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KS99
Obsidian | Level 7

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

Accepted Solutions
sbxkoenk
SAS Super FREQ

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 not nested, 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.

Koen

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4 REPLIES 4
sbxkoenk
SAS Super FREQ

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 not nested, 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.

Koen

KS99
Obsidian | Level 7
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 -
KS99
Obsidian | Level 7
Thank you StatDave! This is a great help! Somebody expanded what you hinted at. I can consult them all!

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