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<meta name="author" content="Catherine Hurley" />
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<meta name="date" content="2019-01-07" />
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<title>Clustering Graphics</title>
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<h1 class="title toc-ignore">Clustering Graphics</h1>
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<h4 class="author"><em>Catherine Hurley</em></h4>
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<h4 class="date"><em>2019-01-07</em></h4>
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<p>This package will order panels in scatterplot matrices and parallel coordinate displays by some merit index. The package contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level. For details on the methods used, consult “Clustering Visualisations of Multidimensional Data”, Journal of Computational and Graphical Statistics, vol. 13, (4), pp 788-806, 2004.</p>
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<div id="displaying-a-correlation-matrix" class="section level2">
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<h2>Displaying a correlation matrix</h2>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(gclus)
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<span class="co">#> Loading required package: cluster</span>
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<span class="kw">data</span>(longley)
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longley.cor <-<span class="st"> </span><span class="kw">cor</span>(longley)
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longley.color <-<span class="st"> </span><span class="kw">dmat.color</span>(longley.cor)</code></pre></div>
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<p><code>dmat.color</code> assigns three colours to the correlations according to the correlation magnitude. High correlations are in pink, the middle third are in blue, and the botom third are in yellow.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>))
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<span class="kw">plotcolors</span>(longley.color,<span class="dt">dlabels=</span><span class="kw">rownames</span>(longley.color))</code></pre></div>
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<p><img src="data:image/png;base64,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<p>If you want to change the colour scheme:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">longley.color <-<span class="st"> </span><span class="kw">dmat.color</span>(longley.cor, <span class="dt">byrank=</span><span class="ot">FALSE</span>)
|
|||
|
|
longley.color <-<span class="st"> </span><span class="kw">dmat.color</span>(longley.cor, <span class="dt">breaks=</span><span class="kw">c</span>(<span class="op">-</span><span class="dv">1</span>,<span class="dv">0</span>,.<span class="dv">5</span>,.<span class="dv">8</span>,<span class="dv">1</span>),
|
|||
|
|
<span class="kw">cm.colors</span>(<span class="dv">4</span>))</code></pre></div>
|
|||
|
|
<p>The plot is easier to interpret if variables are reorded prior to plotting.</p>
|
|||
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>))
|
|||
|
|
longley.o <-<span class="st"> </span><span class="kw">order.hclust</span>(longley.cor)
|
|||
|
|
longley.color1 <-<span class="st"> </span>longley.color[longley.o,longley.o]
|
|||
|
|
<span class="kw">plotcolors</span>(longley.color1,<span class="dt">dlabels=</span><span class="kw">rownames</span>(longley.color1))</code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
</div>
|
|||
|
|
