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<h2 id="prev-class-structure-and-methods">‘prev’ class structure and methods</h2>
<p><br>Functions <a href="?main=functions&sub=truePrev"><code>truePrev</code></a>, <a href="?main=functions&sub=truePrevPools"><code>truePrevPools</code></a>, <a href="?main=functions&sub=truePrevMulti"><code>truePrevMulti</code></a> and <a href="?main=functions&sub=truePrevMulti2"><code>truePrevMulti2</code></a> create objects of S4 class <code>prev</code>. Different methods are available for manipulating these objects.</p>
<h3 id="slots">Slots</h3>
<p>Objects of class <code>prev</code> are so-called <code>S4</code> objects. You can think of these objects as being formally defined lists. Instead of using the <code>$</code>-operator for accessing the different elements, which are called slots, the <code>@</code>-operator needs to be used. Objects of class <code>prev</code> contain the following four slots:</p>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>@par</code>
</td>
<td>
A list of input parameters.
</td>
</tr>
<tr vAlign="top">
<td>
<code>@model</code>
</td>
<td>
The fitted Bayesian model, in BUGS language (and given <code>S3</code> class <code>prevModel</code>).
</td>
</tr>
<tr vAlign="top">
<td>
<code>@mcmc</code>
</td>
<td>
A list, with one element per chain, of the simulated true prevalences (and sensitivities and specificities in the case of <a href="?main=docs&sub=truePrevMulti"><code>truePrevMulti</code></a> and <a href="?main=docs&sub=truePrevMulti2"><code>truePrevMulti2</code></a>).
</td>
</tr>
<tr vAlign="top">
<td>
<code>@diagnostics</code>
</td>
<td>
A list with elements for the Deviance Information Criterion (<code>$DIC</code>), the Brooks-Gelman-Rubin statistic (<code>$BGR</code>), and in the case of <a href="?main=docs&sub=truePrevMulti"><code>truePrevMulti</code></a> and <a href="?main=docs&sub=truePrevMulti2"><code>truePrevMulti2</code></a>, the Bayes-P statistic (<code>$bayesP</code>)
</td>
</tr>
</table>
<h3 id="general-methods">General methods</h3>
<h4 id="print-method">print method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(x, <span class="dt">conf.level =</span> <span class="fl">0.95</span>, <span class="dt">dig =</span> <span class="dv">3</span>, ...)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>conf.level</code>
</td>
<td>
Confidence level to be used in credibility interval
</td>
</tr>
<tr vAlign="top">
<td>
<code>dig</code>
</td>
<td>
Number of decimal digits to print
</td>
</tr>
<tr vAlign="top">
<td>
<code>...</code>
</td>
<td>
Other arguments to pass to the <code>print</code> function
</td>
</tr>
</table>
<p>The <code>print</code> method prints the mean, median, mode, standard deviation and credibility interval of the estimated true prevalence (and sensitivities and specificities, for prev objects created by <a href="?main=functions&sub=truePrevMulti"><code>truePrevMulti</code></a> and <a href="?main=functions&sub=truePrevMulti2"><code>truePrevMulti2</code></a>). In addition, the Brooks-Gelman-Rubin statistic and corresponding upper confidence limit are printed, or the multivariate BGR statistic for prev objects created by <a href="?main=functions&sub=truePrevMulti"><code>truePrevMulti</code></a> and <a href="?main=functions&sub=truePrevMulti2"><code>truePrevMulti2</code></a>. BGR values substantially above 1 indicate lack of convergence. For <code>prev</code> objects created by <a href="?main=functions&sub=truePrevMulti"><code>truePrevMulti</code></a>, the Bayes-P statistic is also printed. Bayes-P should be as close to 0.5 as possible.</p>
<h4 id="summary-method">summary method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(object, conf.level)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>object</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>conf.level</code>
</td>
<td>
Confidence level to be used in credibility interval
</td>
</tr>
</table>
<p>The <code>summary</code> method returns the mean, median, mode, standard deviation, variance, credibility interval and number of samples for each chain separately and for all chains combined.</p>
