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
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