<div id="displaying-a-pairs-plot-with-coloured-panels" class="section level2">
|
|||
|
|
<h2>Displaying a pairs plot with coloured panels</h2>
|
|||
|
|
<p><code>cpairs</code> is a version of <code>pairs</code> All the high-correlation panels appear together in a block.</p>
|
|||
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>))
|
|||
|
|
<span class="kw">cpairs</span>(longley, <span class="dt">order=</span> longley.o,<span class="dt">panel.color=</span> longley.color)</code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p>If the <code>order</code> is not supplied, then the variables are plotted in default dataset order.</p>
|
|||
|
|
</div>
|
|||
|
|
<div id="displaying-a-pcp-plot-with-coloured-panels" class="section level2">
|
|||
|
|
<h2>Displaying a PCP plot with coloured panels</h2>
|
|||
|
|
<p><code>cparcoord</code> is a versions of <code>`parcoord</code> where panels can be coloured. Again, the pink panels have high correlation, blue panels have middling correlation, and yellow panels have low correlation.</p>
|
|||
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">cparcoord</span>(longley, <span class="dt">order=</span> longley.o,<span class="dt">panel.color=</span> longley.color,
|
|||
|
|
<span class="dt">horizontal=</span><span class="ot">TRUE</span>, <span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">4</span>,<span class="dv">1</span>,<span class="dv">1</span>))</code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
</div>
|
|||
|
|
<div id="plotting-re-ordered-dendrograms." class="section level2">
|
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|
|
<h2>Plotting re-ordered dendrograms.</h2>
|
|||
|
|
<p><code>eurodist</code> is a built-in distance matrix giving the distance between European cities.</p>
|
|||
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>))
|
|||
|
|
<span class="kw">data</span>(eurodist)
|
|||
|
|
dis <-<span class="st"> </span><span class="kw">as.dist</span>(eurodist)
|
|||
|
|
hc <-<span class="st"> </span><span class="kw">hclust</span>(dis, <span class="st">"ave"</span>)
|
|||
|
|
<span class="kw">plot</span>(hc)</code></pre></div>
|
|||
|
|
<p><img src="data:image/png;base64,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
|
|||
|
|
<p><code>order.hclust</code> re-orders a dendrogram to improve the similarity between nearby leaves. Applying it to the <code>hc</code> object:</p>
|
|||
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>))
|
|||
|
|
hc1 <-<span class="st"> </span><span class="kw">reorder.hclust</span>(hc, dis)
|
|||
|
|
<span class="kw">plot</span>(hc1)</code></pre></div>
|
|||
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<p><img src="data:image/png;base64,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
|
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<p>Both dendrograms correspond to the same tree structure, but the second one shows that Paris is closer to Cherbourg than Munich, and Rome is closer to Gibralter than to Barcelona.</p>
|
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<p>We can also compare both orderings with an image plot of the colors. The second ordering seems to place nearby cities closer to each other.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
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<span class="kw">layout</span>(<span class="kw">matrix</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">2</span>,<span class="dt">nrow=</span><span class="dv">1</span>,<span class="dt">ncol=</span><span class="dv">2</span>))
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<span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">6</span>,<span class="dv">1</span>,<span class="dv">1</span>))