<h4 id="as.matrix-method">as.matrix method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">as.matrix</span>(x, <span class="dt">iters =</span> <span class="ot">FALSE</span>, <span class="dt">chains =</span> <span class="ot">FALSE</span>)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>iters</code>
</td>
<td>
Logical flag, indicating whether a column should be added for iteration number; defaults to <code>FALSE</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>chains</code>
</td>
<td>
Logical flag, indicating whether a column should be added for chain number; defaults to <code>FALSE</code>
</td>
</tr>
</table>
<p>The <code>as.matrix</code> method converts objects of class <code>prev</code> to <code>matrix</code>.</p>
<h4 id="plot-method">plot method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(x, <span class="dt">y =</span> <span class="ot">NULL</span>, ...)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>y</code>
</td>
<td>
Which parameter to plot? Defaults to <code>NULL</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>...</code>
</td>
<td>
Other arguments to pass to the <code>plot</code> function
</td>
</tr>
</table>
<p>The <code>plot</code> method generates density, trace, Brooks-Gelman-Rubin and autocorrelation plots.</p>
<h3 id="methods-inherited-from-the-coda-package">Methods inherited from the <a href="https://cran.r-project.org/package=coda"><strong>coda</strong></a> package</h3>
<h4 id="densplot-method">densplot method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">densplot</span>(x, <span class="dt">exclude_fixed =</span> <span class="ot">TRUE</span>, ...)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>exclude_fixed</code>
</td>
<td>
Should fixed parameters be excluded from plotting? Defaults to <code>TRUE</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>...</code>
</td>
<td>
Other arguments to pass to the <code>densplot</code> function
</td>
</tr>
</table>
<p>Displays a plot of the density estimate for each variable in <code>x</code>, calculated by the <code>density</code> function.</p>
<h4 id="traceplot-method">traceplot method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">traceplot</span>(x, <span class="dt">exclude_fixed =</span> <span class="ot">TRUE</span>, ...)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>exclude_fixed</code>
</td>
<td>
Should fixed parameters be excluded from plotting? Defaults to <code>TRUE</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>...</code>
</td>
<td>
Other arguments to pass to the <code>traceplot</code> function
</td>
</tr>
</table>
<p>Displays a plot of iterations versus sampled values for each variable in the chain, with a separate plot per variable.</p>
<h4 id="autocorr.plot-method">autocorr.plot method</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">autocorr.plot</span>(x, <span class="dt">exclude_fixed =</span> <span class="ot">TRUE</span>, <span class="dt">chain =</span> <span class="dv">1</span>, ...)</code></pre></div>
<table>
<tbody>
<tr vAlign="top">
<td>
<code>x</code>
</td>
<td>
An object of class <code>prev</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>exclude_fixed</code>
</td>
<td>
Should fixed parameters be excluded from plotting? Defaults to <code>TRUE</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>chain</code>
</td>
<td>
Which chain to plot in <code>autocorr.plot</code>? Defaults to <code>1</code>
</td>
</tr>
<tr vAlign="top">
<td>
<code>...</code>
</td>
<td>
Other arguments to pass to the <code>autocorr.plot</code> function
</td>
</tr>
</table>
<p>Plots the autocorrelation function for each variable in each chain in <code>x</code>.</p>
<h3 id="examples">Examples</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Taenia solium cysticercosis in Nepal
SE <-<span class="st"> </span><span class="kw">list</span>(<span class="dt">dist =</span> <span class="st">"uniform"</span>, <span class="dt">min =</span> <span class="fl">0.60</span>, <span class="dt">max =</span> <span class="fl">1.00</span>)
