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<!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <meta name="generator" content="pandoc" /> <meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="author" content="Diego Hernangomez" /> <meta name="date" content="2020-04-05" /> <title>Head/Tails breaks on the classInt package.</title> <style type="text/css">code{white-space: pre;}</style> <style type="text/css" data-origin="pandoc"> a.sourceLine { display: inline-block; line-height: 1.25; } a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; } a.sourceLine:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode { white-space: pre; position: relative; } div.sourceCode { margin: 1em 0; } pre.sourceCode { margin: 0; } @media screen { div.sourceCode { overflow: auto; } } @media print { code.sourceCode { white-space: pre-wrap; } a.sourceLine { text-indent: -1em; padding-left: 1em; } } pre.numberSource a.sourceLine { position: relative; 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} p { margin: 0.5em 0; } blockquote { background-color: #f6f6f6; padding: 0.25em 0.75em; } hr { border-style: solid; border: none; border-top: 1px solid #777; margin: 28px 0; } dl { margin-left: 0; } dl dd { margin-bottom: 13px; margin-left: 13px; } dl dt { font-weight: bold; } ul { margin-top: 0; } ul li { list-style: circle outside; } ul ul { margin-bottom: 0; } pre, code { background-color: #f7f7f7; border-radius: 3px; color: #333; white-space: pre-wrap; } pre { border-radius: 3px; margin: 5px 0px 10px 0px; padding: 10px; } pre:not([class]) { background-color: #f7f7f7; } code { font-family: Consolas, Monaco, 'Courier New', monospace; font-size: 85%; } p > code, li > code { padding: 2px 0px; } div.figure { text-align: center; } img { background-color: #FFFFFF; padding: 2px; border: 1px solid #DDDDDD; border-radius: 3px; border: 1px solid #CCCCCC; margin: 0 5px; } h1 { margin-top: 0; font-size: 35px; line-height: 40px; } h2 { border-bottom: 4px solid #f7f7f7; padding-top: 10px; padding-bottom: 2px; font-size: 145%; } h3 { border-bottom: 2px solid #f7f7f7; padding-top: 10px; font-size: 120%; } h4 { border-bottom: 1px solid #f7f7f7; margin-left: 8px; font-size: 105%; } h5, h6 { border-bottom: 1px solid #ccc; font-size: 105%; } a { color: #0033dd; text-decoration: none; } a:hover { color: #6666ff; } a:visited { color: #800080; } a:visited:hover { color: #BB00BB; } a[href^="http:"] { text-decoration: underline; } a[href^="https:"] { text-decoration: underline; } code > span.kw { color: #555; font-weight: bold; } code > span.dt { color: #902000; } code > span.dv { color: #40a070; } code > span.bn { color: #d14; } code > span.fl { color: #d14; } code > span.ch { color: #d14; } code > span.st { color: #d14; } code > span.co { color: #888888; font-style: italic; } code > span.ot { color: #007020; } code > span.al { color: #ff0000; font-weight: bold; } code > span.fu { color: #900; font-weight: bold; } code > span.er { color: #a61717; background-color: #e3d2d2; } </style> </head> <body> <h1 class="title toc-ignore">Head/Tails breaks on the <code>classInt</code> package.</h1> <h4 class="author">Diego Hernangomez</h4> <h4 class="date">2020-04-05</h4> <div id="TOC"> <ul> <li><a href="#abstract">Abstract</a></li> <li><a href="#introduction">Introduction</a></li> <li><a href="#breaking-method">Breaking method</a></li> <li><a href="#step-by-step-example">Step by step example</a></li> <li><a href="#implementation-on-classint-package">Implementation on <code>classInt</code> package</a></li> <li><a href="#case-study">Case study</a></li> <li><a href="#testing-and-benchmark">Testing and benchmark</a></li> <li><a href="#references">References</a></li> </ul> </div> <blockquote> <em>There are far more ordinary people (say, 80 percent) than extraordinary people (say, 20 percent); this is often characterized by the 80/20 principle, based on the observation made by the Italian economist Vilfredo Pareto in 1906 that 80% of land in Italy was owned by 20% of the population. A histogram of the data values for these phenomena would reveal a right-skewed or heavy-tailed distribution. How to map the data with the heavy-tailed distribution?</em> <div style="text-align: right"> <span class="citation">Jiang (2013)</span> </div> </blockquote> <div id="abstract" class="section level1"> <h1>Abstract</h1> <p>This vignette discusses the implementation of the “Head/tail breaks” style (<span class="citation">Jiang (2013)</span>) on the <code>classIntervals</code> function. A step-by-step example is presented in order to clarify the method. A case study using <code>spData::afcon</code> is also included, as well as a test suite checking the performance and validation of the implementation.</p> </div> <div id="introduction" class="section level1"> <h1>Introduction</h1> <p>The <strong>Head/tail breaks</strong>, sometimes referred as <strong>ht-index</strong> (<span class="citation">Jiang and Yin (2013)</span>), is a classification scheme introduced by <span class="citation">Jiang (2013)</span> in order to find groupings or hierarchy for data with a heavy-tailed distribution.</p> <p>Heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail. This imbalance between the head and tail, or between many small values and a few large values, can be expressed as <em>“far more small things than large things”</em>.</p> <p>Heavy tailed distributions are commonly characterized by a power law, a lognormal or an exponential function. Nature, society, finance (<span class="citation">Vasicek (2002)</span>) and our daily lives are full of rare and extreme events, which are termed “black swan events” (<span class="citation">Taleb (2008)</span>). This line of thinking provides a good reason to reverse our thinking by focusing on low-frequency events.</p> <div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(classInt)</a> <a class="sourceLine" id="cb1-2" title="2"></a> <a class="sourceLine" id="cb1-3" title="3"><span class="co">#1. Characterization of heavy-tail distributions----</span></a> <a class="sourceLine" id="cb1-4" title="4"><span class="kw">set.seed</span>(<span class="dv">1234</span>)</a> <a class="sourceLine" id="cb1-5" title="5"><span class="co">#Pareto distribution a=1 b=1.161 n=1000</span></a> <a class="sourceLine" id="cb1-6" title="6">sample_par <-<span class="st"> </span><span class="dv">1</span> <span class="op">/</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">-</span><span class="st"> </span><span class="kw">runif</span>(<span class="dv">1000</span>)) <span class="op">^</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">/</span><span class="st"> </span><span class="fl">1.161</span>)</a> <a class="sourceLine" id="cb1-7" title="7">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb1-8" title="8"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">3</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb1-9" title="9"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb1-10" title="10"> <span class="kw">sort</span>(sample_par, <span class="dt">decreasing =</span> <span class="ot">TRUE</span>),</a> <a class="sourceLine" id="cb1-11" title="11"> <span class="dt">type =</span> <span class="st">"l"</span>,</a> <a class="sourceLine" id="cb1-12" title="12"> <span class="dt">ylab =</span> <span class="st">"F(x)"</span>,</a> <a class="sourceLine" id="cb1-13" title="13"> <span class="dt">xlab =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb1-14" title="14"> <span class="dt">main =</span> <span class="st">"80/20 principle"</span></a> <a class="sourceLine" id="cb1-15" title="15">)</a> <a class="sourceLine" id="cb1-16" title="16"><span class="kw">abline</span>(<span class="dt">h =</span> <span class="kw">quantile</span>(sample_par, <span class="fl">.8</span>) ,</a> <a class="sourceLine" id="cb1-17" title="17"> <span class="dt">lty =</span> <span class="dv">2</span>,</a> <a class="sourceLine" id="cb1-18" title="18"> <span class="dt">col =</span> <span class="st">"red3"</span>)</a> <a class="sourceLine" id="cb1-19" title="19"><span class="kw">abline</span>(<span class="dt">v =</span> <span class="fl">0.2</span><span class="op">*</span><span class="kw">length</span>(sample_par) ,</a> <a class="sourceLine" id="cb1-20" title="20"> <span class="dt">lty =</span> <span class="dv">2</span>,</a> <a class="sourceLine" id="cb1-21" title="21"> <span class="dt">col =</span> <span class="st">"darkblue"</span>)</a> <a class="sourceLine" id="cb1-22" title="22"><span class="kw">legend</span>(</a> <a class="sourceLine" id="cb1-23" title="23"> <span class="st">"topleft"</span>,</a> <a class="sourceLine" id="cb1-24" title="24"> <span class="dt">legend =</span> <span class="kw">c</span>(<span class="st">"F(x): p80"</span>, <span class="st">"x: Top 20%"</span>),</a> <a class="sourceLine" id="cb1-25" title="25"> <span class="dt">col =</span> <span class="kw">c</span>(<span class="st">"red3"</span>, <span class="st">"darkblue"</span>),</a> <a class="sourceLine" id="cb1-26" title="26"> <span class="dt">lty =</span> <span class="dv">2</span>,</a> <a class="sourceLine" id="cb1-27" title="27"> <span class="dt">cex =</span> <span class="fl">0.8</span></a> <a class="sourceLine" id="cb1-28" title="28">)</a> <a class="sourceLine" id="cb1-29" title="29"></a> <a class="sourceLine" id="cb1-30" title="30"><span class="kw">hist</span>(</a> <a class="sourceLine" id="cb1-31" title="31"> sample_par,</a> <a class="sourceLine" id="cb1-32" title="32"> <span class="dt">n =</span> <span class="dv">100</span>,</a> <a class="sourceLine" id="cb1-33" title="33"> <span class="dt">xlab =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb1-34" title="34"> <span class="dt">main =</span> <span class="st">"Histogram"</span>,</a> <a class="sourceLine" id="cb1-35" title="35"> <span class="dt">col =</span> <span class="st">"grey50"</span>,</a> <a class="sourceLine" id="cb1-36" title="36"> <span class="dt">border =</span> <span class="ot">NA</span>, </a> <a class="sourceLine" id="cb1-37" title="37"> <span class="dt">probability =</span> <span class="ot">TRUE</span></a> <a class="sourceLine" id="cb1-38" title="38">)</a> <a class="sourceLine" id="cb1-39" title="39"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img 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" /><img 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/></p> </div> <div id="breaking-method" class="section level1"> <h1>Breaking method</h1> <p>The method itself consists on a four-step process performed recursively until a stopping condition is satisfied. Given a vector of values <code>var</code> the process can be described as follows:</p> <ol style="list-style-type: decimal"> <li>Compute <code>mu = mean(var)</code>.</li> <li>Break <code>var</code> into the <code>tail</code> (as <code>var < mu</code>) and the <code>head</code> (as <code>var > mu</code>).</li> <li>Assess if the proportion of <code>head</code> over <code>var</code> is lower or equal than a given threshold (i.e. <code>length(head)/length(var) <= thr</code>)</li> <li>If 3 is <code>TRUE</code>, repeat 1 to 3 until the condition is <code>FALSE</code> or no more partitions are possible (i.e. <code>head</code> has less than two elements expressed as <code>length(head) < 2</code>).</li> </ol> <p>It is important to note that, at the beginning of a new iteration, <code>var</code> is replaced by <code>head</code>. The underlying hypothesis is to create partitions until the head and the tail are balanced in terms of distribution.So the stopping criteria is satisfied when the last head and the last tail are evenly balanced.</p> <p>In terms of threshold, <span class="citation">Jiang, Liu, and Jia (2013)</span> set 40% as a good approximation, meaning that if the head contains more than 40% of the observations the distribution is not considered heavy-tailed.</p> <p>The final breaks are the vector of consecutive <code>mu</code>.