Ordinal Categorical Data
#
Website for ANALYSIS OF ORDINAL CATEGORICAL DATA, 2nd edition

For the second edition of Analysis of Ordinal Categorical Data by Alan
Agresti (Wiley, 2010), this site contains (1) data sets for a couple
of examples in the form of complete SAS programs for conducting the
analyses, (2) examples of the use of various R functions (especially
Thomas Yee's vglm function) for modeling ordinal data (3) examples of
the use of Joe Lang's mph.fit R function for conducting various
analyses in the book, (4) links for some Stata examples, and (5)
corrections of errors in early printings of the book.
**1. Data:** **Datasets**
contains data sets not shown completely in the text, some of them
in the form of SAS programs for conducting the analyses.

**2. R:** **Use of R for ordinal
models** is a pdf file I have prepared of examples of the
use of R, including cumulative logit models, cumulative logit models
without proportional odds, adjacent-categories logit model,
continuation-ratio logit model, cumulative probit model, complementary
log-log model, and use of GEE for repeated measurement data with
cumulative logit model.
**3. R mph.fit:** For examples of the use of the mph.fit
R function for the analysis of standardized residuals for a cumulative
logit model in Section 3.5.8, the ML partial proportional odds
analysis in Section 3.6.5, ML fitting of the mean response model in
Section 5.6.2, the ML fitting of the global odds ratio uniform
association model in Section 6.6.2, ML fitting of the marginal
cumulative logit model for a square table discussed in Section 8.2.1,
the ML fitting of the marginal cumulative logit model to the crossover
data in Section 8.4.3, and the ML fitting of the paired preference
models of Section 8.6.4 that permit responses to be dependent, click
on **mph.fit R analyses**. The function
and a detailed manual can be obtained
from Joseph Lang's home
page.
**4. Stata:** For those who use Stata, you may find it helpful to refer
to **examples**
of categorical data analyses for the first edition of my text "An
Introduction to Categorical Data Analysis" at the site set up by the
UCLA Statistical Computing Center. In particular, Chap 7 has an
example of the linear-by-linear association model, Chap 8 has an example
of the cumulative logit model with proportional odds structure, and
Chap 9 has an example of ordinal quasi-symmetry. In Stata, the ologit
program fits cumulative logit models and the oprobit program fits
cumulative probit models. See
Stata help for
ologit. A program omodel is available from the Stata website for
fitting these models and testing the assumption of the same effects
for each cumulative probability (i.e., the proportional odds
assumption for cumulative logit models). Other ways to fit cumulative
link models are with the
Stata oglm module.
Continuation-ratio logit models can be fitted with the ocratio module.
See Stata
ocratio search. For information about using GEE in Stata,
see Horton
article
and Stata GEE
search. The GLLAMM module for
Stata can fit a very wide variety of models, including logit and
cumulative logit models with random effects. For further details, see
and Stata
gllamm search and Chapter 5 of "Multilevel and Longitudinal
Modeling Using Stata" by S. Rabe-Hesketh and A. Skrondal (Stata
Press, 2005).

**5. Corrections:** Here is a pdf file
showing ** corrections ** of
typos/errors in the second edition. Please e-mail me
(aa@stat.ufl.edu) with errors that you notice.