SP <-<span class="st"> </span><span class="kw">list</span>(<span class="dt">dist =</span> <span class="st">"uniform"</span>, <span class="dt">min =</span> <span class="fl">0.75</span>, <span class="dt">max =</span> <span class="fl">1.00</span>)
TP <-<span class="st"> </span><span class="kw">truePrev</span>(<span class="dt">x =</span> <span class="dv">142</span>, <span class="dt">n =</span> <span class="dv">742</span>, <span class="dt">SE =</span> SE, <span class="dt">SP =</span> SP)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Summarize estimates per chain
<span class="kw">summary</span>(TP)
<span class="co">#> $TP</span>
<span class="co">#> mean median mode sd var</span>
<span class="co">#> chain 1 0.1371857 0.1371797 0.1899004 0.07788892 0.006066685</span>
<span class="co">#> chain 2 0.1336157 0.1321198 0.1831823 0.07938643 0.006302206</span>
<span class="co">#> all chains 0.1354007 0.1344100 0.1881068 0.07865953 0.006187322</span>
<span class="co">#> 2.5% 97.5% samples</span>
<span class="co">#> chain 1 0.007131391 0.2847301 10000</span>
<span class="co">#> chain 2 0.006826268 0.2867341 10000</span>
<span class="co">#> all chains 0.006953099 0.2858581 20000</span>
<span class="co">#> </span>
<span class="co">#> $SE</span>
<span class="co">#> mean median mode sd var 2.5%</span>
<span class="co">#> chain 1 0.7781083 0.7664154 0.6305376 0.1160362 0.01346441 0.6066010</span>
<span class="co">#> chain 2 0.7806555 0.7695690 0.6425182 0.1150805 0.01324352 0.6082347</span>
<span class="co">#> all chains 0.7793819 0.7678970 0.6302666 0.1155635 0.01335492 0.6071912</span>
<span class="co">#> 97.5% samples</span>
<span class="co">#> chain 1 0.9867098 10000</span>
<span class="co">#> chain 2 0.9874131 10000</span>
<span class="co">#> all chains 0.9870292 20000</span>
<span class="co">#> </span>
<span class="co">#> $SP</span>
<span class="co">#> mean median mode sd var 2.5%</span>
<span class="co">#> chain 1 0.9009873 0.9005704 0.8490157 0.05666717 0.003211168 0.8045467</span>
<span class="co">#> chain 2 0.8990168 0.8968261 0.8380739 0.05781682 0.003342784 0.8032723</span>
<span class="co">#> all chains 0.9000021 0.8986753 0.8478642 0.05725192 0.003277783 0.8039108</span>
<span class="co">#> 97.5% samples</span>
<span class="co">#> chain 1 0.9938295 10000</span>
<span class="co">#> chain 2 0.9939376 10000</span>
<span class="co">#> all chains 0.9938842 20000</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Diagnostic plots
<span class="kw">par</span>(<span class="dt">mfrow =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>))
<span class="kw">plot</span>(TP)</code></pre></div>
<p><img 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" title alt width="672" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Generic plots from package coda
<span class="kw">par</span>(<span class="dt">mfrow =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>))
<span class="kw">densplot</span>(TP)</code></pre></div>
<p><img 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" title alt width="672" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
<span class="kw">par</span>(<span class="dt">mfrow =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>))
<span class="kw">traceplot</span>(TP)</code></pre></div>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
<span class="kw">par</span>(<span class="dt">mfrow =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>))
<span class="kw">gelman.plot</span>(TP)</code></pre></div>
<p><img 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" title alt width="672" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
<span class="kw">par</span>(<span class="dt">mfrow =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>))
<span class="kw">autocorr.plot</span>(TP)</code></pre></div>
<p><img 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" title alt width="672" /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Use 'str()' to see the structure of object TP