</p> </div> <div id="step-by-step-example" class="section level1"> <h1>Step by step example</h1> <p>We reproduce here the pseudo-code<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> as per <span class="citation">Jiang (2019)</span>:</p> <pre><code>Recursive function Head/tail Breaks: Rank the input data from the largest to the smallest Break the data into the head and the tail around the mean; // the head for those above the mean // the tail for those below the mean While (head <= 40%): Head/tail Breaks (head); End Function</code></pre> <p>A step-by-step example in <strong>R</strong> (for illustrative purposes) has been developed:</p> <div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"></a> <a class="sourceLine" id="cb3-2" title="2">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb3-3" title="3"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb3-4" title="4">var <-<span class="st"> </span>sample_par</a> <a class="sourceLine" id="cb3-5" title="5">thr <-<span class="st"> </span><span class="fl">.4</span></a> <a class="sourceLine" id="cb3-6" title="6">brks <-<span class="st"> </span><span class="kw">c</span>(<span class="kw">min</span>(var), <span class="kw">max</span>(var)) <span class="co">#Initialise with min and max</span></a> <a class="sourceLine" id="cb3-7" title="7"></a> <a class="sourceLine" id="cb3-8" title="8">sum_table <-<span class="st"> </span><span class="kw">data.frame</span>(</a> <a class="sourceLine" id="cb3-9" title="9"> <span class="dt">iter =</span> <span class="dv">0</span>,</a> <a class="sourceLine" id="cb3-10" title="10"> <span class="dt">mu =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb3-11" title="11"> <span class="dt">prop =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb3-12" title="12"> <span class="dt">n_var =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb3-13" title="13"> <span class="dt">n_head =</span> <span class="ot">NA</span></a> <a class="sourceLine" id="cb3-14" title="14">)</a> <a class="sourceLine" id="cb3-15" title="15"><span class="co">#Pars for chart</span></a> <a class="sourceLine" id="cb3-16" title="16">limchart <-<span class="st"> </span>brks</a> <a class="sourceLine" id="cb3-17" title="17"><span class="co">#Iteration</span></a> <a class="sourceLine" id="cb3-18" title="18"><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">10</span>) {</a> <a class="sourceLine" id="cb3-19" title="19"> mu <-<span class="st"> </span><span class="kw">mean</span>(var)</a> <a class="sourceLine" id="cb3-20" title="20"> brks <-<span class="st"> </span><span class="kw">sort</span>(<span class="kw">c</span>(brks, mu))</a> <a class="sourceLine" id="cb3-21" title="21"> head <-<span class="st"> </span>var[var <span class="op">></span><span class="st"> </span>mu]</a> <a class="sourceLine" id="cb3-22" title="22"> prop <-<span class="st"> </span><span class="kw">length</span>(head) <span class="op">/</span><span class="st"> </span><span class="kw">length</span>(var)</a> <a class="sourceLine" id="cb3-23" title="23"> stopit <-<span class="st"> </span>prop <span class="op"><</span><span class="st"> </span>thr <span class="op">&</span><span class="st"> </span><span class="kw">length</span>(head) <span class="op">></span><span class="st"> </span><span class="dv">1</span></a> <a class="sourceLine" id="cb3-24" title="24"> sum_table =<span class="st"> </span><span class="kw">rbind</span>(sum_table,</a> <a class="sourceLine" id="cb3-25" title="25"> <span class="kw">c</span>(i, mu, prop, <span class="kw">length</span>(var), <span class="kw">length</span>(head)))</a> <a class="sourceLine" id="cb3-26" title="26"> <span class="kw">hist</span>(</a> <a class="sourceLine" id="cb3-27" title="27"> var,</a> <a class="sourceLine" id="cb3-28" title="28"> <span class="dt">main =</span> <span class="kw">paste0</span>(<span class="st">"Iter "</span>, i),</a> <a class="sourceLine" id="cb3-29" title="29"> <span class="dt">breaks =</span> <span class="dv">50</span>,</a> <a class="sourceLine" id="cb3-30" title="30"> <span class="dt">col =</span> <span class="st">"grey50"</span>,</a> <a class="sourceLine" id="cb3-31" title="31"> <span class="dt">border =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb3-32" title="32"> <span class="dt">xlab =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb3-33" title="33"> <span class="dt">xlim =</span> limchart</a> <a class="sourceLine" id="cb3-34" title="34"> )</a> <a class="sourceLine" id="cb3-35" title="35"> <span class="kw">abline</span>(<span class="dt">v =</span> mu, <span class="dt">col =</span> <span class="st">"red3"</span>, <span class="dt">lty =</span> <span class="dv">2</span>)</a> <a class="sourceLine" id="cb3-36" title="36"> ylabel <-<span class="st"> </span><span class="kw">max</span>(<span class="kw">hist</span>(var, <span class="dt">breaks =</span> <span class="dv">50</span>, <span class="dt">plot =</span> <span class="ot">FALSE</span>)<span class="op">$</span>counts)</a> <a class="sourceLine" id="cb3-37" title="37"> labelplot <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"PropHead: "</span>, <span class="kw">round</span>(prop <span class="op">*</span><span class="st"> </span><span class="dv">100</span>, <span class="dv">2</span>), <span class="st">"%"</span>)</a> <a class="sourceLine" id="cb3-38" title="38"> <span class="kw">text</span>(</a> <a class="sourceLine" id="cb3-39" title="39"> <span class="dt">x =</span> mu,</a> <a class="sourceLine" id="cb3-40" title="40"> <span class="dt">y =</span> ylabel,</a> <a class="sourceLine" id="cb3-41" title="41"> <span class="dt">labels =</span> labelplot,</a> <a class="sourceLine" id="cb3-42" title="42"> <span class="dt">cex =</span> <span class="fl">0.8</span>,</a> <a class="sourceLine" id="cb3-43" title="43"> <span class="dt">pos =</span> <span class="dv">4</span></a> <a class="sourceLine" id="cb3-44" title="44"> )</a> <a class="sourceLine" id="cb3-45" title="45"> <span class="kw">legend</span>(</a> <a class="sourceLine" id="cb3-46" title="46"> <span class="st">"right"</span>,</a> <a class="sourceLine" id="cb3-47" title="47"> <span class="dt">legend =</span> <span class="kw">paste0</span>(<span class="st">"mu"</span>, i),</a> <a class="sourceLine" id="cb3-48" title="48"> <span class="dt">col =</span> <span class="kw">c</span>(<span class="st">"red3"</span>),</a> <a class="sourceLine" id="cb3-49" title="49"> <span class="dt">lty =</span> <span class="dv">2</span>,</a> <a class="sourceLine" id="cb3-50" title="50"> <span class="dt">cex =</span> <span class="fl">0.8</span></a> <a class="sourceLine" id="cb3-51" title="51"> )</a> <a class="sourceLine" id="cb3-52" title="52"> <span class="cf">if</span> (<span class="kw">isFALSE</span>(stopit))</a> <a class="sourceLine" id="cb3-53" title="53"> <span class="cf">break</span></a> <a class="sourceLine" id="cb3-54" title="54"> var <-<span class="st"> </span>head</a> <a class="sourceLine" id="cb3-55" title="55">}</a> <a class="sourceLine" id="cb3-56" title="56"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img src="data:image/png;base64,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" 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" 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" /><img src="data:image/png;base64,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" /></p> <p>As it can be seen, in each iteration the resulting head gradually loses the high-tail property, until the stopping condition is met.</p> <table> <thead> <tr class="header"> <th align="right">iter</th> <th align="right">mu</th> <th align="left">prop</th> <th align="right">n_var</th> <th align="right">n_head</th> </tr> </thead> <tbody> <tr class="odd"> <td align="right">1</td> <td align="right">5.6755</td> <td align="left">14.5%</td> <td align="right">1000</td> <td align="right">145</td> </tr> <tr class="even"> <td align="right">2</td> <td align="right">27.2369</td> <td align="left">21.38%</td> <td align="right">145</td> <td align="right">31</td> </tr> <tr class="odd"> <td align="right">3</td> <td align="right">85.1766</td> <td align="left">19.35%</td> <td align="right">31</td> <td align="right">6</td> </tr> <tr class="even"> <td align="right">4</td> <td align="right">264.7126</td> <td align="left">50%</td> <td align="right">6</td> <td align="right">3</td> </tr> </tbody> </table> <p>The resulting breaks are then defined as <code>breaks = c(min(var), mu(iter=1), ..., mu(iter), max(var))</code>.</p> </div> <div id="implementation-on-classint-package" class="section level1"> <h1>Implementation on <code>classInt</code> package</h1> <p>The implementation in the <code>classIntervals</code> function should replicate the results:</p> <div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">ht_sample_par <-<span class="st"> </span><span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>)</a> <a class="sourceLine" id="cb4-2" title="2">brks <span class="op">==</span><span class="st"> </span>ht_sample_par<span class="op">$</span>brks</a> <a class="sourceLine" id="cb4-3" title="3"><span class="co">#> [1] TRUE TRUE TRUE TRUE TRUE TRUE</span></a> <a class="sourceLine" id="cb4-4" title="4"><span class="kw">print</span>(ht_sample_par)</a> <a class="sourceLine" id="cb4-5" title="5"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb4-6" title="6"><span class="co">#> [1.000295,5.675463) [5.675463,27.23693) [27.23693,85.17664) [85.17664,264.7126) </span></a> <a class="sourceLine" id="cb4-7" title="7"><span class="co">#> 855 114 25 3 </span></a> <a class="sourceLine" id="cb4-8" title="8"><span class="co">#> [264.7126,523.6254] </span></a> <a class="sourceLine" id="cb4-9" title="9"><span class="co">#> 3</span></a></code></pre></div> <p>As stated in <span class="citation">Jiang (2013)</span>, the number of breaks is naturally determined, however the <code>thr</code> parameter could help to adjust the final number. A lower value on <code>thr</code> would provide less breaks while a larger <code>thr</code> would increase the number, if the underlying distribution follows the <em>“far more small things than large things”</em> principle.</p> <div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb5-2" title="2"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb5-3" title="3"></a> <a class="sourceLine" id="cb5-4" title="4">pal1 <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"wheat1"</span>, <span class="st">"wheat2"</span>, <span class="st">"red3"</span>)</a> <a class="sourceLine" id="cb5-5" title="5"></a> <a class="sourceLine" id="cb5-6" title="6"><span class="co"># Minimum: single break</span></a> <a class="sourceLine" id="cb5-7" title="7"><span class="kw">print</span>(<span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="dv">0</span>))</a> <a class="sourceLine" id="cb5-8" title="8"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb5-9" title="9"><span class="co">#> [1.000295,5.675463) [5.675463,523.6254] </span></a> <a class="sourceLine" id="cb5-10" title="10"><span class="co">#> 855 145</span></a> <a class="sourceLine" id="cb5-11" title="11"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb5-12" title="12"> <span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="dv">0</span>),</a> <a class="sourceLine" id="cb5-13" title="13"> <span class="dt">pal =</span> pal1,</a> <a class="sourceLine" id="cb5-14" title="14"> <span class="dt">main =</span> <span class="st">"thr = 0"</span></a> <a class="sourceLine" id="cb5-15" title="15">)</a> <a class="sourceLine" id="cb5-16" title="16"></a> <a class="sourceLine" id="cb5-17" title="17"><span class="co"># Two breaks</span></a> <a class="sourceLine" id="cb5-18" title="18"><span class="kw">print</span>(<span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="fl">0.2</span>))</a> <a class="sourceLine" id="cb5-19" title="19"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb5-20" title="20"><span class="co">#> [1.000295,5.675463) [5.675463,27.23693) [27.23693,523.6254] </span></a> <a class="sourceLine" id="cb5-21" title="21"><span class="co">#> 855 114 31</span></a> <a class="sourceLine" id="cb5-22" title="22"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb5-23" title="23"> <span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="fl">0.2</span>),</a> <a class="sourceLine" id="cb5-24" title="24"> <span class="dt">pal =</span> pal1,</a> <a class="sourceLine" id="cb5-25" title="25"> <span class="dt">main =</span> <span class="st">"thr = 0.2"</span></a> <a class="sourceLine" id="cb5-26" title="26">)</a> <a class="sourceLine" id="cb5-27" title="27"></a> <a class="sourceLine" id="cb5-28" title="28"><span class="co"># Default breaks: 0.4</span></a> <a class="sourceLine" id="cb5-29" title="29"><span class="kw">print</span>(<span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>))</a> <a class="sourceLine" id="cb5-30" title="30"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb5-31" title="31"><span class="co">#> [1.000295,5.675463) [5.675463,27.23693) [27.23693,85.17664) [85.17664,264.7126) </span></a> <a class="sourceLine" id="cb5-32" title="32"><span class="co">#> 855 114 25 3 </span></a> <a class="sourceLine" id="cb5-33" title="33"><span class="co">#> [264.7126,523.6254] </span></a> <a class="sourceLine" id="cb5-34" title="34"><span class="co">#> 3</span></a> <a class="sourceLine" id="cb5-35" title="35"><span class="kw">plot</span>(<span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>),</a> <a class="sourceLine" id="cb5-36" title="36"> <span class="dt">pal =</span> pal1,</a> <a class="sourceLine" id="cb5-37" title="37"> <span class="dt">main =</span> <span class="st">"thr = Default"</span>)</a> <a class="sourceLine" id="cb5-38" title="38"></a> <a class="sourceLine" id="cb5-39" title="39"><span class="co"># Maximum breaks</span></a> <a class="sourceLine" id="cb5-40" title="40"><span class="kw">print</span>(<span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="dv">1</span>))</a> <a class="sourceLine" id="cb5-41" title="41"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb5-42" title="42"><span class="co">#> [1.000295,5.675463) [5.675463,27.23693) [27.23693,85.17664) [85.17664,264.7126) </span></a> <a class="sourceLine" id="cb5-43" title="43"><span class="co">#> 855 114 25 3 </span></a> <a class="sourceLine" id="cb5-44" title="44"><span class="co">#> [264.7126,391.279) [391.279,523.6254] </span></a> <a class="sourceLine" id="cb5-45" title="45"><span class="co">#> 2 1</span></a> <a class="sourceLine" id="cb5-46" title="46"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb5-47" title="47"> <span class="kw">classIntervals</span>(sample_par, <span class="dt">style =</span> <span class="st">"headtails"</span>, <span class="dt">thr =</span> <span class="dv">1</span>),</a> <a class="sourceLine" id="cb5-48" title="48"> <span class="dt">pal =</span> pal1,</a> <a class="sourceLine" id="cb5-49" title="49"> <span class="dt">main =</span> <span class="st">"thr = 1"</span></a> <a class="sourceLine" id="cb5-50" title="50">)</a> <a class="sourceLine" id="cb5-51" title="51"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img 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src="data:image/png;base64,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" /><img src="data:image/png;base64,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" /></p> <p>The method always returns at least one break, corresponding to <code>mean(var)</code>.