Why the Poisson distribution is outputting wrong estimates?

[marcioadg]

Hi,

The QQ Plot suggests that the studentized residuals of the response variables are not normally distributed.

When I run it with a poisson distributions (or others) it is outputting completely unfeasible estimates. It should be around 0.20 (which would be 20%) and it is estimating negative numbers.

I thought could be scaling issues (multiplied by 100) but it is not.

I have tried different estimation methods too (LaPlace, Quadrature). Not sure if it could be the linkfunction?

Any thoughts?

Thanks,

Marcio

[Reeza]

Without your data or code its almost impossible to say

[marcioadg]

Thanks Reeza,

Please let me know if this helps. Note this is only one of the codes I tried. Thanks!

proc glimmix data=sows  method=quad  plots=residualpanel(conditional marginal) ;

CLASS week  WeighGr90  ;

MODEL CV = WeighGr90 / dist=poisson   ;

*random _residual_ / group=vargroup ;

random intercept / subject=week ;

lsmeans WeighGr90/pdiff;

output out=igausout pred=p resid=r STUDENT=std;

run;

[lvm]

The default link function for the Poissonis log. So, I think all your graphics are on the log linear predictor scale. You can add the ilink option to the plots statement to get the inverse line (original scale).

[marcioadg]

Thanks lvm, but the problem are the lsmeans estimates, even with the ilink option on the lsmeans statement it is retuning the negative values. Any thoughts?

[lvm · Accepted solution]

You have to be more specific. YOu have to put ilink in the lsmeans statement and in the plots opiton (on the proc statement) to see the inverse link means. Even with ilink option on lsmeans, you first get the logs, then on the right the inverse link (data scale means). Your example code does not give ilink anywhere.

[marcioadg]

lvm,

Using the ilink on the lsmeans statement was actually giving the right information, but you are right I was only looking on the left side of the table (so used to it). It is giving me on the right side like you said, the information that I needed. Thanks a lot! I really appreciate it! Problem solved.