<span class="kw">str</span>(TP)
<span class="co">#> Formal class 'prev' [package "prevalence"] with 4 slots</span>
<span class="co">#> ..@ par :List of 9</span>
<span class="co">#> .. ..$ x : num 142</span>
<span class="co">#> .. ..$ n : num 742</span>
<span class="co">#> .. ..$ SE :List of 2</span>
<span class="co">#> .. .. ..$ d: chr "uniform"</span>
<span class="co">#> .. .. ..$ p: num [1:2] 0.6 1</span>
<span class="co">#> .. ..$ SP :List of 2</span>
<span class="co">#> .. .. ..$ d: chr "uniform"</span>
<span class="co">#> .. .. ..$ p: num [1:2] 0.75 1</span>
<span class="co">#> .. ..$ prior : num [1:2] 1 1</span>
<span class="co">#> .. ..$ nchains: num 2</span>
<span class="co">#> .. ..$ burnin : num 10000</span>
<span class="co">#> .. ..$ update : num 10000</span>
<span class="co">#> .. ..$ inits : NULL</span>
<span class="co">#> ..@ model :Class 'prevModel' chr [1:7] "model {" "x ~ dbin(AP, n)" "AP <- SE * TP + (1 - SP) * (1 - TP)" "SE ~ dunif(0.6, 1)" ...</span>
<span class="co">#> ..@ mcmc :List of 3</span>
<span class="co">#> .. ..$ TP:List of 2</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.125 0.1236 0.1076 0.0944 0.133 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.111 0.0971 0.1033 0.1291 0.1516 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..- attr(*, "class")= chr "mcmc.list"</span>
<span class="co">#> .. ..$ SE:List of 2</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.673 0.696 0.788 0.693 0.629 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.825 0.834 0.88 0.806 0.835 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..- attr(*, "class")= chr "mcmc.list"</span>
<span class="co">#> .. ..$ SP:List of 2</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.877 0.87 0.868 0.863 0.861 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..$ :Class 'mcmc' atomic [1:10000] 0.884 0.875 0.917 0.89 0.927 ...</span>
<span class="co">#> .. .. .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> .. .. ..- attr(*, "class")= chr "mcmc.list"</span>
<span class="co">#> ..@ diagnostics:List of 2</span>
<span class="co">#> .. ..$ DIC:List of 3</span>
<span class="co">#> .. .. ..$ deviance:Class 'mcarray' Named num 7.58</span>
<span class="co">#> .. .. .. .. ..- attr(*, "names")= chr "x"</span>
<span class="co">#> .. .. ..$ penalty :Class 'mcarray' Named num 0.968</span>
<span class="co">#> .. .. .. .. ..- attr(*, "names")= chr "x"</span>
<span class="co">#> .. .. ..$ type : chr "pD"</span>
<span class="co">#> .. .. ..- attr(*, "class")= chr "dic"</span>
<span class="co">#> .. ..$ BGR:List of 2</span>
<span class="co">#> .. .. ..$ psrf : num [1:3, 1:2] 1 1 1 1 1 ...</span>
<span class="co">#> .. .. .. ..- attr(*, "dimnames")=List of 2</span>
<span class="co">#> .. .. .. .. ..$ : chr [1:3] "SE" "SP" "TP"</span>
<span class="co">#> .. .. .. .. ..$ : chr [1:2] "Point est." "Upper C.I."</span>
<span class="co">#> .. .. ..$ mpsrf: num 1</span>
<span class="co">#> .. .. ..- attr(*, "class")= chr "gelman.diag"</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Every slot can be accessed using the '@' operator
<span class="kw">slotNames</span>(TP)
<span class="co">#> [1] "par" "model" "mcmc" "diagnostics"</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## .. input parameters
TP@par
<span class="co">#> $x</span>
<span class="co">#> [1] 142</span>
<span class="co">#> </span>
<span class="co">#> $n</span>
<span class="co">#> [1] 742</span>
<span class="co">#> </span>
<span class="co">#> $SE</span>
<span class="co">#> $SE$d</span>
<span class="co">#> [1] "uniform"</span>
<span class="co">#> </span>
<span class="co">#> $SE$p</span>
<span class="co">#> [1] 0.6 1.0</span>
<span class="co">#> </span>
<span class="co">#> </span>
<span class="co">#> $SP</span>
<span class="co">#> $SP$d</span>
<span class="co">#> [1] "uniform"</span>
<span class="co">#> </span>
<span class="co">#> $SP$p</span>
<span class="co">#> [1] 0.75 1.00</span>
<span class="co">#> </span>
<span class="co">#> </span>