</p> </div> <div id="case-study" class="section level1"> <h1>Case study</h1> <p><span class="citation">Jiang (2013)</span> states that “the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution.” (p. 482), referring to Jenks’ natural breaks method. In this case study we would compare “headtails” vs. “fisher”, that is the alias for the Fisher-Jenks algorithm and it is always preferred to the “jenks” style (see <code>?classIntervals</code>). For this example we will use the <code>afcon</code> dataset from <code>spData</code> package.</p> <div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw">library</span>(spData)</a> <a class="sourceLine" id="cb6-2" title="2"><span class="kw">data</span>(afcon, <span class="dt">package =</span> <span class="st">"spData"</span>)</a></code></pre></div> <p>Let’s have a look to the Top 10 values and the distribution of the variable <code>totcon</code> (index of total conflict 1966-78):</p> <div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"></a> <a class="sourceLine" id="cb7-2" title="2"><span class="co"># Top10</span></a> <a class="sourceLine" id="cb7-3" title="3">knitr<span class="op">::</span><span class="kw">kable</span>(<span class="kw">head</span>(afcon[<span class="kw">order</span>(afcon<span class="op">$</span>totcon, <span class="dt">decreasing =</span> <span class="ot">TRUE</span>),<span class="kw">c</span>(<span class="st">"name"</span>,<span class="st">"totcon"</span>)],<span class="dv">10</span>))</a></code></pre></div> <table> <thead> <tr class="header"> <th></th> <th align="left">name</th> <th align="right">totcon</th> </tr> </thead> <tbody> <tr class="odd"> <td>EG</td> <td align="left">EGYPT</td> <td align="right">5246</td> </tr> <tr class="even"> <td>SU</td> <td align="left">SUDAN</td> <td align="right">4751</td> </tr> <tr class="odd"> <td>UG</td> <td align="left">UGANDA</td> <td align="right">3134</td> </tr> <tr class="even"> <td>CG</td> <td align="left">ZAIRE</td> <td align="right">3087</td> </tr> <tr class="odd"> <td>TZ</td> <td align="left">TANZANIA</td> <td align="right">2881</td> </tr> <tr class="even"> <td>LY</td> <td align="left">LIBYA</td> <td align="right">2355</td> </tr> <tr class="odd"> <td>KE</td> <td align="left">KENYA</td> <td align="right">2273</td> </tr> <tr class="even"> <td>SO</td> <td align="left">SOMALIA</td> <td align="right">2122</td> </tr> <tr class="odd"> <td>ET</td> <td align="left">ETHIOPIA</td> <td align="right">1878</td> </tr> <tr class="even"> <td>SF</td> <td align="left">SOUTH AFRICA</td> <td align="right">1875</td> </tr> </tbody> </table> <div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1"></a> <a class="sourceLine" id="cb8-2" title="2">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb8-3" title="3"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">4</span>, <span class="dv">4</span>, <span class="dv">3</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb8-4" title="4"><span class="kw">hist</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb8-5" title="5"> <span class="dt">n =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb8-6" title="6"> <span class="dt">main =</span> <span class="st">"Histogram"</span>,</a> <a class="sourceLine" id="cb8-7" title="7"> <span class="dt">xlab =</span> <span class="st">"totcon"</span>,</a> <a class="sourceLine" id="cb8-8" title="8"> <span class="dt">col =</span> <span class="st">"grey50"</span>,</a> <a class="sourceLine" id="cb8-9" title="9"> <span class="dt">border =</span> <span class="ot">NA</span>, )</a> <a class="sourceLine" id="cb8-10" title="10"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb8-11" title="11"> <span class="kw">density</span>(afcon<span class="op">$</span>totcon),</a> <a class="sourceLine" id="cb8-12" title="12"> <span class="dt">main =</span> <span class="st">"Distribution"</span>,</a> <a class="sourceLine" id="cb8-13" title="13"> <span class="dt">xlab =</span> <span class="st">"totcon"</span>,</a> <a class="sourceLine" id="cb8-14" title="14">)</a> <a class="sourceLine" id="cb8-15" title="15"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img src="data:image/png;base64,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" /><img src="data:image/png;base64,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" /></p> <p>The data shows that EG and SU data present a clear hierarchy over the rest of values. As per the histogram, we can confirm a heavy-tailed distribution and therefore the <em>“far more small things than large things”</em> principle.</p> <p>As a testing proof, on top of “headtails” and “fisher” we would use also “quantile” to have a broader view on the different breaking styles. As “quantile” is a position-based metric, it doesn’t account for the magnitude of F(x) (hierarchy), so the breaks are solely defined by the position of x on the distribution.</p> <p>Applying the three aforementioned methods to break the data:</p> <div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">brks_ht <-<span class="st"> </span><span class="kw">classIntervals</span>(afcon<span class="op">$</span>totcon, <span class="dt">style =</span> <span class="st">"headtails"</span>)</a> <a class="sourceLine" id="cb9-2" title="2"><span class="kw">print</span>(brks_ht)</a> <a class="sourceLine" id="cb9-3" title="3"><span class="co">#> style: headtails</span></a> <a class="sourceLine" id="cb9-4" title="4"><span class="co">#> one of 91,390 possible partitions of this variable into 5 classes</span></a> <a class="sourceLine" id="cb9-5" title="5"><span class="co">#> [147,1350.619) [1350.619,2488.6) [2488.6,3819.8) [3819.8,4998.5) </span></a> <a class="sourceLine" id="cb9-6" title="6"><span class="co">#> 27 10 3 1 </span></a> <a class="sourceLine" id="cb9-7" title="7"><span class="co">#> [4998.5,5246] </span></a> <a class="sourceLine" id="cb9-8" title="8"><span class="co">#> 1</span></a> <a class="sourceLine" id="cb9-9" title="9"><span class="co">#Same number of classes for "fisher"</span></a> <a class="sourceLine" id="cb9-10" title="10">nclass <-<span class="st"> </span><span class="kw">length</span>(brks_ht<span class="op">$</span>brks) <span class="op">-</span><span class="st"> </span><span class="dv">1</span></a> <a class="sourceLine" id="cb9-11" title="11">brks_fisher <-<span class="st"> </span><span class="kw">classIntervals</span>(afcon<span class="op">$</span>totcon, <span class="dt">style =</span> <span class="st">"fisher"</span>,</a> <a class="sourceLine" id="cb9-12" title="12"> <span class="dt">n =</span> nclass)</a> <a class="sourceLine" id="cb9-13" title="13"><span class="kw">print</span>(brks_fisher)</a> <a class="sourceLine" id="cb9-14" title="14"><span class="co">#> style: fisher</span></a> <a class="sourceLine" id="cb9-15" title="15"><span class="co">#> one of 91,390 possible partitions of this variable into 5 classes</span></a> <a class="sourceLine" id="cb9-16" title="16"><span class="co">#> [147,693.5) [693.5,1474.5) [1474.5,2618) [2618,3942.5) [3942.5,5246] </span></a> <a class="sourceLine" id="cb9-17" title="17"><span class="co">#> 12 17 8 3 2</span></a> <a class="sourceLine" id="cb9-18" title="18"></a> <a class="sourceLine" id="cb9-19" title="19">brks_quantile <-<span class="st"> </span><span class="kw">classIntervals</span>(afcon<span class="op">$</span>totcon, <span class="dt">style =</span> <span class="st">"quantile"</span>,</a> <a class="sourceLine" id="cb9-20" title="20"> <span class="dt">n =</span> nclass)</a> <a class="sourceLine" id="cb9-21" title="21"><span class="kw">print</span>(brks_quantile)</a> <a class="sourceLine" id="cb9-22" title="22"><span class="co">#> style: quantile</span></a> <a class="sourceLine" id="cb9-23" title="23"><span class="co">#> one of 91,390 possible partitions of this variable into 5 classes</span></a> <a class="sourceLine" id="cb9-24" title="24"><span class="co">#> [147,604) [604,833.6) [833.6,1137.2) [1137.2,1877.4) [1877.4,5246] </span></a> <a class="sourceLine" id="cb9-25" title="25"><span class="co">#> 8 9 8 8 9</span></a> <a class="sourceLine" id="cb9-26" title="26"></a> <a class="sourceLine" id="cb9-27" title="27">pal1 <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"wheat1"</span>, <span class="st">"wheat2"</span>, <span class="st">"red3"</span>)</a> <a class="sourceLine" id="cb9-28" title="28">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb9-29" title="29"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb9-30" title="30"><span class="kw">plot</span>(brks_ht, <span class="dt">pal =</span> pal1, <span class="dt">main =</span> <span class="st">"headtails"</span>)</a> <a class="sourceLine" id="cb9-31" title="31"><span class="kw">plot</span>(brks_fisher, <span class="dt">pal =</span> pal1, <span class="dt">main =</span> <span class="st">"fisher"</span>)</a> <a class="sourceLine" id="cb9-32" title="32"><span class="kw">plot</span>(brks_quantile, <span class="dt">pal =</span> pal1, <span class="dt">main =</span> <span class="st">"quantile"</span>)</a> <a class="sourceLine" id="cb9-33" title="33"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img src="data:image/png;base64,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" 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" 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" /></p> <p>It is observed that the top three classes of “headtails” enclose 5 observations, whereas “fisher” includes 13 observations. In terms of classification, “headtails” breaks focuses more on extreme values.</p> <p>The next plot compares a continuous distribution of <code>totcon</code> re-escalated to a range of <code>[1,nclass]</code> versus the distribution across breaks for each style. The continuous distribution has been offset by -0.5 in order to align the continuous and the discrete distributions.