Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic.
I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible ratings, 1 through 4), and the values of two predictor variables x1 and x2 associated with each stimulus: --------------------------------------- Obs# y x1 x2 --------------------------------------- 1 3 2.35 -1.07 2 2 1.78 -0.66 3 4 5.19 -3.51 ... 4000 1 0.63 -0.23 --------------------------------------- I get excellent fits using fit1 <-lrm(y ~ x1+x2, data=my.dataframe1) Now I want to see how well my model can predict y for a new set of 4000 observations. I need to predict y for each new observation *individually*. I know an expression like predicted1<-predict(fit1, newdata=my.dataframe2, type=""fitted.ind") can give *probability* of each of the 4 possible responses for each observation. So my questions are (1) How do I pick the likeliest y (i.e., likeliest of the 4 possible ratings) for each given new observation? (2) Are there good reference that explain the theory behind this type of prediction to a beginner like me? Thank you very much, Jay Hegdé Univeristy of Minnesota -- View this message in context: http://www.nabble.com/Predicting-ordinal-outcomes-using-lrm%7BDesign%7D-tp16704403p16704403.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.