EVOLUTION-MANAGER
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Agglomerative Nesting (AGNES) Object</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for agnes.object {cluster}"><tr><td>agnes.object {cluster}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Agglomerative Nesting (AGNES) Object</h2> <h3>Description</h3> <p>The objects of class <code>"agnes"</code> represent an agglomerative hierarchical clustering of a dataset. </p> <h3>Value</h3> <p>A legitimate <code>agnes</code> object is a list with the following components: </p> <table summary="R valueblock"> <tr valign="top"><td><code>order</code></td> <td> <p>a vector giving a permutation of the original observations to allow for plotting, in the sense that the branches of a clustering tree will not cross.</p> </td></tr> <tr valign="top"><td><code>order.lab</code></td> <td> <p>a vector similar to <code>order</code>, but containing observation labels instead of observation numbers. This component is only available if the original observations were labelled. </p> </td></tr> <tr valign="top"><td><code>height</code></td> <td> <p>a vector with the distances between merging clusters at the successive stages. </p> </td></tr> <tr valign="top"><td><code>ac</code></td> <td> <p>the agglomerative coefficient, measuring the clustering structure of the dataset. </p> <p>For each observation i, denote by m(i) its dissimilarity to the first cluster it is merged with, divided by the dissimilarity of the merger in the final step of the algorithm. The <code>ac</code> is the average of all 1 - m(i). It can also be seen as the average width (or the percentage filled) of the banner plot. Because <code>ac</code> grows with the number of observations, this measure should not be used to compare datasets of very different sizes. </p> </td></tr> <tr valign="top"><td><code>merge</code></td> <td> <p>an (n-1) by 2 matrix, where n is the number of observations. Row i of <code>merge</code> describes the merging of clusters at step i of the clustering. If a number j in the row is negative, then the single observation |j| is merged at this stage. If j is positive, then the merger is with the cluster formed at stage j of the algorithm. </p> </td></tr> <tr valign="top"><td><code>diss</code></td> <td> <p>an object of class <code>"dissimilarity"</code> (see <code><a href="dissimilarity.object.html">dissimilarity.object</a></code>), representing the total dissimilarity matrix of the dataset. </p> </td></tr> <tr valign="top"><td><code>data</code></td> <td> <p>a matrix containing the original or standardized measurements, depending on the <code>stand</code> option of the function <code>agnes</code>. If a dissimilarity matrix was given as input structure, then this component is not available. </p> </td></tr> </table> <h3>GENERATION</h3> <p>This class of objects is returned from <code><a href="agnes.html">agnes</a></code>. </p> <h3>METHODS</h3> <p>The <code>"agnes"</code> class has methods for the following generic functions: <code>print</code>, <code>summary</code>, <code>plot</code>, and <code><a href="../../stats/html/dendrogram.html">as.dendrogram</a></code>. </p> <p>In addition, <code><a href="../../stats/html/cutree.html">cutree</a>(x, *)</code> can be used to “cut” the dendrogram in order to produce cluster assignments. </p> <h3>INHERITANCE</h3> <p>The class <code>"agnes"</code> inherits from <code>"twins"</code>. Therefore, the generic functions <code><a href="pltree.html">pltree</a></code> and <code><a href="../../stats/html/as.hclust.html">as.hclust</a></code> are available for <code>agnes</code> objects. After applying <code>as.hclust()</code>, all <em>its</em> methods are available, of course. </p> <h3>See Also</h3> <p><code><a href="agnes.html">agnes</a></code>, <code><a href="diana.html">diana</a></code>, <code><a href="../../stats/html/as.hclust.html">as.hclust</a></code>, <code><a href="../../stats/html/hclust.html">hclust</a></code>, <code><a href="plot.agnes.html">plot.agnes</a></code>, <code><a href="twins.object.html">twins.object</a></code>. </p> <p><code><a href="../../stats/html/cutree.html">cutree</a></code>. </p> <h3>Examples</h3> <pre> data(agriculture) ag.ag <- agnes(agriculture) class(ag.ag) pltree(ag.ag) # the dendrogram ## cut the dendrogram -> get cluster assignments: (ck3 <- cutree(ag.ag, k = 3)) (ch6 <- cutree(as.hclust(ag.ag), h = 6)) stopifnot(identical(unname(ch6), ck3)) </pre> <hr /><div style="text-align: center;">[Package <em>cluster</em> version 2.0.8 <a href="00Index.html">Index</a>]</div> </body></html>