</p> <div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="co">#Helper function to reescale values</span></a> <a class="sourceLine" id="cb10-2" title="2">help_reescale <-<span class="st"> </span><span class="cf">function</span>(x, <span class="dt">min =</span> <span class="dv">1</span>, <span class="dt">max =</span> <span class="dv">10</span>) {</a> <a class="sourceLine" id="cb10-3" title="3"> r <-<span class="st"> </span>(x <span class="op">-</span><span class="st"> </span><span class="kw">min</span>(x)) <span class="op">/</span><span class="st"> </span>(<span class="kw">max</span>(x) <span class="op">-</span><span class="st"> </span><span class="kw">min</span>(x))</a> <a class="sourceLine" id="cb10-4" title="4"> r <-<span class="st"> </span>r <span class="op">*</span><span class="st"> </span>(max <span class="op">-</span><span class="st"> </span>min) <span class="op">+</span><span class="st"> </span>min</a> <a class="sourceLine" id="cb10-5" title="5"> <span class="kw">return</span>(r)</a> <a class="sourceLine" id="cb10-6" title="6">}</a> <a class="sourceLine" id="cb10-7" title="7">afcon<span class="op">$</span>ecdf_class <-<span class="st"> </span><span class="kw">help_reescale</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb10-8" title="8"> <span class="dt">min =</span> <span class="dv">1</span> <span class="op">-</span><span class="st"> </span><span class="fl">0.5</span>,</a> <a class="sourceLine" id="cb10-9" title="9"> <span class="dt">max =</span> nclass <span class="op">-</span><span class="st"> </span><span class="fl">0.5</span>)</a> <a class="sourceLine" id="cb10-10" title="10">afcon<span class="op">$</span>ht_breaks <-<span class="st"> </span><span class="kw">cut</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb10-11" title="11"> brks_ht<span class="op">$</span>brks,</a> <a class="sourceLine" id="cb10-12" title="12"> <span class="dt">labels =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb10-13" title="13"> <span class="dt">include.lowest =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb10-14" title="14"></a> <a class="sourceLine" id="cb10-15" title="15">afcon<span class="op">$</span>fisher_breaks <-<span class="st"> </span><span class="kw">cut</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb10-16" title="16"> brks_fisher<span class="op">$</span>brks,</a> <a class="sourceLine" id="cb10-17" title="17"> <span class="dt">labels =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb10-18" title="18"> <span class="dt">include.lowest =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb10-19" title="19"></a> <a class="sourceLine" id="cb10-20" title="20">afcon<span class="op">$</span>quantile_break <-<span class="st"> </span><span class="kw">cut</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb10-21" title="21"> brks_quantile<span class="op">$</span>brks,</a> <a class="sourceLine" id="cb10-22" title="22"> <span class="dt">labels =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb10-23" title="23"> <span class="dt">include.lowest =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb10-24" title="24"></a> <a class="sourceLine" id="cb10-25" title="25">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb10-26" title="26"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">4</span>, <span class="dv">4</span>, <span class="dv">1</span>, <span class="dv">1</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb10-27" title="27"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb10-28" title="28"> <span class="kw">density</span>(afcon<span class="op">$</span>ecdf_class),</a> <a class="sourceLine" id="cb10-29" title="29"> <span class="dt">ylim =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.8</span>),</a> <a class="sourceLine" id="cb10-30" title="30"> <span class="dt">lwd =</span> <span class="dv">2</span>,</a> <a class="sourceLine" id="cb10-31" title="31"> <span class="dt">main =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb10-32" title="32"> <span class="dt">xlab =</span> <span class="st">"class"</span></a> <a class="sourceLine" id="cb10-33" title="33">)</a> <a class="sourceLine" id="cb10-34" title="34"><span class="kw">lines</span>(<span class="kw">density</span>(afcon<span class="op">$</span>ht_breaks), <span class="dt">col =</span> <span class="st">"darkblue"</span>, <span class="dt">lty =</span> <span class="dv">2</span>)</a> <a class="sourceLine" id="cb10-35" title="35"><span class="kw">lines</span>(<span class="kw">density</span>(afcon<span class="op">$</span>fisher_breaks), <span class="dt">col =</span> <span class="st">"limegreen"</span>, <span class="dt">lty =</span> <span class="dv">2</span>)</a> <a class="sourceLine" id="cb10-36" title="36"><span class="kw">lines</span>(<span class="kw">density</span>(afcon<span class="op">$</span>quantile_break),</a> <a class="sourceLine" id="cb10-37" title="37"> <span class="dt">col =</span> <span class="st">"red3"</span>,</a> <a class="sourceLine" id="cb10-38" title="38"> <span class="dt">lty =</span> <span class="dv">2</span>)</a> <a class="sourceLine" id="cb10-39" title="39"><span class="kw">legend</span>(<span class="st">"topright"</span>,</a> <a class="sourceLine" id="cb10-40" title="40"> <span class="dt">legend =</span> <span class="kw">c</span>(<span class="st">"Continuous"</span>, <span class="st">"headtails"</span>,</a> <a class="sourceLine" id="cb10-41" title="41"> <span class="st">"fisher"</span>, <span class="st">"quantile"</span>),</a> <a class="sourceLine" id="cb10-42" title="42"> <span class="dt">col =</span> <span class="kw">c</span>(<span class="st">"black"</span>, <span class="st">"darkblue"</span>, <span class="st">"limegreen"</span>, <span class="st">"red3"</span>),</a> <a class="sourceLine" id="cb10-43" title="43"> <span class="dt">lwd =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">1</span>),</a> <a class="sourceLine" id="cb10-44" title="44"> <span class="dt">lty =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>),</a> <a class="sourceLine" id="cb10-45" title="45"> <span class="dt">cex =</span> <span class="fl">0.8</span></a> <a class="sourceLine" id="cb10-46" title="46">)</a> <a class="sourceLine" id="cb10-47" title="47"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img 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" /><!-- --></p> <p>It can be observed that the distribution of “headtails” breaks is also heavy-tailed, and closer to the original distribution. On the other extreme, “quantile” provides a quasi-uniform distribution, ignoring the <code>totcon</code> hierarchy</p> <p>In terms of data visualization, we compare here the final map using the techniques mentioned above. On this plotting exercise:</p> <ul> <li><code>cex</code> of points are always between <code>1</code> and <code>5</code>.</li> <li>For the continuous approach, no classes are provided. This plot will be used as the reference.</li> <li>For all the rest of styles, <code>col</code> and <code>cex</code> on each point is defined as per the class of that point.</li> </ul> <div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1">custompal <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"#FE9F6D99"</span>,</a> <a class="sourceLine" id="cb11-2" title="2"> <span class="st">"#DE496899"</span>,</a> <a class="sourceLine" id="cb11-3" title="3"> <span class="st">"#8C298199"</span>,</a> <a class="sourceLine" id="cb11-4" title="4"> <span class="st">"#3B0F7099"</span>,</a> <a class="sourceLine" id="cb11-5" title="5"> <span class="st">"#00000499"</span>)</a> <a class="sourceLine" id="cb11-6" title="6"></a> <a class="sourceLine" id="cb11-7" title="7">afcon<span class="op">$</span>cex_points <-<span class="st"> </span><span class="kw">help_reescale</span>(afcon<span class="op">$</span>totcon,</a> <a class="sourceLine" id="cb11-8" title="8"> <span class="dt">min =</span> <span class="dv">1</span>,</a> <a class="sourceLine" id="cb11-9" title="9"> <span class="dt">max =</span> <span class="dv">5</span>)</a> <a class="sourceLine" id="cb11-10" title="10">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb11-11" title="11"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="fl">1.5</span>, <span class="fl">1.5</span>, <span class="dv">2</span>, <span class="fl">1.5</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb11-12" title="12"><span class="co"># Plot continuous</span></a> <a class="sourceLine" id="cb11-13" title="13"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb11-14" title="14"> <span class="dt">x =</span> afcon<span class="op">$</span>x,</a> <a class="sourceLine" id="cb11-15" title="15"> <span class="dt">y =</span> afcon<span class="op">$</span>y,</a> <a class="sourceLine" id="cb11-16" title="16"> <span class="dt">axes =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb11-17" title="17"> <span class="dt">cex =</span> afcon<span class="op">$</span>cex_points,</a> <a class="sourceLine" id="cb11-18" title="18"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-19" title="19"> <span class="dt">col =</span> <span class="st">"grey50"</span>,</a> <a class="sourceLine" id="cb11-20" title="20"> <span class="dt">main =</span> <span class="st">"Continuous"</span>,</a> <a class="sourceLine" id="cb11-21" title="21">)</a> <a class="sourceLine" id="cb11-22" title="22"></a> <a class="sourceLine" id="cb11-23" title="23">mcont <-<span class="st"> </span>(<span class="kw">max</span>(afcon<span class="op">$</span>totcon) <span class="op">-</span><span class="st"> </span><span class="kw">min</span>(afcon<span class="op">$</span>totcon)) <span class="op">/</span><span class="st"> </span><span class="dv">4</span></a> <a class="sourceLine" id="cb11-24" title="24">legcont <-<span class="st"> </span><span class="dv">1</span><span class="op">:</span><span class="dv">5</span> <span class="op">*</span><span class="st"> </span>mcont <span class="op">-</span><span class="st"> </span>(mcont <span class="op">-</span><span class="st"> </span><span class="kw">min</span>(afcon<span class="op">$</span>totcon))</a> <a class="sourceLine" id="cb11-25" title="25"></a> <a class="sourceLine" id="cb11-26" title="26"><span class="kw">legend</span>(<span class="st">"bottomleft"</span>,</a> <a class="sourceLine" id="cb11-27" title="27"> <span class="dt">xjust =</span> <span class="dv">1</span>,</a> <a class="sourceLine" id="cb11-28" title="28"> <span class="dt">bty =</span> <span class="st">"n"</span>,</a> <a class="sourceLine" id="cb11-29" title="29"> <span class="dt">legend =</span> <span class="kw">paste0</span>(<span class="st">" "</span>,</a> <a class="sourceLine" id="cb11-30" title="30"> <span class="kw">round</span>(legcont, <span class="dv">0</span>)</a> <a class="sourceLine" id="cb11-31" title="31"> ),</a> <a class="sourceLine" id="cb11-32" title="32"> <span class="dt">col =</span> <span class="st">"grey50"</span>,</a> <a class="sourceLine" id="cb11-33" title="33"> <span class="dt">pt.cex =</span> <span class="kw">seq</span>(<span class="dv">1</span>, <span class="dv">5</span>),</a> <a class="sourceLine" id="cb11-34" title="34"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-35" title="35"> <span class="dt">title =</span> <span class="st">"totcon"</span></a> <a class="sourceLine" id="cb11-36" title="36">)</a> <a class="sourceLine" id="cb11-37" title="37"><span class="kw">box</span>()</a> <a class="sourceLine" id="cb11-38" title="38"></a> <a class="sourceLine" id="cb11-39" title="39"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb11-40" title="40"> <span class="dt">x =</span> afcon<span class="op">$</span>x,</a> <a class="sourceLine" id="cb11-41" title="41"> <span class="dt">y =</span> afcon<span class="op">$</span>y,</a> <a class="sourceLine" id="cb11-42" title="42"> <span class="dt">axes =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb11-43" title="43"> <span class="dt">cex =</span> afcon<span class="op">$</span>ht_breaks,</a> <a class="sourceLine" id="cb11-44" title="44"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-45" title="45"> <span class="dt">col =</span> custompal[afcon<span class="op">$</span>ht_breaks],</a> <a class="sourceLine" id="cb11-46" title="46"> <span class="dt">main =</span> <span class="st">"headtails"</span></a> <a class="sourceLine" id="cb11-47" title="47">)</a> <a class="sourceLine" id="cb11-48" title="48"><span class="kw">legend</span>(</a> <a class="sourceLine" id="cb11-49" title="49"> <span class="st">"bottomleft"</span>,</a> <a class="sourceLine" id="cb11-50" title="50"> <span class="dt">xjust =</span> <span class="dv">1</span>,</a> <a class="sourceLine" id="cb11-51" title="51"> <span class="dt">bty =</span> <span class="st">"n"</span>,</a> <a class="sourceLine" id="cb11-52" title="52"> <span class="dt">legend =</span> <span class="kw">paste0</span>(<span class="st">" "</span>,</a> <a class="sourceLine" id="cb11-53" title="53"> <span class="kw">round</span>(brks_ht<span class="op">$</span>brks[<span class="dv">2</span><span class="op">:</span><span class="dv">6</span>],<span class="dv">0</span>)</a> <a class="sourceLine" id="cb11-54" title="54"> ),</a> <a class="sourceLine" id="cb11-55" title="55"> <span class="dt">col =</span> custompal,</a> <a class="sourceLine" id="cb11-56" title="56"> <span class="dt">pt.cex =</span> <span class="kw">seq</span>(<span class="dv">1</span>, <span class="dv">5</span>),</a> <a class="sourceLine" id="cb11-57" title="57"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-58" title="58"> <span class="dt">title =</span> <span class="st">"totcon"</span></a> <a class="sourceLine" id="cb11-59" title="59">)</a> <a class="sourceLine" id="cb11-60" title="60"><span class="kw">box</span>()</a> <a class="sourceLine" id="cb11-61" title="61"></a> <a class="sourceLine" id="cb11-62" title="62"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb11-63" title="63"> <span class="dt">x =</span> afcon<span class="op">$</span>x,</a> <a class="sourceLine" id="cb11-64" title="64"> <span class="dt">y =</span> afcon<span class="op">$</span>y,</a> <a class="sourceLine" id="cb11-65" title="65"> <span