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<html><head><title>R: Semiparametric Bayesian Gaussian copula estimation</title>
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<table width="100%" summary="page for sbgcop.mcmc {sbgcop}"><tr><td>sbgcop.mcmc {sbgcop}</td><td align="right">R Documentation</td></tr></table>
<h2>Semiparametric Bayesian Gaussian copula estimation</h2>
<h3>Description</h3>
<p>
<code>sbgcop.mcmc</code> is used to semiparametrically estimate the
parameters of a Gaussian copula. It can be used for posterior
inference on the copula parameters, or for imputation of
missing values in matrix-valued data.
</p>
<h3>Usage</h3>
<pre>
sbgcop.mcmc(Y, S0 = diag(dim(Y)[2]), n0 = dim(Y)[2] + 2, nsamp = 100,
odens = max(1, round(nsamp/1000)), seed = 1, verb = TRUE)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>Y</code></td>
<td>
an n x p matrix. Missing values are allowed. </td></tr>
<tr valign="top"><td><code>S0</code></td>
<td>
a p x p positive definite matrix </td></tr>
<tr valign="top"><td><code>n0</code></td>
<td>
a positive integer </td></tr>
<tr valign="top"><td><code>nsamp</code></td>
<td>
number of iterations of the Markov chain. </td></tr>
<tr valign="top"><td><code>odens</code></td>
<td>
output density: number of iterations between
saved samples. </td></tr>
<tr valign="top"><td><code>seed</code></td>
<td>
an integer for the random seed</td></tr>
<tr valign="top"><td><code>verb</code></td>
<td>
print progress of MCMC(TRUE/FALSE)? </td></tr>
</table>
<h3>Details</h3>
<p>
This function produces MCMC samples from the posterior
distribution of a correlation matrix, using a scaled
inverse-Wishart prior distribution and an extended rank
likelihood. It also provides imputation for missing values
in a multivariate dataset.
</p>
<h3>Value</h3>
<p>
An object of class <code>psgc</code> containing the following components:
</p>
<table summary="R argblock">
<tr valign="top"><td><code>C.psamp </code></td>
<td>
an array of size p x p x <code>nsamp/odens</code>,
consisting of posterior samples of the correlation matrix. </td></tr>
<tr valign="top"><td><code>Y.pmean </code></td>
<td>
the original datamatrix with imputed
values replacing missing data </td></tr>
<tr valign="top"><td><code>LPC </code></td>
<td>
the log-probability of the latent variables at each
saved sample. Used for diagnostic purposes. </td></tr>
</table>
<h3>Author(s)</h3>
<p>
Peter Hoff
</p>
<h3>References</h3>
<p>
http://www.stat.washington.edu/hoff/
</p>
<h3>Examples</h3>
<pre>
fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)
</pre>
<hr><div align="center">[Package <em>sbgcop</em> version 0.95 <a href="00Index.html">Index]</a></div>
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