[R] OT: Predicted probabilities from ordinal regressions
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Fri May 11 23:03:50 CEST 2007
Andrew Perrin wrote:
> Sorry if this is too off-topic, as we may not implement this in R
> (although we may do so).
> A student of mine is looking at some public opinion data in which there
> appears to be a statistically significant difference between levels of
> support for a proposal based on which of two question wordings is used.
> That is, roughly half the sample was asked the question with wording 1,
> the other half with wording 2, and the difference is large enough to be of
> interest (approx. 6 percentage points different with an N of about
> The question is how best to model this. In the past I have generated
> predicted probabilities based on the sample asked wording 1, used these to
> assign those asked wording 2 to predicted categories, and used a logistic
> regression to predict difference between predicted and actual response. In
> this case, though, the question of interest uses a four-level ordinal
> response, so ordinary predicted probabilities based on, e.g., and ordinal
> logistic regression generate literally probabilities of being in each of
> the four categories. Transforming this outcome into a prediction of
> membership in one of the given categories is not straightforward. Can
> anyone provide some insight into how to model predicted vs. actual
> outcomes on an ordinal scale?
With the lrm function in the Design package you can get predicted
probabilities for each class as well as predicted mean scores.
> Andrew J Perrin - andrew_perrin (at) unc.edu - http://perrin.socsci.unc.edu
> Assistant Professor of Sociology; Book Review Editor, _Social Forces_
> University of North Carolina - CB#3210, Chapel Hill, NC 27599-3210 USA
> New Book: http://www.press.uchicago.edu/cgi-bin/hfs.cgi/00/178592.ctl
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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