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Posted 02-02-2024 04:54 AM
(1026 views)

Hi, all, dear SAS community,

Please bear with me if I returned with too simple a stats question.

I ran two Poisson models, one simply with controls, the other with controls plus variables of interest.

```
/** Model (1) **/
proc genmod data = dataset;
model Y = X1 X2 X3 X4 X5 year1-year9 ind1-ind63 / dist=poisson link=log; run;
/** Model (2) **/
proc genmod data = dataset;
model Y = X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 year1-year9 ind1-ind63 / dist=poisson link=log; run;
```

I want to check whether one model is more significant (in terms of fit) than the other. In other words, I want to perform a model test like F-Test when comparing OLS models. According to my internet search, Poisson works differently; they suggest using CONTRAST option in PROC GENMOD but I am not really sure. And the ways they show how to use CONTRAST options look so complicated. Please help! I really appreciate you, SAS community!

Sincerely,

KS -,

1 ACCEPTED SOLUTION

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Indeed, you need a a **likelihood ratio test**.

Like is said on this page:

SAS® 9.4 and SAS® Viya® 3.5 Programming Documentation

SAS Visual Statistics Programming Guide

Regression Action Set

Poisson Regression

https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/casactstat/casactstat_regression_examples0...

- See the "Fit Statistics" table. The –2 log likelihood at the converged estimates is a certain numeric value (the lower the better -- a perfect fitting model would have a value of 0). You can use this –2 log likelihood value
**to compare the model to nested model alternatives**by means of a**likelihood ratio test**. **To compare models that are**, information criteria such as AIC (Akaike’s information criterion), AICC (Akaike’s bias-corrected information criterion), and BIC (Schwarz Bayesian information criterion) are used. These criteria penalize the –2 log likelihood for the number of parameters.*not*nested

Koen

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See this note and the examples it links to.

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Indeed, you need a a **likelihood ratio test**.

Like is said on this page:

SAS® 9.4 and SAS® Viya® 3.5 Programming Documentation

SAS Visual Statistics Programming Guide

Regression Action Set

Poisson Regression

https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/casactstat/casactstat_regression_examples0...

- See the "Fit Statistics" table. The –2 log likelihood at the converged estimates is a certain numeric value (the lower the better -- a perfect fitting model would have a value of 0). You can use this –2 log likelihood value
**to compare the model to nested model alternatives**by means of a**likelihood ratio test**. **To compare models that are**, information criteria such as AIC (Akaike’s information criterion), AICC (Akaike’s bias-corrected information criterion), and BIC (Schwarz Bayesian information criterion) are used. These criteria penalize the –2 log likelihood for the number of parameters.*not*nested

Koen

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Thank you, sbxkoenk! This is a great and detailed help! I can use it my entire life as long as I get troubled by Poisson!

KS -

KS -

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Thank you StatDave! This is a great help! Somebody expanded what you hinted at. I can consult them all!

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