I'm working on a project and have run into an expected issue. After running PROC LOGISTIC on my data, I noticed that a few of the odds ratios and regression coefficients seemed to be the inverse of what they should be. After some investigation using PROC FREQ to run the odds ratios, I believe there is some form of error with the odds ratios from PROC LOGISTIC.
The example below is of the response variable "MonthStay" and one of the variables in question "KennelCough". MonthStay = Y and the event of interest is KennelCough = N.
I don't know how to remedy this suspected error. Am I missing something in my code to get the correct calculations? Or am I totally misunderstanding what's going on? Thanks!
Here is the PROC FREQ code and result:
proc freq data = capstone.adopts_dog order = freq;
tables KennelCough*MonthStay / relrisk;
run;
Here is the PROC LOGISTIC CODE and results:
proc logistic data = capstone.adopts_dog plots(only)=(roc(id=prob) effect);
class Breed(ref='Chihuahua') Gender(ref='Female')
Color(ref='Black') Source(ref='Stray') EvalCat(ref='TR') SNAtIn(ref='No')
FoodAggro(ref='Y') AnimalAggro(ref='Y') KennelCough(ref='Y') Dental(ref='Y')
Fearful(ref='Y') Handling(ref='Y') UnderAge(ref='Y') InJuris(ref='Alameda County')
InRegion(ref='East Bay SPCA - Dublin') OutRegion(ref='East Bay SPCA - Dublin')
/ param=ref;
model MonthStay(event='Y') = Age Gender Breed Weight Color Source EvalCat SNatIn
NumBehvCond NumMedCond FoodAggro AnimalAggro KennelCough Dental Fearful
Handling UnderAge Injuris InRegion OutRegion
/ lackfit aggregate scale = none selection = backward rsquare;
output out = probdogs4 PREDPROBS=I reschi = pearson h = leverage;
run;
Class Level Info
Odds Ratios Estimates
