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Comparing output from linear regression to output from quasipoisson to determine the model that fits best.

R 2.7
Windows XP

I have two model that have been run using exactly the same data, both fit using glm(). One model is a linear regression (gaussian(link = "identity"))  the other a quasipoisson(link = "log"). I have log likelihoods from each model. Is there any way I can determine which model is a better fit to the data? anova() does not appear to work as the models have the same residual degrees of freedom:

fit1<-glm(PHYSFUNC~HIV,data=KA)
summary(fit1)

fitQP<-glm(PHYSFUNC~HIV,data=KA,family=quasipoisson)
summary(fitQP)

anova(fit1,fitOP)


Program OUTPUT:
Call:
glm(formula = PHYSFUNC ~ HIV, data = KA)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-4.197  -4.192  -2.192   2.808  19.808  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.19670    0.08508   49.33   <2e-16 ***
HIV         -0.00487    0.12071   -0.04    0.968    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for gaussian family taken to be 22.78134)

    Null deviance: 142429  on 6253  degrees of freedom
Residual deviance: 142429  on 6252  degrees of freedom
  (213 observations deleted due to missingness)
AIC: 37302

Number of Fisher Scoring iterations: 2
Call:
glm(formula = PHYSFUNC ~ HIV, family = quasipoisson, data = KA)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-2.897  -2.895  -1.193   1.250   6.644  

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.434297   0.020280   70.72   <2e-16 ***
HIV         -0.001161   0.028780   -0.04    0.968    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for quasipoisson family taken to be 5.432011)

    Null deviance: 35439  on 6253  degrees of freedom
Residual deviance: 35439  on 6252  degrees of freedom
  (213 observations deleted due to missingness)
AIC: NA

Number of Fisher Scoring iterations: 5
Analysis of Deviance Table

Model 1: PHYSFUNC ~ HIV
Model 2: PHYSFUNC ~ HIV
  Resid. Df Resid. Dev   Df Deviance
1      6252     142429              
2      6252      35439    0   106989
Thanks,
John





John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

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