class="dt">axes =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb11-66" title="66"> <span class="dt">cex =</span> afcon<span class="op">$</span>fisher_breaks,</a> <a class="sourceLine" id="cb11-67" title="67"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-68" title="68"> <span class="dt">col =</span> custompal[afcon<span class="op">$</span>fisher_breaks],</a> <a class="sourceLine" id="cb11-69" title="69"> <span class="dt">main =</span> <span class="st">"fisher"</span></a> <a class="sourceLine" id="cb11-70" title="70">)</a> <a class="sourceLine" id="cb11-71" title="71"><span class="kw">legend</span>(</a> <a class="sourceLine" id="cb11-72" title="72"> <span class="st">"bottomleft"</span>,</a> <a class="sourceLine" id="cb11-73" title="73"> <span class="dt">xjust =</span> <span class="dv">1</span>,</a> <a class="sourceLine" id="cb11-74" title="74"> <span class="dt">bty =</span> <span class="st">"n"</span>,</a> <a class="sourceLine" id="cb11-75" title="75"> <span class="dt">legend =</span> <span class="kw">paste0</span>(<span class="st">" "</span>,</a> <a class="sourceLine" id="cb11-76" title="76"> <span class="kw">round</span>(brks_fisher<span class="op">$</span>brks[<span class="dv">2</span><span class="op">:</span><span class="dv">6</span>],<span class="dv">0</span>)</a> <a class="sourceLine" id="cb11-77" title="77"> ),</a> <a class="sourceLine" id="cb11-78" title="78"> <span class="dt">col =</span> custompal,</a> <a class="sourceLine" id="cb11-79" title="79"> <span class="dt">pt.cex =</span> <span class="kw">seq</span>(<span class="dv">1</span>, <span class="dv">5</span>),</a> <a class="sourceLine" id="cb11-80" title="80"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-81" title="81"> <span class="dt">title =</span> <span class="st">"totcon"</span></a> <a class="sourceLine" id="cb11-82" title="82">)</a> <a class="sourceLine" id="cb11-83" title="83"><span class="kw">box</span>()</a> <a class="sourceLine" id="cb11-84" title="84"></a> <a class="sourceLine" id="cb11-85" title="85"><span class="kw">plot</span>(</a> <a class="sourceLine" id="cb11-86" title="86"> <span class="dt">x =</span> afcon<span class="op">$</span>x,</a> <a class="sourceLine" id="cb11-87" title="87"> <span class="dt">y =</span> afcon<span class="op">$</span>y,</a> <a class="sourceLine" id="cb11-88" title="88"> <span class="dt">axes =</span> <span class="ot">FALSE</span>,</a> <a class="sourceLine" id="cb11-89" title="89"> <span class="dt">cex =</span> afcon<span class="op">$</span>quantile_break,</a> <a class="sourceLine" id="cb11-90" title="90"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-91" title="91"> <span class="dt">col =</span> custompal[afcon<span class="op">$</span>quantile_break],</a> <a class="sourceLine" id="cb11-92" title="92"> <span class="dt">main =</span> <span class="st">"quantile"</span></a> <a class="sourceLine" id="cb11-93" title="93">)</a> <a class="sourceLine" id="cb11-94" title="94"><span class="kw">legend</span>(</a> <a class="sourceLine" id="cb11-95" title="95"> <span class="st">"bottomleft"</span>,</a> <a class="sourceLine" id="cb11-96" title="96"> <span class="dt">xjust =</span> <span class="dv">1</span>,</a> <a class="sourceLine" id="cb11-97" title="97"> <span class="dt">bty =</span> <span class="st">"n"</span>,</a> <a class="sourceLine" id="cb11-98" title="98"> <span class="dt">legend =</span> <span class="kw">paste0</span>(<span class="st">" "</span>,</a> <a class="sourceLine" id="cb11-99" title="99"> <span class="kw">round</span>(brks_quantile<span class="op">$</span>brks[<span class="dv">2</span><span class="op">:</span><span class="dv">6</span>],<span class="dv">0</span>)</a> <a class="sourceLine" id="cb11-100" title="100"> ),</a> <a class="sourceLine" id="cb11-101" title="101"> <span class="dt">col =</span> custompal,</a> <a class="sourceLine" id="cb11-102" title="102"> <span class="dt">pt.cex =</span> <span class="kw">seq</span>(<span class="dv">1</span>, <span class="dv">5</span>),</a> <a class="sourceLine" id="cb11-103" title="103"> <span class="dt">pch =</span> <span class="dv">20</span>,</a> <a class="sourceLine" id="cb11-104" title="104"> <span class="dt">title =</span> <span class="st">"totcon"</span></a> <a class="sourceLine" id="cb11-105" title="105">)</a> <a class="sourceLine" id="cb11-106" title="106"><span class="kw">box</span>()</a> <a class="sourceLine" id="cb11-107" title="107"></a> <a class="sourceLine" id="cb11-108" title="108"><span class="kw">par</span>(opar)</a></code></pre></div> <p><img src="data:image/png;base64,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" 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" 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" 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" /></p> <p>As per the results, “headtails” seems to provide a better understanding of the most extreme values when the result is compared against the continuous plot. The “quantile” style, as expected, just provides a clustering without taking into account the real hierarchy. The “fisher” plot is in-between of these two interpretations.</p> <p>It is also important to note that “headtails” and “fisher” reveal different information that can be useful depending of the context. While “headtails” highlights the outliers, it fails on providing a good clustering on the tail, while “fisher” seems to reflect better these patterns. This can be observed on the values of Western Africa and the Niger River Basin, where “headtails” doesn’t highlight any special cluster of conflicts, “fisher” suggests a potential cluster. This can be confirmed on the histogram generated previously, where a concentration of <code>totcon</code> around 1,000 is visible.</p> </div> <div id="testing-and-benchmark" class="section level1"> <h1>Testing and benchmark</h1> <p>On this section the performance of the “headtails” implementation is tested, in terms of speed and handling of corner cases. A small benchmark with another styles is also presented.</p> <p>Testing has been performed over the following distributions:</p> <p><strong>Heavy-tailed distributions</strong></p> <ul> <li>Pareto</li> <li>Exponential</li> <li>Log-normal</li> <li>Weibull</li> <li>Log-Cauchy, also known as super-heavy tail distribution (<span class="citation">Falk, Huesler, and Reiss (2011)</span>, p. 80, <span class="citation">Fraga Alves, Haan, and Neves (2009)</span>)</li> </ul> <p><strong>Non heavy-tailed distributions</strong></p> <ul> <li>Normal (non heavy-tailed)</li> <li>Truncated Normal (left-tailed)</li> <li>Uniform distribution</li> </ul> <div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="co">#Init samples</span></a> <a class="sourceLine" id="cb12-2" title="2"><span class="kw">set.seed</span>(<span class="dv">2389</span>)</a> <a class="sourceLine" id="cb12-3" title="3"></a> <a class="sourceLine" id="cb12-4" title="4"><span class="co">#Pareto distributions a=7 b=14</span></a> <a class="sourceLine" id="cb12-5" title="5">paretodist <-<span class="st"> </span><span class="dv">7</span> <span class="op">/</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">-</span><span class="st"> </span><span class="kw">runif</span>(<span class="dv">5000000</span>)) <span class="op">^</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">/</span><span class="st"> </span><span class="dv">14</span>)</a> <a class="sourceLine" id="cb12-6" title="6"><span class="co">#Exponential dist</span></a> <a class="sourceLine" id="cb12-7" title="7">expdist <-<span class="st"> </span><span class="kw">rexp</span>(<span class="dv">5000000</span>)</a> <a class="sourceLine" id="cb12-8" title="8"><span class="co">#Lognorm</span></a> <a class="sourceLine" id="cb12-9" title="9">lognormdist <-<span class="st"> </span><span class="kw">rlnorm</span>(<span class="dv">5000000</span>)</a> <a class="sourceLine" id="cb12-10" title="10"><span class="co">#Weibull</span></a> <a class="sourceLine" id="cb12-11" title="11">weibulldist <-<span class="st"> </span><span class="kw">rweibull</span>(<span class="dv">5000000</span>, <span class="dv">1</span>, <span class="dt">scale =</span> <span class="dv">5</span>)</a> <a class="sourceLine" id="cb12-12" title="12"><span class="co">#LogCauchy "super-heavy tail"</span></a> <a class="sourceLine" id="cb12-13" title="13">logcauchdist <-<span class="st"> </span><span class="kw">exp</span>(<span class="kw">rcauchy</span>(<span class="dv">5000000</span>, <span class="dv">2</span>, <span class="dv">4</span>))</a> <a class="sourceLine" id="cb12-14" title="14"><span class="co">#Remove Inf </span></a> <a class="sourceLine" id="cb12-15" title="15">logcauchdist <-<span class="st"> </span>logcauchdist[logcauchdist <span class="op"><</span><span class="st"> </span><span class="ot">Inf</span>]</a> <a class="sourceLine" id="cb12-16" title="16"></a> <a class="sourceLine" id="cb12-17" title="17"><span class="co">#Normal dist</span></a> <a class="sourceLine" id="cb12-18" title="18">normdist <-<span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">5000000</span>)</a> <a class="sourceLine" id="cb12-19" title="19"><span class="co">#Left-tailed distr</span></a> <a class="sourceLine" id="cb12-20" title="20">leftnorm <-</a> <a class="sourceLine" id="cb12-21" title="21"><span class="st"> </span><span class="kw">sample</span>(<span class="kw">rep</span>(normdist[normdist <span class="op"><</span><span class="st"> </span><span class="kw">mean</span>(normdist)], <span class="dv">3</span>), <span class="dt">size =</span> <span class="dv">5000000</span>)</a> <a class="sourceLine" id="cb12-22" title="22"></a> <a class="sourceLine" id="cb12-23" title="23"><span class="co">#Uniform distribution</span></a> <a class="sourceLine" id="cb12-24" title="24">unifdist <-<span class="st"> </span><span class="kw">runif</span>(<span class="dv">5000000</span>)</a></code></pre></div> <p>Let’s define a helper function and proceed to run the whole test suite:</p> <div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1">testresults <-<span class="st"> </span><span class="kw">data.frame</span>(</a> <a class="sourceLine" id="cb13-2" title="2"> <span class="dt">Title =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb13-3" title="3"> <span class="dt">style =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb13-4" title="4"> <span class="dt">nsample =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb13-5" title="5"> <span class="dt">thresold =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb13-6" title="6"> <span class="dt">nbreaks =</span> <span class="ot">NA</span>,</a> <a class="sourceLine" id="cb13-7" title="7"> <span class="dt">time_secs =</span> <span class="ot">NA</span></a> <a class="sourceLine" id="cb13-8" title="8">)</a> <a class="sourceLine" id="cb13-9" title="9"></a> <a class="sourceLine" id="cb13-10" title="10">benchmarkdist <-</a> <a class="sourceLine" id="cb13-11" title="11"><span class="st"> </span><span class="cf">function</span>(dist,</a> <a class="sourceLine" id="cb13-12" title="12"> <span class="dt">style =</span> <span class="st">"headtails"</span>,</a> <a class="sourceLine" id="cb13-13" title="13"> <span class="dt">thr =</span> <span class="fl">0.4</span>,</a> <a class="sourceLine" id="cb13-14" title="14"> <span class="dt">title =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb13-15" title="15"> <span class="dt">plot =</span> <span class="ot">FALSE</span>) {</a> <a class="sourceLine" id="cb13-16" title="16"> init <-<span class="st"> </span><span class="kw">Sys.time</span>()</a> <a class="sourceLine" id="cb13-17" title="17"> br <-<span class="st"> </span><span class="kw">classIntervals</span>(dist, <span class="dt">style =</span> style, <span class="dt">thr =</span> thr)</a> <a class="sourceLine" id="cb13-18" title="18"> a <-<span class="st"> </span><span class="kw">Sys.time</span>() <span class="op">-</span><span class="st"> </span>init</a> <a class="sourceLine" id="cb13-19" title="19"> test <-<span class="st"> </span><span class="kw">data.frame</span>(</a> <a class="sourceLine" id="cb13-20" title="20"> <span class="dt">Title =</span> title,</a> <a class="sourceLine" id="cb13-21" title="21"> <span class="dt">style =</span> style,</a> <a class="sourceLine" id="cb13-22" title="22"> <span class="dt">nsample =</span> <span class="kw">format</span>(<span class="kw">length</span>(br<span class="op">$</span>var), </a> <a class="sourceLine" id="cb13-23" title="23"> <span class="dt">scientific =</span> <span class="ot">FALSE</span>, <span class="dt">big.mark =</span> <span class="st">","</span>),</a> <a class="sourceLine" id="cb13-24" title="24"> <span class="dt">thresold =</span> thr,</a> <a class="sourceLine" id="cb13-25" title="25"> <span class="dt">nbreaks =</span> <span class="kw">length</span>(br<span class="op">$</span>brks) <span class="op">-</span><span class="st"> </span><span class="dv">1</span>,</a> <a class="sourceLine" id="cb13-26" title="26"> <span class="dt">time_secs =</span> <span class="kw">as.character</span>(<span class="kw">round</span>(a,<span class="dv">4</span>))</a> <a class="sourceLine" id="cb13-27" title="27"> )</a> <a class="sourceLine" id="cb13-28" title="28"> testresults <-<span class="st"> </span><span class="kw">unique</span>(<span class="kw">rbind</span>(testresults, test))</a> <a class="sourceLine" id="cb13-29" title="29"> </a> <a class="sourceLine" id="cb13-30" title="30"> <span class="cf">if</span> (plot) {</a> <a class="sourceLine" id="cb13-31" title="31"> <span class="kw">plot</span>(</a> <a class="sourceLine" id="cb13-32" title="32"> <span class="kw">density</span>(br<span class="op">$</span>var,</a> <a class="sourceLine" id="cb13-33" title="33"> <span class="dt">from =</span> <span class="kw">quantile</span>(dist,.