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, ripley at stats.ox.ac.uk
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