Hi,
I have a question about proc logistic.
I am creating a regresion logistic model with proc logistic.
All the vars included in the model have dependencies with the target table.
Using proc freq or proc discrim I can see that there is a dependency
When I am exploring the vars, I find that there are vars highly correlated.
The vars rango_ant and rango_edad hace a correlation about 0.9
Do I have to exclude one of them from my model?
This is my model:
proc logistic data=test outmodel=modelo1 plots(only)=roc; class cod_posicion nivel_sal rango_edad rango_ant rango_eval; model baja = rango_edad rango_ant nivel_sal rango_eval cod_posicion ; quit;
Do I have tu use cross effects?. Like this:
proc logistic data=test outmodel=modelo1 plots(only)=roc;
class cod_posicion nivel_sal rango_edad rango_ant rango_eval;
model baja = rango_edad rango_ant nivel_sal rango_eval cod_posicion rango_edad*rango_ant ;
quit;
proc logistic data=test outmodel=modelo1 plots(only)=roc; class cod_posicion nivel_sal rango_edad rango_ant rango_eval; model baja = rango_edad rango_ant nivel_sal rango_eval cod_posicion rango_edad*rango_ant ; quit;
I don't know the effect of correlation in logistic regresion.
Can anybody help me?, any help will be greatly appreciated
Thanks in advance
I suggest you post this in one of the Analytics communities. It really doesn't have anything to do with Enterprise Guide.
Tom
Technically, SAS EG has a task called "Logistic Regression". This task runs PROC LOGISTIC to do the calculations.
It's a little confusing, but here's how I'd describe the dividing line. If I had questions about the meanings of the variable roles or about some of the task options, I'd post under Enterprise Guide. But if a question pertains to the underlying PROC LOGISTIC code, as yours does, I think it's a better fit in the Analytics community. I'm an IT guy, and I monitor the EG community for technical questions about using the product, but I don't have the statistical methodology chops to answer questions like yours. Those guys are in the Analytics community.
Tom
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