Yes, this seems to have done something. Now I need to figure out how to use this code snippet. Any resources or recommendations? I will check back, but I am now off to the web to better understand the statement.
P.S., I can likely stare at the code and figure it out, but how does the scoring treat the "Dispersion" parameter. I only recently started to using GENMOD for its easy BAYES features. Also, in the below example, DV=logged continuous variable and IV1 is binary and IV2 is binary. Thank you.
label P_LOS_nl = 'Predicted: LOS_nl' ;
drop _LMR_BAD;
_LMR_BAD=0;
*** Generate design variables for Protocol;
drop _0_0 _0_1 ;
_0_0= 0;
_0_1= 0;
length _st8 $ 8; drop _st8;
_st8 = left(trim(put (Protocol, $8.)));
if _st8 = 'n' then do;
_0_0 = 1;
end;
else if _st8 = 'y' then do;
_0_1 = 1;
end;
else do;
_0_0 = .;
_0_1 = .;
_LMR_BAD=1;
goto _SKIP_000;
end;
*** Generate design variables for Surg_Consult;
drop _1_0 _1_1 ;
_1_0= 0;
_1_1= 0;
length _st8 $ 8; drop _st8;
_st8 = left(trim(put (Surg_Consult, $8.)));
if _st8 = 'n' then do;
_1_0 = 1;
end;
else if _st8 = 'y' then do;
_1_1 = 1;
end;
else do;
_1_0 = .;
_1_1 = .;
_LMR_BAD=1;
goto _SKIP_000;
end;
*** Compute Linear Predictors;
drop _LP0;
_LP0 = 0;
*** Effect: Protocol;
_LP0 = _LP0 + (-0.11223272308563) * _0_0;
*** Effect: Surg_Consult;
_LP0 = _LP0 + (-0.62587806220401) * _1_0;
*** Predicted values;
_LP0 = _LP0 + 3.06620493060315;
_SKIP_000:
if _LMR_BAD=1 then do;
P_LOS_nl = .;
end;
else do;
P_LOS_nl = _LP0;
end;
