I am using
PROC LOGISTIC along with
Class statements to do binary logit model(default=1,non-default=0) on a bank loan dataset where I have 7 numeric independent variables and 1 categorical independent variable.
Hosmer and Lemeshow test yields a
p = 0.6757, and
Percent Concordant = 73.3 so the model is fine I think.
To my dismay Gini is coming up as
0.2305, and ks is only
I there any way I can do anything about the results or am I missing anything?
%let Keepvar= Age_in_years Level_of_education Years_with_Current_Employer Years_at_Current_Address Household_income_in_thousands Debt_to_income_ratio Credit_card_debt__in_thousands Other_debt_in__in_thousands; PROC LOGISTIC DATA = work.bankloan descending outest=estimates; class Level_of_education (ref='1') / param=ref; MODEL Defaulted = &keepvar/selection=backword rsquare lackfit; RUN; %macro rankorder(data=, est=, dependent= ); proc score data = &data score = &est out = mlscore type = parms; var &keepvar; run; data mlscore; set mlscore; prob = exp(&dependent.2) /(1+exp(&dependent.2)); run; proc rank data = mlscore out = mlrankscore groups =10; var prob; ranks probrank; run; proc sql; select probrank, avg(prob) as probability, count(*) as total,avg(&dependent) as Act_Avg_Response, sum(&dependent) as sum_of_response from mlrankscore group by probrank order by probrank descending; quit; %mend; %rankorder(data=bankloan, est=estimates, dependent=Defaulted);