<span class="co">#> $prior</span>
<span class="co">#> [1] 1 1</span>
<span class="co">#> </span>
<span class="co">#> $nchains</span>
<span class="co">#> [1] 2</span>
<span class="co">#> </span>
<span class="co">#> $burnin</span>
<span class="co">#> [1] 10000</span>
<span class="co">#> </span>
<span class="co">#> $update</span>
<span class="co">#> [1] 10000</span>
<span class="co">#> </span>
<span class="co">#> $inits</span>
<span class="co">#> NULL</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## .. fitted model
TP@model
<span class="co">#> model {</span>
<span class="co">#> x ~ dbin(AP, n)</span>
<span class="co">#> AP <- SE * TP + (1 - SP) * (1 - TP)</span>
<span class="co">#> SE ~ dunif(0.6, 1)</span>
<span class="co">#> SP ~ dunif(0.75, 1)</span>
<span class="co">#> TP ~ dbeta(1, 1)</span>
<span class="co">#> }</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## .. simulated TP, SE, SP
<span class="kw">str</span>(TP@mcmc)
<span class="co">#> List of 3</span>
<span class="co">#> $ TP:List of 2</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.125 0.1236 0.1076 0.0944 0.133 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.111 0.0971 0.1033 0.1291 0.1516 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..- attr(*, "class")= chr "mcmc.list"</span>
<span class="co">#> $ SE:List of 2</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.673 0.696 0.788 0.693 0.629 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.825 0.834 0.88 0.806 0.835 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..- attr(*, "class")= chr "mcmc.list"</span>
<span class="co">#> $ SP:List of 2</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.877 0.87 0.868 0.863 0.861 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..$ :Class 'mcmc' atomic [1:10000] 0.884 0.875 0.917 0.89 0.927 ...</span>
<span class="co">#> .. .. ..- attr(*, "mcpar")= num [1:3] 11001 21000 1</span>
<span class="co">#> ..- attr(*, "class")= chr "mcmc.list"</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## .. convert simulated TP, SE, SP to matrix
<span class="kw">head</span>(<span class="kw">as.matrix</span>(TP))
<span class="co">#> TP SE SP</span>
<span class="co">#> [1,] 0.12497886 0.6729475 0.8774353</span>
<span class="co">#> [2,] 0.12357302 0.6959699 0.8699901</span>
<span class="co">#> [3,] 0.10761131 0.7880032 0.8683324</span>
<span class="co">#> [4,] 0.09438967 0.6927515 0.8632794</span>
<span class="co">#> [5,] 0.13304616 0.6286654 0.8607214</span>
<span class="co">#> [6,] 0.12531370 0.7360327 0.8846877</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## .. DIC and BGR (and bayesP)
TP@diagnostics
<span class="co">#> $DIC</span>
<span class="co">#> Mean deviance: 7.576 </span>
<span class="co">#> penalty 0.9684 </span>
<span class="co">#> Penalized deviance: 8.544 </span>
<span class="co">#> </span>
<span class="co">#> $BGR</span>
<span class="co">#> Potential scale reduction factors:</span>
<span class="co">#> </span>
<span class="co">#> Point est. Upper C.I.</span>
<span class="co">#> SE 1 1</span>
<span class="co">#> SP 1 1</span>
<span class="co">#> TP 1 1</span>
<span class="co">#> </span>
<span class="co">#> Multivariate psrf</span>
<span class="co">#> </span>
<span class="co">#> 1</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## TP@mcmc elements inherit from class 'mcmc.list' in coda package
## List all available methods for this class
<span class="kw">methods</span>(<span class="dt">class =</span> <span class="st">"mcmc.list"</span>)
<span class="co">#> [1] [ acfplot as.array as.matrix as.mcmc </span>
<span class="co">#> [6] autocorr.diag batchSE coerce densityplot end </span>
<span class="co">#> [11] head HPDinterval initialize plot qqmath </span>
<span class="co">#> [16] rejectionRate show slotsFromS3 start summary </span>
<span class="co">#> [21] tail thin time window xyplot </span>
<span class="co">#> see '?methods' for accessing help and source code</span></code></pre></div>
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