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). -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ 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.