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<table width="100%" summary="page for glmmPQL {MASS}"><tr><td>glmmPQL {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>Fit Generalized Linear Mixed Models via PQL</h2>
<h3>Description</h3>
<p>
Fit a GLMM model with multivariate normal random effects, using
Penalized Quasi-Likelihood.
</p>
<h3>Usage</h3>
<pre>
glmmPQL(fixed, random, family, data, correlation, weights,
control, niter = 10, verbose = TRUE, ...)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>fixed</code></td>
<td>
a two-sided linear formula giving fixed-effects part of the model.
</td></tr>
<tr valign="top"><td><code>random</code></td>
<td>
A formula or list of formulae describing the random effects.
</td></tr>
<tr valign="top"><td><code>family</code></td>
<td>
a GLM family.
</td></tr>
<tr valign="top"><td><code>data</code></td>
<td>
an optional data frame used as the first place to find
variables in the formulae.
</td></tr>
<tr valign="top"><td><code>correlation</code></td>
<td>
an optional correlation structure.
</td></tr>
<tr valign="top"><td><code>weights</code></td>
<td>
optional case weights as in <code>glm</code>.
</td></tr>
<tr valign="top"><td><code>control</code></td>
<td>
an optional argument to be passed to <code>lme</code>.
</td></tr>
<tr valign="top"><td><code>niter</code></td>
<td>
maximum number of iterations.
</td></tr>
<tr valign="top"><td><code>verbose</code></td>
<td>
logical: print out record of iterations?
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
Further arguments for <code>lme</code>.
</td></tr>
</table>
<h3>Details</h3>
<p>
<code>glmmPQL</code> works by repeated calls to <code><a href="../../nlme/html/lme.html">lme</a></code>, so
package <code>nlme</code> will be loaded at first use if necessary.
</p>
<h3>Value</h3>
<p>
A object of class <code>"lme"</code>: see <code><a href="../../nlme/html/lmeObject.html">lmeObject</a></code>.</p>
<h3>References</h3>
<p>
Schall, R. (1991) Estimation in generalized linear models with
random effects.
<EM>Biometrika</EM>
<B>78</B>, 719–727.
</p>
<p>
Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in
generalized linear mixed models.
<EM>Journal of the American Statistical Association</EM>
<B>88</B>, 9–25.
</p>
<p>
Wolfinger, R. and O'Connell, M. (1993) Generalized linear mixed models: a
pseudo-likelihood approach.
<EM>Journal of Statistical Computation and Simulation</EM>
<B>48</B>, 233–243.
</p>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>See Also</h3>
<p>
<code><a href="../../nlme/html/lme.html">lme</a></code>
</p>
<h3>Examples</h3>
<pre>
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
</pre>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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