Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm & weights), on the following context:
1) unbalanced sample (n0=10000, n1=700); 2) sampling weights used to rebalance the sample (w0=1, w1=14.29); e 3) after modelling, adjust the intercept in order to reflect the expected % of 1’s in the population (e.g., circa 7%, as opposed to 50%). I have identified references that deal with the last point, but no conclusive article or book dealing with this specific use of weights in unbalaced samples. The area under the ROC is about 0.70, and the estimated probabilities are close to the frequencies of 1’s in different ranges, which looks satisfactory. Hosmer & Lemeshow’s test is not significant, as expected. Can someone comment on the adopted strategy, or suggest some specific bibliography that might address the issue of weights and unbalanced samples in logistic regression? Thanks in advance, André Guimarães ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
