Hi all, I am running a logistic stepwise to produce a model of predictive independent variables to some response variable. At the moment I am currently running the below code on a dataset which in turns produces some model. proc logistic data=inputdata outset=ouptutdata covout; /* Parameter estimates and and their covariances for the final selected model */ model default_12mnd (event='1')=&faktorlist. / selection=stepwise slentry=0.3 /* A significance level of 0.3 is required to allow a variable into the model */ slstay=0.35 /* A significance level of 0.35 is required for a variable to stay in the model */ details lackfit; /* A Hosmer and Lemeshow goodness-of-fit test for the final selected model */ output out=pred p=phat lower=lcl upper=ucl /* The output contains the cumulative predicted probabilities and the corresponding confidence limits, and the individual and cross validated predicted probabilities for each observation */ predprob=(individual crossvalidate); run; My question is assuming the data can be grouped then is there a way to select/force say, a maximum of 2 variables from grouping 1, max 3 variables from grouping 2 etc? Ultimately I would like to try and create a more 'balanced' model as at the moment most variables that end up in the model tend to be from one particular grouping. I understand this this will result in a less accurate model but ultimately would like it to be more practical. Thank you in advance

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