predict.html

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<html><head><title>R: Model Predictions</title>
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<table width="100%" summary="page for predict {arules}"><tr><td>predict {arules}</td><td align="right">R Documentation</td></tr></table>
<h2>Model Predictions</h2>


<h3>Description</h3>

<p>
Provides the S4 method <code>predict</code> for <code>itemMatrix</code> (e.g.,
transactions).  Predicts the membership (nearest neighbor) of new data to
clusters represented by medoids or labeled examples.
</p>


<h3>Usage</h3>

<pre>
## S4 method for signature 'itemMatrix':
predict(object, newdata, labels = NULL, blocksize = 200,...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
medoids (no labels needed) or examples (labels needed). </td></tr>
<tr valign="top"><td><code>newdata</code></td>
<td>
objects to predict labels for. </td></tr>
<tr valign="top"><td><code>labels</code></td>
<td>
an integer vector containing the labels for the examples in
<code>object</code>. </td></tr>
<tr valign="top"><td><code>blocksize</code></td>
<td>
a numeric scalar indicating how much memory predict can
use for big <code>x</code> and/or <code>y</code> (approx. in MB). This is only a crude
approximation for 32-bit machines (64-bit architectures need double the
blocksize in memory) and using the default Jaccard method for dissimilarity
calculation.  In general, reducing <code>blocksize</code> will decrease the
memory usage but will increase the run-time.</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments passed on to <code>dissimilarity</code>. E.g., 
<code>method</code>.</td></tr>
</table>

<h3>Value</h3>

<p>
An integer vector of the same length as <code>newdata</code>  
containing the predicted labels for each element.</p>

<h3>See Also</h3>

<p>
<code><a href="dissimilarity.html">dissimilarity</a></code>,
<code><a href="itemMatrix-class.html">itemMatrix-class</a></code>
</p>


<h3>Examples</h3>

<pre>
data("Adult")

## sample
small &lt;- sample(Adult, 500)
large &lt;- sample(Adult, 5000)

## cluster a small sample
d_jaccard &lt;- dissimilarity(small)
hc &lt;- hclust(d_jaccard)
l &lt;-  cutree(hc, k=4)

## predict labels for a larger sample
labels &lt;- predict(small, large, l)

## plot the profile of the 1. cluster
itemFrequencyPlot(large[labels==1, itemFrequency(large) &gt; 0.1])
</pre>



<hr><div align="center">[Package <em>arules</em> version 0.6-6 <a href="00Index.html">Index]</a></div>

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