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nbar
2007 Jan 06
2
negative binomial family glm R and STATA
Dear Lister,
I am facing a strange problem fitting a GLM of the negative binomial
family. Actually, I tried to estimate theta (the scale parameter)
through glm.nb from MASS and could get convergence only relaxing the
convergence tolerance to 1e-3. With warning messages:
glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3))
There were 25 warnings (use warnings() to see
2005 Jun 30
1
RE : Dispersion parameter in Neg Bin GLM
...upTREAT -1.0205 0.2598 -3.9287 < 1e-4
Overdispersion coefficients:
Estimate Std. Error z value Pr(> z)
phi.groupCTRL 0.8287 0.412 2.0117 0.0221
Overdispersion coefficients set to fixed values:
Value
phi.groupTREAT 0
Log-likelihood = -121.149; nbpar = 3; df.residual = 72; Deviance = 111.826; AIC = 248.297
________________________________
De: r-help-bounces at stat.math.ethz.ch de la part de Edward McNeil
Date: jeu. 6/30/2005 8:50
??: r-help at r-project.org
Objet : [R] Dispersion parameter in Neg Bin GLM
Hi,
Can someone tell me if...
2011 Feb 10
2
Comparison of glm.nb and negbin from the package aod
...)
(Intercept) -3.795e+00 1.421e+00 -2.671e+00 7.570e-03
ll 9.378e-01 2.221e-01 4.222e+00 2.417e-05
Overdispersion coefficients:
Estimate Std. Error z value Pr(> z)
phi.(Intercept) 1.154e-01 5.56e-02 2.076e+00 1.895e-02
Log-likelihood statistics
Log-lik nbpar df res. Deviance AIC AICc
-8.77e+01 3 29 5.209e+01 1.814e+02 1.822e+02
The thing i really dont understand is why there is such a big difference
between the deviances? (glm.nb = 30.67 and negbin=52.09?) Shouldnt they be
nearly the same??
thanks for your help,
sabine...