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? thanks! ______________________________________________ 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.
