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Edit File: print.igraphHRG.html
<!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: Print a hierarchical random graph model to the screen</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 print.igraphHRG {igraph}"><tr><td>print.igraphHRG {igraph}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Print a hierarchical random graph model to the screen</h2> <h3>Description</h3> <p><code>igraphHRG</code> objects can be printed to the screen in two forms: as a tree or as a list, depending on the <code>type</code> argument of the print function. By default the <code>auto</code> type is used, which selects <code>tree</code> for small graphs and <code>simple</code> (=list) for bigger ones. The <code>tree</code> format looks like this: </p> <pre>Hierarchical random graph, at level 3: g1 p= 0 '- g15 p=0.33 1 '- g13 p=0.88 6 3 9 4 2 10 7 5 8 '- g8 p= 0.5 '- g16 p= 0.2 20 14 17 19 11 15 16 13 '- g5 p= 0 12 18 </pre> <p>This is a graph with 20 vertices, and the top three levels of the fitted hierarchical random graph are printed. The root node of the HRG is always vertex group #1 (‘<code>g1</code>’ in the the printout). Vertex pairs in the left subtree of <code>g1</code> connect to vertices in the right subtree with probability zero, according to the fitted model. <code>g1</code> has two subgroups, <code>g15</code> and <code>g8</code>. <code>g15</code> has a subgroup of a single vertex (vertex 1), and another larger subgroup that contains vertices 6, 3, etc. on lower levels, etc. The <code>plain</code> printing is simpler and faster to produce, but less visual: </p> <pre>Hierarchical random graph: g1 p=0.0 -> g12 g10 g2 p=1.0 -> 7 10 g3 p=1.0 -> g18 14 g4 p=1.0 -> g17 15 g5 p=0.4 -> g15 17 g6 p=0.0 -> 1 4 g7 p=1.0 -> 11 16 g8 p=0.1 -> g9 3 g9 p=0.3 -> g11 g16 g10 p=0.2 -> g4 g5 g11 p=1.0 -> g6 5 g12 p=0.8 -> g8 8 g13 p=0.0 -> g14 9 g14 p=1.0 -> 2 6 g15 p=0.2 -> g19 18 g16 p=1.0 -> g13 g2 g17 p=0.5 -> g7 13 g18 p=1.0 -> 12 19 g19 p=0.7 -> g3 20</pre> <p>It lists the two subgroups of each internal node, in as many columns as the screen width allows. </p> <h3>Usage</h3> <pre> ## S3 method for class 'igraphHRG' print(x, type = c("auto", "tree", "plain"), level = 3, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p><code>igraphHRG</code> object to print.</p> </td></tr> <tr valign="top"><td><code>type</code></td> <td> <p>How to print the dendrogram, see details below.</p> </td></tr> <tr valign="top"><td><code>level</code></td> <td> <p>The number of top levels to print from the dendrogram.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Additional arguments, not used currently.</p> </td></tr> </table> <h3>Value</h3> <p>The hierarchical random graph model itself, invisibly. </p> <h3>See Also</h3> <p>Other hierarchical random graph functions: <code><a href="consensus_tree.html">consensus_tree</a>()</code>, <code><a href="fit_hrg.html">fit_hrg</a>()</code>, <code><a href="hrg-methods.html">hrg-methods</a></code>, <code><a href="hrg_tree.html">hrg_tree</a>()</code>, <code><a href="hrg.html">hrg</a>()</code>, <code><a href="predict_edges.html">predict_edges</a>()</code>, <code><a href="print.igraphHRGConsensus.html">print.igraphHRGConsensus</a>()</code>, <code><a href="sample_hrg.html">sample_hrg</a>()</code> </p> <hr /><div style="text-align: center;">[Package <em>igraph</em> version 1.3.5 <a href="00Index.html">Index</a>]</div> </body></html>