compare GLMs with negative binomial error distributions. My
question is: what is the appropriate method to use in the anova function
to compare models? If only one fitted object like
m<-glm.nb(number<-p+sal+temp,data=data)
is specified in the anova function (anova(m)), a fixed theta is used to
generate the analysis of deviance:
Analysis of Deviance Table
Model: Negative Binomial(0.2345), link: log
Response: number
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL 117 122.707
p 1 11.327 116 111.380 0.001
sal 1 2.286 115 109.094 0.131
tem 1 1.979 114 107.115 0.159
ph 1 2.567 113 104.549 0.109
Warning message:
In anova.negbin(m) : tests made without re-estimating 'theta'
If multiple fitted objects like
m1<-glm.nb(number~1,data=data)
m2<-glm.nb(number~p,data=data)
m3<-glm.nb(number~p+sal,data=data)
m4<-glm.nb(number~p+sal+temp,data=data)
is specified (anova(m1,m2,m3,4)), the theta is assumed re-estimated in
each case to calculate the likelihood ratio tests:
Likelihood ratio tests of Negative Binomial Models
Response: number
Model theta Resid. df 2 x log-lik. Test df LR
stat. Pr(Chi)
1 1 0.1892056 117 -527.7463
2 p 0.2153105 116 -517.9349 1 vs 2 1
9.811382 0.001734351
3 p + sal 0.2214626 115 -515.7942 2 vs 3 1
2.140706 0.143435894
4 p + sal + tem 0.2261900 114 -513.8846 3 vs 4 1
1.909643 0.167002884
5 p + sal + tem + ph 0.2344827 113 -511.3633 4 vs 5 1
2.521237 0.112322429
The conclusions are the same, but I'd like to know if one method is
favored over the other.
Thanks,
Gary Nelson.
More information about the R-help
mailing list