I may be missing a point, but the proportional odds model easily gives you odds ratios for Y>=j (independent of j by PO assumption). Other options include examining a rank correlation between the linear predictor and Y, or (if Y is numeric and spacings between categories are meaningful) you can get predicted mean Y (see the Mean.lrm in the R rms package, a replacement for the Design package).
Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Frank-Harrell-in-the-predict-lrm-Design-help-tp2883311p2954274.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
