Prof Brian Ripley explained : > On Mon, 26 Nov 2007, Max wrote: > >> Hi everyone, I'm trying to understand some R output here for ordinal >> regression. I have some integer data called "A" split up into 3 ordinal >> categories, top, middle and bottom, T, M and B respectively. >> >> I have to explain this output to people who have a very poor idea about >> statistics and just need to make sure I know what I'm talking about >> first. >> >> Here's the output: >> >> Call: >> polr(formula = Factor ~ A, data = a, Hess = TRUE, method = "logistic") >> >> Coefficients: >> Value Std. Error t value >> A -0.1259028 0.04758539 -2.645829 >> >> Intercepts: >> Value Std. Error t value >> B|M -2.5872 0.5596 -4.6232 >> M|T 0.3044 0.4864 0.6258 >> >> Residual Deviance: 204.8798 >> AIC: 210.8798 >> >> I really am not sure what the intercepts mean at all. However, my >> understanding of the coefficient of A is that as the category >> increases, A decreases? If I have an A value of 10, how to I figure out >> the estimated probability that this score is in one of the three >> categories? > > Use predict(): see the book polr supports for examples (and the theory).
I appreciate the reply, but have difficulty understanding what you mean by "the book polr supports"? :-? The manuals in R don't reference the polr() command, nor do they write about ordinal regression in R. (from what I can tell) The documentation of the polr() doesn't explain the output or the theory... I've done web searches on polr() and the MASS library and have found little of direct help to my question. Thanks, -Max ______________________________________________ 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.
