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<TABLE WIDTH="100%"><TR><TD>ordglm {gnlm}</TD><TD ALIGN="right">R Documentation</TD></TR></TABLE>
<h2>Generalized Linear Ordinal Regression Models</h2>
<h3>Description</h3>
<p>
<CODE>ordglm</CODE> fits linear regression functions with
logistic or probit link to ordinal response data by proportional odds.
<h3>Usage</h3>
<PRE>
ordglm(formula, link="logit", maxiter=10, weights=1)
</PRE>
<h3>Arguments</h3>
<TABLE>
<TR VALIGN="TOP"><TD><CODE>formula</CODE></TD>
<TD>
A model formula. The response must be integers numbered
from zero to one less than the number of ordered categories.</TD></TR>
<TR VALIGN="TOP"><TD><CODE>link</CODE></TD>
<TD>
Logit or probit link function.</TD></TR>
<TR VALIGN="TOP"><TD><CODE>maxiter</CODE></TD>
<TD>
Maximum number of iterations allowed.</TD></TR>
<TR VALIGN="TOP"><TD><CODE>weights</CODE></TD>
<TD>
A vector containing the frequencies for grouped data.</TD></TR>
</TABLE>
<P>
</P>
<h3>Value</h3>
<P>
A list of class ordglm is returned.
The printed output includes the -log likelihood, the corresponding
AIC, the deviance, the maximum likelihood estimates, standard errors,
and correlations.</P>
<h3>Author(s)</h3>
<p>
J.K. Lindsey, adapted and heavily modified from Matlab code
(ordinalMLE) by J.H. Albert.
<h3>References</h3>
<p>
Jansen, J. (1991) Fitting regression models to ordinal
data. Biometrical Journal 33, 807-815.
<P>
Johnson, V.E. and Albert, J.H. (1999) Ordinal Data Modeling. Springer-Verlag.
<h3>See Also</h3>
<p>
<CODE><A HREF="../../base/html/glm.html">glm</A></CODE>, <CODE><A HREF="../../gnlm/html/nordr.html">nordr</A></CODE>
<h3>Examples</h3>
<PRE>
# McCullagh (1980) JRSS B42, 109-142
# tonsil size: 2x3 contingency table
y <- c(0:2,0:2)
carrier <- gl(2,3,6)
wt <- c(19,29,24,497,560,269)
ordglm(y~carrier, weights=wt)
</PRE>
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