In linear regression, I've seen (granted, not many) situations where basic transformations to some of the predictors can significantly improve the fit and stability of the model, and often a scatterplot of the response variable vs each individual predictor can provide useful clues as to whether a transformation can help.
Is there a similar approach that would work for logistic regression – i.e., what is an intuitive way to determine whether any predictor transformations can help?
Best Answer
If it's feasible that you can bin in such a way that the bins aren't too wide but all of them contain some 0s and some 1s, it's possible to do a logit-transformation on the proportions and see if logit(p) is reasonably linear. However, the presence of other (meaningful/important) IVs can make the impression from such marginal relationships meaningless
Another possibility is to fit a nonparametric relationship and see if it's clearly showing signs that it's not logistic in shape; this can be done in the presence of other covariates, since one can fit a GAM term in an otherwise linear (in the GLM sense) model.