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cmat <-<span class="st"> </span><span class="kw">dmat.color</span>(eurodist, <span class="kw">rev</span>(<span class="kw">cm.colors</span>(<span class="dv">5</span>)))
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<span class="kw">plotcolors</span>(cmat[hc<span class="op">$</span>order,hc<span class="op">$</span>order], <span class="dt">rlabels=</span><span class="kw">labels</span>(eurodist)[hc<span class="op">$</span>order])
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<span class="kw">plotcolors</span>(cmat[hc1<span class="op">$</span>order,hc1<span class="op">$</span>order], <span class="dt">rlabels=</span><span class="kw">labels</span>(eurodist)[hc1<span class="op">$</span>order])</code></pre></div>
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<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwAAAAFQCAYAAAAIkBlQAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7J0HfFVF9sdPAKnSEfBPERYQpfcSwQWkxazSBKQIRPjAIiAuCIiwqAgCi/gBLBRF0AURkd6SCBhYpEtVOiy9SEdwRQj88zt6nzeP95I3Ife9l+Q3nw/v3jv3zNy53xumnDkzJ+RuXBAGEiABEiABEiABEiABEiCBNEEgXZp4S74kCZAACZAACZAACZAACZCAEuAAgH8IJEACJEACJEACJEACJJCGCHAAkIY+Nl+VBEiABEiABEiABEiABDgA4N8ACZAACZAACZAACZAACaQhAhwApKGPzVclARIgARIgARIgARIgAQ4A+DdAAiRAAiRAAiRAAiRAAmmIAAcAaehj81VJgARIgARIgARIgARIgAMA/g2QAAmQAAmQAAmQAAmQQBoiwAFAGvrYfFUSIAESIAESIAESIAES4ACAfwMkQAIkQAIkQAIkQAIkkIYIcACQhj42X5UESIAESIAESIAESIAEOADg3wAJkAAJkAAJkAAJkAAJpCECHACkoY/NVyUBEiABEiABEiABEiABDgD4N0ACJEACJEACJEACJEACaYgABwBp6GPzVUmABEiABEiABEiABEiAAwD+DZAACZAACZAACZAACZBAGiLAAUAa+th8VRIgARIgARIgARIgARLIQASph8BPP/0kv/32W+p5Ib4JCZBAPAIZM2aU/Pnzx4vjBQmAAOt//h2QQOomkNz1PwcAqeTvBZX/N998I4UKFfL5je5e8FlUBf+bz0ye0iSQFAK5kpLIIM0VA9lgE8106pQ0atSIg4Bg+zABLg/r/wB/AIcfzzrRYcApJPvkrv85AEghHz6xYkLz/3//939Sr169xERd9+/Md536dLK0nk9iFCKB+yLQ4L5SJ574x8RFglaibEwMZ/mC9usErmCs/wPH3h9PZp3oD8rB/4zkrv+5BiD4vzlLSAIkQAIkQAIkQAIkQALJRoADgGRDyYxIgARIgARIgARIgARIIPgJcADg9o1GjRolzz33XLzYJUuW6AKrBQsWyEcffRTvHi9IgARIgARSBwHW/6njO/ItSIAEEifAAYCNUWxsrMyfP19w/PHHPy2FJ02aJL/88otcv35drl69Knfu3FEZW1I9vX37tnuU/O9//4sXh7Q3btyIF8cLEiABEiCBwBJg/R9Y/nw6CZCAfwlwAGDjHR0dLVWrVpX27dvL5MmT9c6mTZtkx44d0q9fP72GTM+ePaVMmTISFRXlimvVqpXOHAwfPlzjunfvLm3bttW4cuXKyfHjxwV5hYaGSqdOnaRx48Zy69YtleUPCZAACZBAYAmw/g8sfz6dBEjAvwQ4ALDx/vTTT6Vz587y7LPPyrJly1RTX7NmTalUqZK89957KoltNqdMmaKmQF988YXGjR49Wj777DNZuHChHD58WE6cOKEzBBhMIB8MBCIjIwUNTNOmTWXWrFkycOBAnVWwPZ6nJEACJEACASLA+j9A4PlYEiCBgBDgNqB/YL9w4YJq9C9evCghISFqujN79mzp1q1bvA/z+OOP63XhwoXl2rVrcuXKFdm5c6d06NBB42EGtGfPHj2Hth8hb968cvPmTendu7cMHTpUypcvL9WqVdPZBhXgDwmQAAmQQMAIsP4PGHo+mARIIEAEOAPwB/iZM2fKq6++KqtXr5ZVq1ap5h62/wjp06dXu3+cp0sXH1muXLmkZMmSgvSLFi0SmAKVKlUKoppOT/74iYnbwxvmQ/v375ds2bLJ8uXL7bd5TgIkQAIkEAACrP8DAJ2PJAESCCgBzgD8gX/69OmCXX6sUKVKFe30b968WU2AWrduLb169bJuxzsOGTJE2rVrp536Bx54QCIiIuLdty4wUOjTp48ULFhQzp49KyNHjrRu8UgCJEACJBAgAqz/AwSejyUBEggYgZC7cSFgT09BD4YJT6ZMmRIsMXb8yZIlS4IyuAnToRw5ciQqZyJw8uRJOXjwoNSvX9/nZKaegAe09DlrCpJAkgk0SHJK3xKu9k0sKKXgCbJxnCIBJogM/iPA+l+E9b///t7cn8Q60Z1I2rxO7vo/vj1L2mTq01sn1vlHJr50/iGX3J1/5MlAAiRAAiTgDAHW/85wZa4kQAKBI8ABQODY88kkQAIkQAIkQAIkQAIk4HcCXAPgd+QOPvCiiIlZTzpDk55xhkVfZigfTKYZTk+5GqKRcNMEhvKm38owe6Pym/wNoxymf8dh881Kf3eJmXzIM2byJtJr7sRJlzRJQdk0Q8Dh+t9pjqZ1rmmdaFqvOPm+TtcppnWiKUtTNk62L6Zld7IsplwgH8h+D2cAkvLFmIYESIAESIAESIAESIAEUigBDgBS6IdjsUmABEiABEiABEiABEggKQQ4AEgKNaYhARIgARIgARIgARIggRRKgGsAEvhwcOj18MMPq8StW7ekRo0aMnbsWMmYMWMCqXiLBEiABEggpRNg/Z/SvyDLTwIkkBABzgAkQOfnn3+WtWvX6j94CD527Jh89dVXrhS3b992ndtPfv31V9cl9o92D97SucvxmgRIgARIIDAEWP8HhjufSgIk4B8CnAHwkXPmzJl1n39rD/8RI0bIunXrJCQkRLp06SJt
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