<span class="dv">0005</span>),</a> <a class="sourceLine" id="cb13-34" title="34"> <span class="dt">to =</span> <span class="kw">quantile</span>(dist,.<span class="dv">9995</span>)</a> <a class="sourceLine" id="cb13-35" title="35"> ),</a> <a class="sourceLine" id="cb13-36" title="36"> <span class="dt">col =</span> <span class="st">"black"</span>,</a> <a class="sourceLine" id="cb13-37" title="37"> <span class="dt">cex.main =</span> <span class="fl">.9</span>,</a> <a class="sourceLine" id="cb13-38" title="38"> <span class="dt">main =</span> <span class="kw">paste0</span>(</a> <a class="sourceLine" id="cb13-39" title="39"> title,</a> <a class="sourceLine" id="cb13-40" title="40"> <span class="st">" "</span>,</a> <a class="sourceLine" id="cb13-41" title="41"> style,</a> <a class="sourceLine" id="cb13-42" title="42"> <span class="st">", thr ="</span>,</a> <a class="sourceLine" id="cb13-43" title="43"> thr,</a> <a class="sourceLine" id="cb13-44" title="44"> <span class="st">", nbreaks = "</span>,</a> <a class="sourceLine" id="cb13-45" title="45"> <span class="kw">length</span>(br<span class="op">$</span>brks) <span class="op">-</span><span class="st"> </span><span class="dv">1</span></a> <a class="sourceLine" id="cb13-46" title="46"> ),</a> <a class="sourceLine" id="cb13-47" title="47"> <span class="dt">ylab =</span> <span class="st">""</span>,</a> <a class="sourceLine" id="cb13-48" title="48"> <span class="dt">xlab =</span> <span class="st">""</span></a> <a class="sourceLine" id="cb13-49" title="49"> )</a> <a class="sourceLine" id="cb13-50" title="50"> <span class="kw">abline</span>(<span class="dt">v =</span> br<span class="op">$</span>brks,</a> <a class="sourceLine" id="cb13-51" title="51"> <span class="dt">col =</span> <span class="st">"red3"</span>,</a> <a class="sourceLine" id="cb13-52" title="52"> <span class="dt">lty =</span> <span class="dv">2</span>)</a> <a class="sourceLine" id="cb13-53" title="53"> }</a> <a class="sourceLine" id="cb13-54" title="54"> <span class="kw">return</span>(testresults)</a> <a class="sourceLine" id="cb13-55" title="55"> }</a> <a class="sourceLine" id="cb13-56" title="56">opar <-<span class="st"> </span><span class="kw">par</span>(<span class="dt">no.readonly =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-57" title="57"><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>), <span class="dt">cex =</span> <span class="fl">0.8</span>)</a> <a class="sourceLine" id="cb13-58" title="58"></a> <a class="sourceLine" id="cb13-59" title="59"><span class="co"># Pareto----</span></a> <a class="sourceLine" id="cb13-60" title="60">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(paretodist, <span class="dt">title =</span> <span class="st">"Pareto"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-61" title="61">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(paretodist, <span class="dt">title =</span> <span class="st">"Pareto"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-62" title="62">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(paretodist, <span class="dt">title =</span> <span class="st">"Pareto"</span>, <span class="dt">thr =</span> <span class="fl">.75</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-63" title="63"></a> <a class="sourceLine" id="cb13-64" title="64"><span class="co">#Sample 2,000 obs</span></a> <a class="sourceLine" id="cb13-65" title="65"><span class="kw">set.seed</span>(<span class="dv">1234</span>)</a> <a class="sourceLine" id="cb13-66" title="66">Paretosamp <-<span class="st"> </span><span class="kw">sample</span>(paretodist, <span class="dv">2000</span>)</a> <a class="sourceLine" id="cb13-67" title="67">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(Paretosamp,</a> <a class="sourceLine" id="cb13-68" title="68"> <span class="dt">title =</span> <span class="st">"Pareto sample"</span>,</a> <a class="sourceLine" id="cb13-69" title="69"> <span class="dt">style =</span> <span class="st">"fisher"</span>,</a> <a class="sourceLine" id="cb13-70" title="70"> <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-71" title="71">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(Paretosamp,</a> <a class="sourceLine" id="cb13-72" title="72"> <span class="dt">title =</span> <span class="st">"Pareto sample"</span>,</a> <a class="sourceLine" id="cb13-73" title="73"> <span class="dt">style =</span> <span class="st">"headtails"</span>,</a> <a class="sourceLine" id="cb13-74" title="74"> <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-75" title="75"></a> <a class="sourceLine" id="cb13-76" title="76"></a> <a class="sourceLine" id="cb13-77" title="77"><span class="co">#Exponential----</span></a> <a class="sourceLine" id="cb13-78" title="78">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(expdist, <span class="dt">title =</span> <span class="st">"Exponential"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-79" title="79">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(expdist, <span class="dt">title =</span> <span class="st">"Exponential"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-80" title="80">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(expdist, <span class="dt">title =</span> <span class="st">"Exponential"</span>, <span class="dt">thr =</span> <span class="dv">1</span>)</a> <a class="sourceLine" id="cb13-81" title="81">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(expdist, <span class="dt">title =</span> <span class="st">"Exponential"</span>,</a> <a class="sourceLine" id="cb13-82" title="82"> <span class="dt">style =</span> <span class="st">"quantile"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-83" title="83"></a> <a class="sourceLine" id="cb13-84" title="84"><span class="co">#Weibull-----</span></a> <a class="sourceLine" id="cb13-85" title="85">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(weibulldist, <span class="dt">title =</span> <span class="st">"Weibull"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-86" title="86">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(weibulldist, <span class="dt">title =</span> <span class="st">"Weibull"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-87" title="87">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(weibulldist, <span class="dt">title =</span> <span class="st">"Weibull"</span>, <span class="dt">thr =</span> <span class="dv">1</span>)</a> <a class="sourceLine" id="cb13-88" title="88"></a> <a class="sourceLine" id="cb13-89" title="89"><span class="co">#Logcauchy</span></a> <a class="sourceLine" id="cb13-90" title="90">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(logcauchdist, <span class="dt">title =</span> <span class="st">"LogCauchy"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-91" title="91">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(logcauchdist, <span class="dt">title =</span> <span class="st">"LogCauchy"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-92" title="92">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(logcauchdist, <span class="dt">title =</span> <span class="st">"LogCauchy"</span>, <span class="dt">thr =</span> <span class="dv">1</span>)</a> <a class="sourceLine" id="cb13-93" title="93"></a> <a class="sourceLine" id="cb13-94" title="94"><span class="co">#Normal----</span></a> <a class="sourceLine" id="cb13-95" title="95">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(normdist, <span class="dt">title =</span> <span class="st">"Normal"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-96" title="96">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(normdist, <span class="dt">title =</span> <span class="st">"Normal"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-97" title="97">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(normdist, <span class="dt">title =</span> <span class="st">"Normal"</span>, <span class="dt">thr =</span> <span class="dv">1</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-98" title="98"></a> <a class="sourceLine" id="cb13-99" title="99"><span class="co">#Truncated Left-tail Normal----</span></a> <a class="sourceLine" id="cb13-100" title="100">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(leftnorm, <span class="dt">title =</span> <span class="st">"Left Normal"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>)</a> <a class="sourceLine" id="cb13-101" title="101">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(leftnorm, <span class="dt">title =</span> <span class="st">"Left Normal"</span>, <span class="dt">thr =</span> <span class="dv">-100</span>)</a> <a class="sourceLine" id="cb13-102" title="102">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(leftnorm, <span class="dt">title =</span> <span class="st">"Left Normal"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>, <span class="dt">thr =</span> <span class="dv">500</span>)</a> <a class="sourceLine" id="cb13-103" title="103"></a> <a class="sourceLine" id="cb13-104" title="104"><span class="co">#Uniform----</span></a> <a class="sourceLine" id="cb13-105" title="105">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(unifdist, <span class="dt">title =</span> <span class="st">"Uniform"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>, <span class="dt">thr =</span> <span class="fl">0.7</span>)</a> <a class="sourceLine" id="cb13-106" title="106">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(unifdist, <span class="dt">title =</span> <span class="st">"Uniform"</span>, <span class="dt">thr =</span> <span class="dv">0</span>)</a> <a class="sourceLine" id="cb13-107" title="107">testresults <-<span class="st"> </span><span class="kw">benchmarkdist</span>(unifdist, <span class="dt">title =</span> <span class="st">"Uniform"</span>, <span class="dt">plot =</span> <span class="ot">TRUE</span>, <span class="dt">thr =</span> <span class="dv">1</span>)</a> <a class="sourceLine" id="cb13-108" title="108"><span class="kw">par</span>(opar)</a> <a class="sourceLine" id="cb13-109" title="109"></a> <a class="sourceLine" id="cb13-110" title="110"><span class="co"># Results</span></a> <a class="sourceLine" id="cb13-111" title="111">knitr<span class="op">::</span><span class="kw">kable</span>(testresults[<span class="op">-</span><span class="dv">1</span>, ], <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</a></code></pre></div> <table> <thead> <tr class="header"> <th align="left">Title</th> <th align="left">style</th> <th align="left">nsample</th> <th align="right">thresold</th> <th align="right">nbreaks</th> <th align="left">time_secs</th> </tr> </thead> <tbody> <tr class="odd"> <td align="left">Pareto</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">15</td> <td align="left">0.6361</td> </tr> <tr class="even"> <td align="left">Pareto</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4134</td> </tr> <tr class="odd"> <td align="left">Pareto</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.75</td> <td align="right">15</td> <td align="left">0.4864</td> </tr> <tr class="even"> <td align="left">Pareto sample</td> <td align="left">fisher</td> <td align="left">2,000</td> <td align="right">0.40</td> <td align="right">12</td> <td align="left">0.0234</td> </tr> <tr class="odd"> <td align="left">Pareto sample</td> <td align="left">headtails</td> <td align="left">2,000</td> <td align="right">0.40</td> <td align="right">8</td> <td align="left">3e-04</td> </tr> <tr class="even"> <td align="left">Exponential</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">16</td> <td align="left">0.5284</td> </tr> <tr class="odd"> <td align="left">Exponential</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4364</td> </tr> <tr class="even"> <td align="left">Exponential</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">1.00</td> <td align="right">17</td> <td align="left">0.5724</td> </tr> <tr