[R] Anova function and glm.nb

From: Nelson, Gary (FWE) <Gary.Nelson_at_state.ma.us>
Date: Mon, 07 Apr 2008 15:20:44 -0400


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.



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