Re: [R] Comparing output from linear regression to output from quasipoisson to determine the model that fits best.
John Sorkin wrote:
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:
Since the class of the models is quite different, I'd go on by looking
carefully at the residuals.
Uwe Ligges
fit1-glm(PHYSFUNC~HIV,data=KA)
summary(fit1)
fitQP-glm(PHYSFUNC~HIV,data=KA,family=quasipoisson)
summary(fitQP)
anova(fit1,fitOP)
Program OUTPUT:
fit1-glm(PHYSFUNC~HIV,data=KA)
summary(fit1)
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) 4.196700.08508 49.33 2e-16 ***
HIV -0.004870.12071 -0.040.968
---
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
fitQP-glm(PHYSFUNC~HIV,data=KA,family=quasipoisson)
summary(fitQP)
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 -0.040.968
---
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
anova(fit1,fitQP)
Analysis of Deviance Table
Model 1: PHYSFUNC ~ HIV
Model 2: PHYSFUNC ~ HIV
Resid. Df Resid. Dev Df Deviance
1 6252 142429
2 6252 354390 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)
Confidentiality Statement:
This email message, including any attachments, is for th...{{dropped:6}}
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[R] 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:
fit1-glm(PHYSFUNC~HIV,data=KA)
summary(fit1)
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) 4.196700.08508 49.33 2e-16 ***
HIV -0.004870.12071 -0.040.968
---
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
fitQP-glm(PHYSFUNC~HIV,data=KA,family=quasipoisson)
summary(fitQP)
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 -0.040.968
---
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
anova(fit1,fitQP)
Analysis of Deviance Table
Model 1: PHYSFUNC ~ HIV
Model 2: PHYSFUNC ~ HIV
Resid. Df Resid. Dev Df Deviance
1 6252 142429
2 6252 354390 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)
Confidentiality Statement:
This email message, including any attachments, is for th...{{dropped:6}}
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
