Re: [R] OT: Predicted probabilities from ordinal regressions

From: Frank E Harrell Jr <>
Date: Fri, 11 May 2007 16:03:50 -0500

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
> 30,000).
> 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?
> Thanks.

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) -
> Assistant Professor of Sociology; Book Review Editor, _Social Forces_
> University of North Carolina - CB#3210, Chapel Hill, NC 27599-3210 USA
> New Book:
> ______________________________________________
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> 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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Received on Fri 11 May 2007 - 21:08:49 GMT

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