class="odd"> <td align="left">Exponential</td> <td align="left">quantile</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">24</td> <td align="left">0.8606</td> </tr> <tr class="even"> <td align="left">Weibull</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">16</td> <td align="left">0.5363</td> </tr> <tr class="odd"> <td align="left">Weibull</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4414</td> </tr> <tr class="even"> <td align="left">Weibull</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">1.00</td> <td align="right">17</td> <td align="left">0.5376</td> </tr> <tr class="odd"> <td align="left">LogCauchy</td> <td align="left">headtails</td> <td align="left">4,991,187</td> <td align="right">0.40</td> <td align="right">6</td> <td align="left">0.4102</td> </tr> <tr class="even"> <td align="left">LogCauchy</td> <td align="left">headtails</td> <td align="left">4,991,187</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4556</td> </tr> <tr class="odd"> <td align="left">LogCauchy</td> <td align="left">headtails</td> <td align="left">4,991,187</td> <td align="right">1.00</td> <td align="right">6</td> <td align="left">0.4562</td> </tr> <tr class="even"> <td align="left">Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">2</td> <td align="left">0.4963</td> </tr> <tr class="odd"> <td align="left">Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4534</td> </tr> <tr class="even"> <td align="left">Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">1.00</td> <td align="right">17</td> <td align="left">0.552</td> </tr> <tr class="odd"> <td align="left">Left Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.40</td> <td align="right">2</td> <td align="left">0.6246</td> </tr> <tr class="even"> <td align="left">Left Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">-100.00</td> <td align="right">2</td> <td align="left">0.585</td> </tr> <tr class="odd"> <td align="left">Left Normal</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">500.00</td> <td align="right">21</td> <td align="left">0.7941</td> </tr> <tr class="even"> <td align="left">Uniform</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.70</td> <td align="right">22</td> <td align="left">0.4963</td> </tr> <tr class="odd"> <td align="left">Uniform</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">0.00</td> <td align="right">2</td> <td align="left">0.4122</td> </tr> <tr class="even"> <td align="left">Uniform</td> <td align="left">headtails</td> <td align="left">5,000,000</td> <td align="right">1.00</td> <td align="right">22</td> <td align="left">0.5308</td> </tr> </tbody> </table> <p><img 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" /><img 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" 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" 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" 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ERFRaWlpeEt0sjHp23IEHkVEUuCDWQfDOaD6TTyDvvhw4dv3bq1tbWVWqf73LlzJEmeOnUqICCgZw6AGh/szJkz06ZNo/7/4MEDJyennhUBAL1Eez5YUVHR4sWLjYyMFi1aRBDEqFGjEEKjR4+mw6cSiUTyJ5izs3N9fX1TUxP2UgCAcdoTbOjQoSkpKc3NzSkpKSRJXrt2DSF06dIlT09PvEXKhELps2fyBNPT0/Pw8OhlRyL4YBgBHwwj7Qm2a9euL7/80tzcPCUl5eTJk9HR0VFRUTExMZs2bcJbZH1qqkVamryTAyHk7e3dy5mX4INhBHwwjLQP9h09evTjx4/Ly8u9vLxYLJaLi8vZs2fXrVsXHh6Ot8gOPhjCkWDgg2HWBB8MEy+NpjcxMfH19aX+z+PxeDweTaUqdtMjhLy8vFRu1QcAfZ0upquUlJScOXMmLi5OzTnFxcXHjx9XPl5WVtbZuqIdEszHx+ebb77RIFoA6GN0kWDFxcWJiYnqE0woFNbX1ysfb2hoUNkDaeTj0+DoiLeKOJB9MJgPptPQ5wCYmZlRHZLKbN26dcOGDYpHrKysamtr6QsGAFRCtw/W8QmWmZlZUlJSU1NjZWXl5eU1evRogiCwZ7VYLFasIiKEvL29CwsLR48ejb0sAGCQ9gQrLS2NioqqrKz08/PjcrkCgYDP53O53IyMjCFYa18yoZBobDTgcBQPUrXEHieY+MkTlqUlgWPPeTmiR48MBw/GKIhIUlxRYeDsjFFS2tCA9PX1X76ZvQT/hctk4qoqAycnjJLiigqDQYOQnq7vINkeX2xs7Ny5c6urqy9evJienn7hwoWqqqpVq1bFxMTgLbI+NdX7wgXDl5PBx8enqKiox5rgg2EEfDCMtCdYXl7e8uXL9RR+EgiCWLZsGZ/Px1sk5YN1qCL6+Pj0qp8DfDC8muCDYaI9nUJDQ3ft2iVR+LRkMllycrI/7sHaSKmbHiHk4+NTWFiIvSAAYJb2NlhKSsrs2bOTkpICAgI4HE5TUxOfz7e3tz958iT2UkmSZLFe6l/x9PR88OCBRCLpcBwA+jTt32Y3N7ecnJycnJySkpLa2lqqFzE0NBR7kUY+Pk+srU1fTiRjY2NHR8eysjJvb+8eaIIPhhHwwXBCnwOgxgeLjY3dv39/h4PTp08/ceIEffEAgDL9c11ElVXBoUOHQjMM6Gcwsy6iQXOzcoL5+vr2OMFgPhhGYD4YRphZFzH89m19ff0Ox319fQsKCnqmCT4YRsAHwwhj+4OpfIIVFBSQPdszGnwwvJrgg2GCsXURlRPM0tLSxMSksrKSkZAAgA6YSTCSJJWriAghPz+/v/76S/vxAABNMJBgRj4+jzgclYZyjxNsIPtgsC6iLsPAsAnO+PE3HB3HqHqC+fv7Z2dn90DTLj6+13F1xCUlBa+ggaOj07ZteDUt583DK4gQcsE9u9zQ1dVxyxa8mo6ffYZXkCZ0yAdDCPn5+d29e1f78QAATTDjgxm1tKhMsICAAD6fL+u+vwE+GEbAB8MIMz7Y1PJylZ0cHA7H2tr6/v373dUEHwwj4INhRId8MIqAgID8/Pxui4IPhlcTfDBM6FY3PUIoMDCwJwkGADqJDhnNFP7+/pBgQL+BGR+szNgYbxURfDCMgA+GE/pmwqiZDzZ06FBq2KEyYrGYzWYLhUL6AgMAOf1zPphUKu2sDcZisby8vLCvtAMAjMCMD2bS1qZm7Y0e1BLBB8MI+GAYYcYHm1tXpybBAgMD79y50y1N8MEwAj4YRpjxwZBM1lkVESEUGBh4+/bt7omCD4ZXE3wwTDDmg6l5gvF4vG4nGADoJDpnNCOEHBwcWCzWY9zNFQDQPsz4YAUEoX6B0WHDhv3555+aa4IPhhHwwTBCkD1bA0MDOBzOjh07lixZovKlqqoqs86/vhs2bDA3N9+4cSNNsQEARXZ2dnx8fFZWFk36zFQRlfcH68Dw4cPz8vK0Fg8A0AQzPpi5TKandmen7iYY+GAYAR8MI8z4YLFisfo2mJeXV21trcqtn1UCPhhGwAfDyEsJVlxc3NLSghDKzc399NNPt2/ffu/ePexFysRiPYTU70yrp6fH4/G60c8BPhheTfDBMNGeYKmpqcOGDXv+/Pnhw4fHjh2bl5d39erVoKCgs2fP4i2SJEn19UOK4cOH37p1C2/RAKBl2utpH3zwwfHjx+3s7D755JMTJ05EREQghE6fPp2QkDBlyhSMRcpkMk02Vg8ODsae2wCgZdqfJE1NTWFhYQihurq6kSNHUgcnTpxYVlaGt0iWp2dR5y6znODgYM2fYOCDYQR8MJzIJ64sWrQoISFBLBbHxsbu3LmTOvj5559PnDixZzNhOpsPJhAIOBxOl2+XSCQcDqehoaFnpQOAJtA9H6y9ivjVV19FRkY6Ozt7eHh8++23X3/9tUgkYrPZp3H3zqmZDKaIvr5+YGBgXl7e+PHj8QYAAFqjPcE4HM6FCxdu3rxZXFw8f/58S0tLT0/P8PBw7EVKmpqsNOjkQAgFBQXl5uZqkmDiJ09YlpaEoWFvg1NA9OiR4eDBGAURSYorKgycnTFKShsakL6+PoeDURP/hctk4qoqAycnjJLiigqDQYOQZl8kBukY34gRIxYuXLhq1aq33nqLjuxCCDUdO7aopUWTM4ODg3M18zrAB8MI+GAY6WJt+pKSkjNnzsTFxak5p6ioKD09Xfm4WCxua2tTPi4VifQ16EVECAUHB3+m4RLk4IPh1QQfDBNdPGGLi4sTExPVn9Pa2lqvCpIkVS6CrWE3PULI19e3oqJCIBBocjIA6CBdPMGmTZv2qKuRaTwej8fjKR9PTk42MTFRPk5qZjSjF/0cf/7559ixYzU5HwB0jY4JlpmZWVJSUlNTY2Vl5eXlNXr0aA2fNt3Aza3M2FjDc4OCgm7dutVlgg1kH0yPzcarCT4YRtoTrLS0NCoqqrKy0s/Pj8vlCgQCPp/P5XIzMjKGYP3uEiEhlywsNDx5+PDhly9f7vI02B8MI7A/GEbaEyw2Nnbu3LkJCQny+htJknv27ImJidHkK645GvpgFEFBQTt27MBYOgBok/a2UF5e3vLlyxVbRwRBLFu2DPsaoNLmZk2fXwj5+fndv3+/ublZ/WkwHwwjMB8MI+3pFBoaumvXLolCn69MJktOTvbHPdRNcvp0lMY2i6Ghoa+vb5fLJIIPhhHwwTDSXkVMSUmZPXt2UlJSQEAAh8Npamri8/n29vYnT57EW6RMLNbQB6Og7OYuXG/wwfBqgg+GifYEc3Nzy8nJycnJKSkpqa2tpXoRQ0NDsRepuQ9GERwc3LOd0QGAcTp204eEhISEhNBaZHcTLCQkJDk5mb54AIA+ujCa6UA6ePCj7gxO9ff3Ly0tFQqF7M4NH/DBMAI+GE7omwnT2XywK1eujBkzpltSYWFhV69exRQXALTTD/cHk3W1ZpsyI0aMuHnzJk3xAAB9MJBg0uZmbjeXEw4NDf3jjz/UnAA+GEbAB8MIAwlmcP78lPv3u/WWsLCwGzduqDkBfDCMgA+GESaeYCJRd0v18vJ6/vx5dXV156Lgg2HVBB8ME0xswEeS3R2hTxDEiBEj1NcSAUAH6RudHAih8PDwzMxMOuIBAPpgIMHaBg2qsrTs7rteffXV69evd/bqQPbBYF1EnYY+B6AzH+zo0aOzZ8/urlpTU5OZmVlLSwuO0ADgv/RDH6xb88HkmJqa+vn5qe9LBABdg4lOjpYWsx51fI0fP/7ixYsqXwIfDCPgg2GEgQQzu3Zt9N27PXijmgQDHwwj4INhhIknmETSs1LHjBmTm5urenYz+GB4NcEHwwQzKw/3oJseIWRqahoSEoJ3gRAAoBUGEiw4OHjEiBE9e29ERMTvv/+ONx4AoA8G5oNxAgIMVS1Iqgl/+9vfFi9erHx8IPtgMB9MlyHIbg5s1xwOh7Njx44lS5Zg1JRKpfb29nfu3HHsCzcX0H2ys7Pj4+OzsrJo0tf13V86oK+vP2HChPPnzzMdCABoBBNjEYVCydOnPX57RETEuXPnOhwEHwwj4INhhIEEq09Nrfroox6/fdKkScpPMPDBMAI+GEaY8cF642B4enqyWKzi4uKXjoIPhlcTfDBM9LE2GMWECRMuXLjAdBQA0DV9MsHGjx9/6dIlpqMAgK5hwAcz8vFB3R9Nr8j48eMTEhJIhZnR4INhBHwwnNA3E6az+WBYcHNzKygooEkcGDj0w/lgWBg3btyVK1eYjgIAukBdgi1YsICOInvpg1GMGzdOsRkGPhhGwAfDSHuCnVAiLS2N+g/eInvpg1GMHz9ecVg9+GAYAR8MI+2dHBs2bCgqKvL19VWcz79p0yaEUGRkJMYie+mDUbi7uxsaGhYUFPj6+iIEPhhuTfDBMNGeYLdu3VqzZk1hYeH333/v6uqKECIIIj8/n7nYumDSpEnnzp37b4IBgE7SXkVks9l79+6Nj4+fOHHiwYMHmQtJU6ZMmfLbb78xHQUAqKOjDxYZGRkWFhYdHY296SWn9z4YRURExNKlS1tbW42NjcEHwwj4YDhR2Xkvk8m++uqr+fPn98YBoNUHoxgzZkxGRgatRQD9G2Z8MIIg3n333dTUVC1ne3d5/fXXT506xXQUANApXQyVKikpOXPmTFxcnJpzCgsLVdYnxWJxa2ur8nGZUCgTClk2Nt0KVCWvv/76pEmTkpOTJTU1LEtLwtCw95pyRI8eGQ4ejFEQkaS4osLA2RmjpLShAenr63dnS94uwX/hMpm4qsrAyQmjpLiiwmDQINSj1ZO0SRfxFRcXJyYmqj9HJBLVq4J6RCqfj8UHo/D29uZwOLm5ueCDYQR8MIx08QSbNm3ao658/cDAwMDAQOXjycnJJqoWt8Hig8mJjIxMT0+PBR8Mryb4YJjomGCZmZklJSU1NTVWVlZeXl6jR4/u7l5eWubNN9+Mjo6Oxb3DCABgoT3BSktLo6KiKisr/fz8uFyuQCDg8/lcLjcjI2MI7h5wjISEhAgEAoFAYMF0JACgTHuCxcbGzp07NyEhQb7sLkmSe/bsiYmJwbuYLi4fjIIgiDfffDO/uNgLfDBMgA+GE3mHvbm5eV1dXYdefIlEYmNj0zMHQAs+GMWVK1d4PJ4WCgL6H9rzwUJDQ3ft2iVRaDHLZLLk5GR/nW/evPrqq7W1tUVFRUwHAgAdaU+wlJSU06dP29nZjRs3bsaMGePHj3dwcEhJSTlw4ADeIrHMB1NET09vwZQp6UePYtREMB8MIzAfDCHk5uaWk5Pz22+/LV++fMqUKUuXLj19+nR+fr67uzveIjH6YHJmP3hQinuAMvhguAAfrJ2QkJCQkBBai8Trg1HYWVvX5+aWlZV5eHjg0gQfTHcF+44PpusjTTSEIIjQ0NBjx44xHQgAvEQ/STCEUHh4+I8//sh0FADwEgwkmJGPDxt3LdR05MhhM2ZUVlbeu3cPl2Zf8cFMcHfzgg+GkT62P5h64uLibGxsPvzwQ62VCPR1YH+wbrBgwYLDhw8zHQUAtNP39gdTCbUuYlhYmFQqzcnJwaIJPhg2wAfTJnT4YNR8MIIg/v73v3///fdYNMEHw8VA9sH63v5gqnlhB/3jH/84cuRIW1tb7yXBB9NdQfDBmMLNzY3H46WnpzMdCAAg1P8SDCG0ZMkS7OMnAaBn9B8fTL4u4qxZs27duvXw4cNeaoIPhgvwwWhB+z6YnBUrVjg6Om7cuFH7RQN9C/DBesKiRYt++OEH+n47AEBD+pUPJv8zNDRUX18/MzOzN5rgg2EDfDBtQp8PpngkJibm22+/7Y0m+GC4AB9Mq9Dqg8mJjo5OT0+vr6/vsST4YLorCD4Y49jY2Lz++uspKSlMBwIMaPptgiGEVq9e/dVXX4nFYqYDAQYu/dAHkzNs2DAfH58jR470TBN8MFwMZB9M9f5gWNDauohqyMjIGD58OLMxALoMM/uD9RumTp3a1tZ28eJFpgMBBij90weTQxDE6tWrd+zY0QNN8MGwAT6YNtGODybn786TsKsAABC4SURBVH//+40bN3qw7i/4YLgAH0yraMcHk2NiYrJs2bIvv/yyu5Lgg+muIPhgOsW777577NixyspKpgMBBhwDIsFsbGwWLVrU5V64AICd/uyDKbJ+/foffvihWw8x8MFwAT7Yf/njjz+2bt164cIFqVRKHWlqatq6dWvPHABd8MEUWbdu3TvvvMN0FIBuoT0f7NChQ5MmTcrKyvrHP/6xbNky6mBTU1NCQgJDuY+ZhISEo0ePFhcXMx0IMIBoT7B//vOfx44dS09PLywszMnJ+e6772gqUps+mCLW1tbr169fv369hprgg2EDfDCEUHV1dXh4OELI1NT0u++++/DDDwUCAR1FatkHU2TlypV3797VcGAH+GC4AB8MIYSGDRsmn6HI4/HefPPN2NhYCXaPRes+mCJGRkafffZZQkICqcFqAuCD6a5gX/TBdu/e/cknnzg6Oj5+/Bgh9K9//evp06ehoaHMxUYLc+bMEYlEp06dYjoQYEDQvsMlj8crLy+/evUql8tFCJmamp4/f/7MmTM3btxgLjz8EATxySefbNq0acaMGXp6A8IGBBjkpW8Yl8udMWMGh8P572t6ejNnzty8eTPeIhnxwRSZOXOmpaXl/v371Z8GPhguBrIP1sW6iCUlJWfOnImLi1NzTkFBwcmTJ5WPf/TRR4mJie+++25vY6SB/Pz8iIiI/Px8Ozs7pmMBmIThdRGLi4u7HGEklUrrVUEZbfhCxUlAQMCiRYvWrVvHdCBAf4c+D7uzkRzS5mZxbS3eskTV1bK2tm69pampydXV9ffff+/shLaHD3sd18vIZKJHj/BKSurrJQIBXk38Fy6Vih4/xispevyYfDHeqDdoe0ZzZmbmDz/8kJiYeODAgatXr5I0PIIY9MEUMTU13bdv39KlSxsaGlSeAD4YLgayD9bei1haWhoVFVVZWenn58flcgUCAZ/P53K5GRkZQzTuP9AEBn2wDkyZMmXGjBnLly9PTU1VfhV8MN0V7Is+WGxs7Ny5c6urqy9evJienn7hwoWqqqpVq1bFxMQwGB/dfPHFF/n5+T1eeQoA1NOeYHl5ecuXL1e0hgiCWLZsGZ/PZyIwLWFsbHzgwIH4+Pg6rLVBAKBoT6fQ0NBdu3Ypjo2SyWTJycn+uG0Wxn2wDoSGhs6bN2/NmjUdjoMPhouB7IO19yLev38/ODjY0tJy7Nix06dPHzdunK2trb+/f1lZWc/6T3RtPpgampqaPD09f/zxR6YDAbQN3b2I7Z0cbm5uOTk5OTk5JSUltbW1VlZWXl5e/W8sokpMTU2PHDkydepUT0/P4cOHMx0O0H9gdfg7JCQkBHf9rQMyoVAmFLJsbDBqip88YVlaEoaGPVYICgrat2/f66+/np2d7eTkhBASPXpkOHgwvhgRIklxRYWBszNGSWlDA9LX138xug0L+C9cJhNXVRk4OWGUFFdUGAwahHR+NGn/XxdRc95444133nln/vz5UqkUgQ+Gj4Hsg/X/dRG7RUJCApvN3rJlCwIfTJcF+44P1rGKOMAhCOLgwYMhISEjR450YDoYoB+g61VY7TNo0KCjR4++9dZbdMzmBgYaA2VdxG4xatSoAwcOnG1tLcK6/Av4YBjpez4YdvqQD6aSQ4cOubi4lJeXMx0IQCPa88GADixcuLCurm7y5MlXrlxxcIAWGdAT+vn+YL1B9OjRypUrFy1a9Nprr+FZwQ7WRcRH31sXUWvorA/WAcoH27hx45gxYyIjI1taWnopCD4YRsAH6xRd9sEUkdtWO3fudHJymjt3rlgsxiKIE/DBdBvopu8aPT29gwcPslisefPmtbW1MR0O0JeABNMIFot15MgRIyOjMWPGlJSUMB0O0GcAH6xTOswHMzQ0PHz48KJFi0aNGpWent4DQfDBMAI+WJ/3wTojNzfX2dk5KSmJ6UAADGh7VSmgS4KCgq5evbp///633nrrOe4ucqCfAT5Yp6ixg9zc3LKzs01MTEaMGHHnzh1NFcEHwwf4YJ3St3ywzl41MTH55ptvPvroo4iIiKSkJJkGHzb4YBgBH6xT+pwPpoYFCxZkZmb+9NNPYWFh2dnZvRfsNuCD6TbQBustnp6ely5dWr16dVRU1OrVq4VCIdMRAToEJBgGCIJYuHDh3bt3nz59OmzYsN9//53piABdgYHR9EY+PkhfH6+mFnywLrG0tDx06NDp06dXrFjh4eHx+eef83g8xRNo8sH02Gy8muCD4YQ+B6C/+mBdIhKJkpOTBw0aFBkZee7cOabDAdQBPljfw8DA4J133iktLZ0+fXpcXNyYMWOuXbvGdFAAM4AP1im9tINMTEyWLl16586d2NjYxYsX83i8z7dufaK5aaYZ4IPpOOCDdQqWdRH19PSio6OLi4t3795df+PGheDg9evXV1VVYYkQgQ+m8zDQydGffDANIQhizJgxwaampSUlWa2tfn5+YWFhs2fPjoqKsrCw6JU0+GC6DbTBtIqBgUFSUtLjx49jYmJ+/fVXNze3uXPnHjp06MmTJ0yHBtBCxydYZmZmSUlJTU0NtfnD6NGjCYJgJLJ+DJvNnjNnzpw5c+rr648fP56enr5q1SovL6/XXnttypQpoaGh+rhtDIApGNhCtr/6YF2i7INZWlrGxMTExMRIJJIrV6789ttvy5cvf/z48ZgxY8aOHTtq1KigoCADAwM1muCD6TryDvtx48Zt2bJFqrBzu0wm271799ixY3vmAAxYH6yXVFZWHj58eMWKFTwej8PhREREfPzxxxkZGTU1NUyH1g+h2wcjSJKkMo3L5ZaXl1taWiqmn1QqdXBwqO1RnxKHw9mxY8eSJUsw/AwMVAQCwZUrVzIzM2/evJmTk8NisXx8fMLDw8eOHRscHOzYJ37CdZvs7Oz4+PisrCya9NuriNQWsps2bWKx/nuQpi1kdXN/MGV0YX8wc3PzGTNmzJgxg/rzyZMnBQUF169f37NnT25ubmtrq7+rq6uHh6uvr5eXl7u7u4uLi4uLi2Hv7gPsD4aR9gRLSUmZPXt2UlJSQEAAh8Npamri8/n29vYnT57EW2R9aqrw1q3ByckYNR/Hx1vOnm2BdbZVUWio719/sayscAm23LnzYOnSoTdu9FjB3t7e3t5+/Pjx1J8NDQ1l7733rLX1prn5tWvXDh069PDhw4qKCgcHB3d3d1dXVzc3N3d3d0dHRwcHB2tra2trayMjoy5LKQoP983LY9na9jjODjTn5FSsXet99SouQYTQ/XnznHfsYI8YgVGTDhjYQnYA+mA0CVpYWDjY2jpyOJPff19+UCqVPnz4sKys7OHDh+Xl5RcuXKiqqqqqqnr27NmzZ89MTEyoLHV0dLSzs7OwsOBwODYvsLKysrKywv8BDWAfjIEtZAFa0dfXd3d3d3d3V/lqfX19TU1NTU1NRUVFTU1NY2NjTU0Nn8+vra199uxZXV1dXV1d6rNnr7m5tbDZFhYWpqamRkZGVMucxWJZWFhYWFhwuVxLS0sWi8XhcBBCBEHI7XIzMzMzMzM2m21gYGD2og+W/fy5RCKpr69HCHG5XD2dr9dhpIuRHCUlJWfOnImLi1NzDp/PP3XqlPJxkiTFYrHyLlsymUwmk+HdfYskSalUin1HL4lEgvBpUj20eIOUyWSoOzeTw+FwOBz1vkvB4MF/Xbsm4XIbGxubm5vb2tqopfnFYnFjY2NjY6NAIGhsbBSLxeXl5VQM8rX7m5qampubW1tbxWJxc3MzQogkyUG1tdH19a/xeAihxsZGkiSNjIxMTEyot7DZbMVGo6GhIVuV8cDlchX/XFpUlLR+vcDVtctLlusvXbr0lVdeUT6BVtexiwQrLi5OTExUn2AIIerHqQNmZmZOqtq1ht7e2H0wdlgYdh/MfOZMvD4Yy97ebOJEjIIIIePAQOw+GGf6dLatrR6b3dthXC8QPXhQt39/2ebN8iNtbW3ytf6bm5sV1yQXiUQqZ4U3NjYq/mnC4bw5fXqDsTFCiMViqfmJEQqFIpEIIWSLr1WpOe3d9NgJDw/fsWPHyJEjadIHgN6jvW56ChgqBQAYYWCoFPhgGCWlDQ1IX1+fw8GoCT4YRtrji42NnTt3bnV19cWLF9PT06nu3VWrVsXExOAtckDNB1ME1kXESF+ZD9aeYHl5ecuXL1fsQiUIYtmyZXw+H2+R4IPhBOaD6Tbt6UQNlVLsjaFpqBQADBzoHSpVWFioPNuipbCwic+/cuhQj2WVcX7wIP/qVQHWZTm8WlqOHTsmwde8MS4vd6yrO4T1wm3u3GnV12/Cqund0vLTTz9JXvadegO7pMS6ulpw8yZGi5lobi4sLERq5/JoQmFhIZZ4OqNjNz3GoVJr1669fPmy8nGr+/c5DQ2/Ghv3WFmZeRLJn3p6RVibvGuFwq9MTET4OlFtSHKuRLKn198JRSaIxUKZ7A8NRhhqznqRKMnQsBWfoANJTmtt/c3Li4Pv1+qNiorLtrZ1OLq1Jk6cuG3btt7rqIa+mTCdsXfv3rffflv75XYXd3f3srIypqPogrS0tHnz5jEdRdcMGzYsLy+P6SgYQNd7OQGgTwMJBgA0AgkGADQCCQYANAIJBgA0AgkGADSi//HHH2u5SBsbGy8vL2esY17pwMXFJSQkRMfXALWxsfHw8HDVYN4hszg5OY0YMUL9Go/9EhrngwEAAFVEAKARSDAAoBFIMACgEUgwAKARSDAAoBFIMACgEUgwAKARSDAAoBFIMACgEW0nWHl5+eTJky0sLCZMmFBaWqrl0jXk9u3bY8eONTc39/b2xruEBh0IhcKhQ4eeOHGC6UBU8/z58+joaDs7uyFDhqSlpTEdjrbRdoLNnj371VdfvX//fkRERFRUlJZL14TGxsbx48fPmjXr0aNHSUlJy5Ytu9GLHb20wJo1a4qKipiOolNmzZrl4OBQVFSUnJwcExNTXFzMdETaRZvrE+Tk5FhaWorFYpIkpVKpra1tZmamNgPQhFOnTg0aNEj+Z2Rk5NatWxmMRz3p6enjxo3z9fVNT09nOhYV3Lhxw97envrESZJ88uSJUChkNiQto9Un2L179wICAqgtavX09Pz8/O7du6fNADRh3Lhx8kdWc3Nzbm4uHbsQYqG6unrNmjUHDx7U2SH/xcXFHh4ey5cvt7Oz8/X1PX/+vHzXogGCVhOstrZWceEuLpdbU1OjzQA0gcPhUFNpqJbY1KlTx44dy3RQKiBJMjo6+sMPP3Rzc2M6lk559uxZVlaWn59faWlpUlLSkiVLbt26xXRQWkWrCWZlZdXU1CT/UyAQWOHbARkjQqEwLi5u2rRpK1asSElJ0c3nQ3JysrGxcXR0NNOBqMPS0tLb23v16tUcDmfy5MlTp07NyMhgOiit0sUGfHjx8vLi8/kymUxPT48kyYKCAi8vL20GoAkymWz69OnW1tYFBQXm5uZMh9Mp169fP3HihHxzqVmzZs2fPz81NZXZqDrg6empuBi7VCo1xrrgbB9Ay20+Pz+/pKQkqVSanJzs7e0tk8m0HECXnDx50tHR8fHjx9UvaGpqYjqoLvD399fNTg6SJP38/LZt29bc3Hz69GkOh1NcXMx0RFpF2930J0+e/Omnn+zt7dPS0s6cOaODu/vdvHmzsrLS2dnZ4QW7d+9mOqg+zOnTp3/99VcHB4cPPvjg6NGjOlhnoRVYMgAAaASGSgEAjUCCAQCN/D/M7Disb931+wAAAABJRU5ErkJggg==" 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" 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" 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/diTQ3p3QLAQiSspj937tzHH39s2F5RUVFTU0P6ARCAhwgJq+m9vb0XLVpk2J6Xl9fR0WHYDvNggDt0BVZeXn7kyBHD1fQbN24cvAsnJyejBRYfH8/jGbklgc358zMvXRpmvgA8VBhYTY9pNHA9GOAIBlbTA8AdDKymR3BrRMAZ+udIYrFYLBZTGrLP2fmmSERpCABY4j63bZPJZBKJhNyQ3X5+v3p6ktsnAOx0nwKrrq5OT0+nJxUAHj33KbCIiIi6ujqSQ8I8GOAMBm48al1SEnb5Mrl9AsBODNx4FObBAHcwceNRADgDbjwKAIUYWCrV5+zcaGdHbp8AsBMDS6V6Jk78pbyc3D4BYCdmlkoBwBEMLJUy6euz7OmhNAQALMHAEy75P/74FNkDJwCwExOPkIX7IgLOYKDAAOAOZh6CTn9QABjBQIGpXFwabG3pjwsA/RgosF5///+6u9MfFwD6wTkYABRioMBgHgxwBwMFZnnu3OyqKvrjAkA/ZubB4HowwBFwDgYAhZgpMLgvIuAIBgpM7eoK82CAI4w8nOFB9fT0NDQ0GLYPdJjqCwj4edy4kccFgP1IKLDvvvsuJSXFsF2pVLa1tY28fwAeXiQUWGRkZGRkpGG7QCCwM3ZrAJgHA9zBwDnYqKKiudXV9McFgH4wDwYAhWAeDAAKwfVgAFCImXmweqGQ/rgA0I+JCy4DAy+4uNAfFwD6wTkYABRi4hyst5ff20t/XADox8w82DyZjP64ANAP5sEAoBCcgwFAIbgeDAAKMVBgmnHj6mxs6I8LAP2YmGieNOm8szP9cQGgn/7lKiUlJTKZrLm5mXg+2MyZM2FlEwDDpiuwmpqa6OjoxsZGf39/oVAol8ulUqlQKDxx4oSXlxeJIWEeDHCHrsASEhIWL168du1a7XPQcRzfuXNnfHx8UVERiSHNzpyZ/9tvJHYIAGvpzsHKy8uTk5O11YUQwjAsKSlJSvbD8mAeDHCHrpxCQkIkEolKpdK2aDSazMzMgIAAJhID4FGg+4iYlZUVExOTkZERGBgoEAgUCoVUKnV0dMzNzSU3JIyaAO7QFZiHh0dZWVlZWZlMJmtpaSFGEUNCQkgPqRk37oZAQHq3ALDQPcP0P/3005kzZ0JCQhYvXkycjHV2du7YsWPNmjUkhtQEBZU4OZHYIQCspTsHO3DgwJw5c0pLS5cuXZqUlEQ0KhSKtWvXMpQbAA89XYH94x//yMnJOXr0aFVVVVlZ2Z49eygKCfNggDt0BdbU1DR9+nSEkJWV1Z49e9LS0uRyORUheadPP3f9OhU9A8A2ugKbPHnyF198Qfw/KCjoxRdfTEhI6D9qTxaYBwPcoSuwHTt2bNq0ydnZub6+HiH0r3/9648//qBiFBEA7tCNIgYFBdXW1p47d04oFCKErKysvv/++/z8/J9++on0qHA9GOCIe4bphUKhSCQ6cuSIdjX9888/b/TBDiOBu7ldt7Ymt08A2ImE1fTFxcUSicSwvbu7u6Ojw7Adnzy52NFx5KkDwH4krKYfP378okWLDNvz8vIsLS3JTReAh4uuwMrLy48cOWK4mn7jxo2Dd+Hs7Gy0wOLj43k8I88fw3p7rfr6hpkvAA8VBlbTm5w69fyNG+T2CQA7MbGaHubBAGcwsJoeAO7QP0cSi8VisZjSkHA9GOCO+9y2TSaTGR2CHwncza3WyorcPgFgp/sUWHV1dXp6OskxH3/8nIMDyX0CwEr3KbCIiIi6ujp6UgHg0cPAjUdhHgxwBxM3Hv2//1tQX09ihwCwFgM3HkVqNcyDAY5g4MajAHAHA0ulYB4McAcDS6WQuzvMgwGOYGCpFBYcXDR6NOndAsBCDCyVAoA7GHjCJcyDAe5goMDwkydfuHmT/rgA0I+JIxhcDwY4g4ECQ3DbNsAZzBQYABzBQIGZjB//O59Pf1wA6MfEOVhw8FmRiP64ANCPiQLDMDgHAxzBxDlYT481BQ9tAYCFmJkHi751i/64ANCPiQJTqWAeDHAEM+dg9AcFgBEwDwYAhYw8nOFB9fb21hu7x8ZAQ4Um48fXWFiMPC4A7EdCgRUUFKSmphq2K5XKtrY2w3aTKVPO2NmNPC4A7EdCgUVFRUVFRRm2CwQCO2OFBPNggDuYGEXs7hao1fTHBYB+DBSYprAwprmZ/rgA0I+JUUS12pSBqAAwAK4HA4BCMA8GAIUYKDBTT0+YBwMcwUSBicXfC4X0xwWAfnA9GAAUgnkwACjEQIGpCwr+dPs2/XEBoB9cDwYAheB6MAAoBPNgAFCImXmwa+bm9McFgH76l6uUlJTIZLLm5mbi+WAzZ84k/RMdb+rUUzY25PYJADvpCqympiY6OrqxsdHf318oFMrlcqlUKhQKT5w44eXlRWJImAcD3KErsISEhMWLF69du1b7HHQcx3fu3BkfH19UVERiSJgHA9yhOwcrLy9PTk7WVhdCCMOwpKQkqVRKbsi+/PyXjN1KAIBHj66cQkJCJBKJqt89dzUaTWZmZkBAAMkx4XowwBm6j4hZWVkxMTEZGRmBgYECgUChUEilUkdHx9zcXNKjwjkY4AhdgXl4eJSVlZWVlclkspaWFmIUMSQkhMHkAHjY6Q/Ti8VisVhMbUgvL5gHAxzBwESzWUjId9bW9McFgH7/O4I1NDTk5OQM9KKVK1eSGBLmwQB3/K/AzMzMLl++/MUXXwQGBnp6euq9iNwCw7u7bTQaEjsEgLX+V2BjxozJysoiFnO8/vrrlIbsPX785fZ2SkMAwBL3DHIkJCTY2tpSHRJXqWAeDHDEPQUWGxtLQ0i4HgxwBwOr6QHgDhJW05eWlu7du9ewvaenR6FQGAnp5VVtZjbizAF4CJCwmt7NzW3KlCmG7V9++aWFsRuMmk+bVsjnjyxtAB4S+F02Njatra34vVQqlb29PT4s1tbWWVlZhu2NjY1jx44dXp8AkKu0tDQ0NJS6/hlYTQ/zYIA7GFhN352b+0pHB7l9AsBODKymh/siAu5gYDU9jPsD7rjPanqZTCaRSOhJBYBHz30KrLq6Oj09ndyQZo89dpWnf+QE4JF0nwKLiIioq6sjN+So0NACS0ty+wSAnRhYKgXXgwHuYODGozAPBriDgRuPKo8d+0tnJ4kdAsBaDNx4FK4HA9zBxI1H4b6IgDOYufEoABzBwFIpc29vmAcDHMHAUimL6dPzR42iNAQALMHMM5rhHAxwBAMFhnd3C6HAADcwUGCdR47EdXXRHxcA+jFxBIN5MMAZzJyD0R8UAEYwUGAAcAcDBWbu7V1pAoUNOIGBX3TLGTOOwwP4ADfAPBgAFGKgwDRKJcyDAY5gZh4soaeH/rgA0A/mwQCgEDOjeXAOBjiChMtG+vr6jN55CqoIABIKLD8/f9WqVYbtSqWytbXVsH2Ujw/MgwGOIKHAFi5cuHDhQsN2gUAgEokM2/lPPJELF1wCboB5MAAoBPNgAFCIgQLr+OabV/v66I8LAP1gHgwACsH1YABQCIbLAaAQAwU2ysdHCgcxwA0MFJjVzJlHYaIZcAPMgwFAIWbmwWzpjwoAExgoMHlOTqJaTX9cAOgH82AAUAiuBwOAQjCaBwCF9C8bKSkpkclkzc3NxPPBZs6cSfrCC4sJE66Q2yMAbKUrsJqamujo6MbGRn9/f6FQKJfLpVKpUCg8ceKEl5cXiSGtZ806AhPNgBt0BZaQkLB48eK1a9dqn4OO4/jOnTvj4+OLiopIDAnzYIA7dOdg5eXlycnJJv3WWGAYlpSUJJVKyQ0J82CAO3TlFBISIpFIVCqVtkWj0WRmZgYEBJAbsv3w4WQ4ggFu0H1EzMrKiomJycjICAwMFAgECoVCKpU6Ojrm5uaSGxLmwQB36ArMw8OjrKysrKxMJpO1tLQQo4ghISEUBcZxHC4MA488/Xmw3t7e3t7enrtgNAKAkWBgmN5iwgQphsERDHABA8P0MA8GuIOBYXoEU2GAMxgYpod5MMAdDAzTt2VnJ+M4HMEAFzAwTA/zYIA7GBumhyMY4AIShukvXLiwe/duw3a1Wt3d3d3/pI6g0WjMzc1TUlJM4N5SgEZ//etf/fz8DNtNTSn8REXCML2Tk9OUKVMM248fP+7j48MzeFIR389P/Je/2AcFjTh5AB6Ag4OD4W8jQkhN6R1i8LtsbGxaW1vxe6lUKnt7e3xYQkNDS0tLh7ctAPQoLS0NDQ2lrn8GhukB4A4Ghuk1SqWms5Nnb09utwCwEAPD9G3Z2Z3nz7vt2kV6zwCwjf45n1gsFovFlIbEVSoENx4F3HCfgXKZTCaRSOhJBYBHj5FRy/6qq6vT09NXrlw5vN6rqqrMzMz0GpVVVZ2VlT8cODC8Pmmg0WjkcrmtLXuXTOI43t7ebmdnx3Qig2ltbRWJRExnMZiOjg5LS0tKQ9ynwCIiIurq6obX9YwZM3bs2GHYbldba9vWdqK8fHjd0kCtVqtUqlGjRjGdyIBwHO/p6bGwsGA6kcEolUqqf31HSKlUBgUFhYeHUxdC/7IRGm48+vnnn1+4cCErK4vcbkl08uTJjz76qLCwkOlEBiSVShctWnTlCnvv4NrR0eHi4iKXy5lOZDC2trbXr18XCoXUhWDgimYAuIOBK5oB4A5mrmgGgCNgqRQAFGJgqRQA3MHYjUcB4ATqFuoP5Pr16yy/jOX27dvfffcd01kMRqlUHj16lOksBqPRaLKzs5nO4j4OHz6s0WgoDQG3TwOAQnDRPgAUggIDgEJQYABQCAoMAApBgQFAISgwACgEBQYAhaDAAKAQFBgAFCKnwGpra8PDw21tbZ9++umamhpSNh9hnwihixcvhoWF2djY+Pj4HHjwW4AMsnlXV5evr++xY8dISVWvN1Zl2NHRERcXN2bMGC8vr0OHDrEwQ4RQcXFxSEiItbW1WCz+4Ycf2JUkKQuupkyZsmHDhtbW1s2bNwcFBZGy+Qj7bG9vt7W13bZtW3t7e0FBgZWV1YULF8jaPDExESGkXQ04wlT1emNVhrNnz16zZk1ra2tBQYGlpeXVq1fZlmFXV5dIJNq8eXNTU9OWLVvs7Ow6OzvZkyQJBVZWVmZnZ9fX14fjuFqtdnBwKCkpwXH80qVLYWFhQqFwzpw5+fn5/TfZvXt3U1PTIJsP1OfQ5eXljR07VvtlVFTUli1bhp7VQJvjOH706NEnn3zSz8+P2O8jTFWvN1Zl+NNPPzk6OhIb4jh+69atrq4uVmWI47hUKjUxMVEoFDiOd3d3m5mZXbp0iT1JklBgBw8eDAsL03751FNP7du3T6FQjB07Nisrq6Oj48iRI3w+v7q6Wvua+fPnX758eZDNjTY+UFZyubyuro74v0KhcHV1PX369NCzMro5juM3b9709PT8/fffAwICiP0+klQNe2NVhgcOHJg+ffry5csdHBx8fX2/+uortmWI43hfX5+7u/v7779/69atrVu3urq69vT0sCdJEs7BWlpaBAKB9kuhUNjc3Jyfn//YY48tX77c2tp64cKF8+bNO3fu3NA3N9r4QFkJBAJXV1d09xP2/Pnzw8LChp6V0c1xHI+Li0tLS/Pw8Bg8/6FkaLQ3VmV4+/bt0tJSf3//mpqajIyM5cuX//LLL6zKECHE4/F27Nixbt06Jyent956KyMjw9zcnD1J3ue+iEMhEokUCoX2S7lcLhKJamtrf/nll/Hjx2vbw8LCvv7669TUVCLXOXPmmJmZTZgwYdmyZYabjxo1yrDxQRPr6up65513cnJy3nvvvfj4eITQ0LM6deqU4eY7duywsLCIi4u779sfSnqZmZmGvbEqQzs7Ox8fn5SUFIRQeHj4/PnzT5w4YW5uzp4MEUIXL15MTEw8efLkrFmziouL4+Lixo0bx6LdOMQD8SAuXLjg4OCgVqtxHNdoNE5OTj/88MO+ffumTZumfU1FRcWtW7e0X/Y/Rhvd3GjjA2WlVqufeuqp6OjoO3fuaBuHnpXRzZcsWaK395YsWTLsVI32xqoMS0pKPD09tV9GRUVt3bqVVRniOP7BBx+8+OKL2i9felkCEEYAAAEBSURBVOmlTZs2sSdJckYR/f39MzIy1Gp1Zmamj4+PRqMhbjqwf/9+hUKRm5s7yIdgo5sP1Dh0ubm5zs7O9fX1TXcpFIqhZ2V08/79az+ajzzV/r2xLUN/f/8PP/yws7Pz+PHjAoGgurqabRmWlJSIRKJjx461t7cfP37c3t6+qKiIPUmSU2A1NTVPPvmkvb39rFmzrl27RjQWFxdPnTqVz+dPnDhRbwxar8CMbm60cejS0tL0/ggRo0NDzGqgzbX67/cRpqrXG6sy/P3332fPni0QCCZNmlRYWMjCDHEcP3z4cGBgIJ/PDwgIIEZi2JMk3DIAAArBUikAKAQFBgCF/h+XiB2fyGduCAAAAABJRU5ErkJggg==" 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" 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" 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6QzdaKlpVVfX9/c3Ex/HwwAhXXx4sUdO3ZcvHixU9WFEMIwTEtLq6amhqRgHYItGCCFHK8He/78+dKlS0+dOkXcfbuzOBxOdXU1G8dpuR4MtmCAFBWhoa/i47u/HhzHPT09V6xYMXHixK6tQVtbu6am5nlw8OsPN/KlEhQYIIW8+mAHDhx4+fJlYGBgl9dAbMEUpQ8GgOLIz8/fvn379evXGQxGl1dCbMHkmKpTYAsGFFRVVZW7u/u+ffvMzMy6sx42m/327Vt5peos2IIBUnSzD9bc3Lxo0SIXF5dF3T7cr6mpWVdXB30woFS62Qfz8/Nrbm4OCwvrfhINDY26ujr91au7v6ougAIDCufYsWOZmZk3btxQVZXDv0+iwLq/nq6BAgOk6HIfrKioaOPGjZ06H0o6osDomhcRDnIAUnStD4bj+IoVK4KCglqmW+s+osDo6oPBFgyQomt9sFOnTgmFwpUrV8oxCVFguIoK9MFAryYUCr/77rujR4/Kd0bN9/tgNE3iDV8RgaI4evTosGHD5D4hNBzkAEqos32w2trakJCQs2fPyj0JUWDQBwNKpbN9sMDAwMmTJ5MxXcX7Phjt9wcDgC6nTp1KSUn5888/yVi5mppafX09GWuWBeyDAVLIPi/igQMHNm7cmJ6erq+vT0YSFovV0NBA17yIUGCAFDL2wQ4ePLh3795r166RN78tsQWDPhhQKrL0wQoKCrZt25abmztkyBDykhBbMLquB4MtGKDNunXrgoODhw4dSuooampqDQ0NpA4hBRQYoEdWVlZ5ebmnpyfZA7FYLBoPcsBXRECKDvtgO3fu3LJlS58+fchOQmzBoA8GlIr0Ptiff/5ZUlLi4eFBQRLiIAfMiwh6kdDQ0PXr13dnpg3ZqampCYVCHMcpGEsSFBgghZQ+WElJyZUrV77++mtqkmAYxmAw6p4/hz4YUB5S+mA//PDDmjVrtCg8vZ3FYkEfDCiVj/XBioqKzp8/v3//firDqKmpNQuF0AcDyi84ONjHx0dbW5vKQVksVhMd1YVgCwao9PDhw0uXLsXFxVE8rpqaWnNzM8WDEqDAACna7YPt3bvXy8uLyr0vApPJxEeMYI0cSfG4qE2BlZSUDBw4UF1d/c6dO5mZmerq6rNnz+7mvKqgd5Lsg7158+Z///tfYWEh9WGYTGaziwvb1pb6oVv3wU6dOmVjY/P27duTJ09OmDAhLy/v2rVrdnZ2Fy5coD4WUD6JiYkzZ840MjKifmgmk9nY2Ej9uEh8C7Z169azZ88aGRkFBQWdO3du6tSpCKG0tDR/f/9PP/2UlnCg52ozLyKO43FxcQkJCbSEYTKZja9eiWpr6ZwXUSAQjB07FiFUWVk5btw44sHJkyc/evSI4kxACbTpg126dEldXd3JyYmWMEwmUzUhgeb7g82cOXP37t1NTU2zZ88+cuQI8WB0dDQZ0yQApdemDxYVFbWaptnhEbEPRlMfrPUr4v79+11dXU1MTIYOHXrkyJEDBw40NjZqaGikpaVRHwsokwcPHnC53KSkJLoCMJnMZtr7YGw2Oysr6/bt2yUlJQsXLtTV1R0+fDhd23SgTIKCgjZs2MBisegKwGQyRbQf5CA4ODg4ODjQEgUok5Y+2O3bt69fv07X4Q0Ck8msHTiQ/j6YJD6fn56evm7dOinLnD171s/PT/Lxurq6ysrKbqUDPRbRB8Nx3MfHJygoSENDg8YwDAbj5bhx7KlTqR+6gwIrKSkJCwuTXmCzZ8+2traWfNzKykpPT69b6UAPd/LkyYaGhq9outixhUL0wdrl4uLy5MkT6cv06dOn3XlLMAzrei7QwzW/efNOKNyyZcuZM2fkezOHLmAymXh1NS19sLYFlpOTw+fzX7x4oaenZ2Zm5uzsDHUCuqAiNPQ3LnfSpEmKcJyMyWT2T09/zWYbentTPHRrgZWWlrq5uZWVlVlaWnI4nJqaGh6Px+FwMjIyhg0bRnEs0NNVVVbeys3dWVxMdxCEiKOITU0098E8PT3nz5/v7+/fskHHcTw2Nnb58uXZ2dnUJwM9WnZ2trOz84ABA+gOghDRB6PpcpXWL8d5eXne3t7iX5cxDPPy8uLxeHQEAz1YTk5OeXm53O/01WVMJlMkEtEydGs5OTo6RkZGil/4KRKJYmJiyJs0HCirrVu32nl4sG1s6A7yHpPJfGVgQHMfLD4+3t3dPSoqavTo0Ww2WyAQ8Hg8Y2Pj1NRU6mOBnuvq1avPnz+fdfkyBZOKyojJZBabm9PcBzM1NeVyuVwul8/nv3z5kjiK6OjoSH0m0KMFBwdv3bpVcaoLIcRgMIRCIS1Dtz1Mb29vD6fPgy67efMmn8/38PBocz0YvRgMhopAQPP1YAB0344dO/z9/RkMhoz3B6MGg8Gw5XJhXkTQs+Xn53O53DNnziDZ7g9GGQaDUQ/3BwM93Y4dO3x9fWm8LOVjGAwGXYfpYQsG5OPhw4dZWVmHDx+mO0g7oMBAjxcWFvbNN9+0zHnY4f3BqMRgMJ5qaSni9WAAyOLFixdJSUnFYmceSr8/GMUYDMYtY2Na+mCwDwbk4ODBg/Pnzzc0NKQ7SPsUqA8GQGc1NjYePHjw0qVL4g8qVB+MyWQy3r2DPhjokZKSkiwtLf/1r3+JP6hofbBpjx/TPC8iAF0THR29Zs2aNg8qWh8Mhz4Y6Inu3Lnz/PnzmTNn0h1EGhoP00OBgW6Jjo729vZWqFN7JUEfDPRIr169OnfuXFhYmORTitYHK2UwoA8Gepi4uDg3Nzd9fX3JpxStD3aRxVLEeREB+Jj6+vqYmJiLFy/SHaRjNPbBYB8MdFFCQsKYMWMsLS3bfbb5zRuRQEBxpI9hMBisxkZRbS31Q0OBga5oaGjYtWtXYGDgxxZQtD6Yh0AAfTDQY8TFxVlZWUmZUQL6YATYBwOdJhAIdu3adf78ebqDyAr6YKAniYyMnDx5spWVFd1BZKWqqioSiXAcp2Fo6ocEPVp1dXVkZGROTo70xRSqD4Zh2MM+fRjm5tQPDQUGOic6Ovqzzz4bPny49MUUqg+GELrAZLImTqR+XCgw0Anv3r2Ljo7OysqiO0inEa0wdXV1iseFfTDQCSdOnHBwcBgpwzlHCtUHQwjpqao2VldTPy4UGOiE/fv3r127VpYlFaoPhhD6sr6++uRJ6seFAgOyunbtmlAonDJliiwLK1QfDCGkqqIiouMuslBgQFYHDhzw9vbuoXc8VVFRoeUWYVBgQCYVFRXnz59fsmQJ3UG6iK4Cg6OIQCaHDx+eN28eh8ORcXmF6oMhhP7W0MBNTakfFwoMdEwoFB48eDAtLU32lyhaH+yGoaE3HbcNgq+IoGPJyclmZmbW1tZ0B+k6ui4JgwIDHcBxfM+ePb6+vp16laL1wbQRanr7lvpxocBABzIyMkQikYuLS6depWh9sM/Ly/HOfMWVFygwIA2O49u2bfvuu+86e3Re0fpgfTBM4fpgHh4elOUAiunnn39GCM2dO5fuIN1F/2H6c+fOtXnu9OnTCxcuRAi5urpSGgoohqampm+//Xb//v09tLksTkVFhZZrLlsLbPPmzcXFxSNHjhSfRDIgIABBgfVWCQkJJiYm06ZN68JrFa0P9kJPb2i/ftSP21pgf/7554YNG4qKio4dOzZ48GCEEIZhBQUF1GcCiqC+vj44ODg5OblrL1e0Phhv2DDLf96eghqt+2AaGhoHDx708fGZPHlyYmIi9VGAQomPj7e1tZUyrU3Poqqq2qQIk964urqOHTv2q6++ktwlA71HQ0NDaGhod/4NKNT9wRBCWiJRMx19uXaOIvbt2zczM3PKlCnEEQ7QC/34449WVlZ2dnZdXoOi9cH+H4+nc/069eO2fy4ihmGrV69evXo1xWmAIhCJRGFhYfHdKw9F64M5jx+vaWFB/bgdNJr5fH5kZKT0ZZKTk7H2CASCyspK+UUFFDl37pyent6ECRPoDiJPTCaTlnssdVBgJSUl7d6cRpy7uzveHi0tLT09PflFBRQJCwvz8/OjO4WS6OByFRcXlydPnlATBSiC33///dWrV3PmzOnmehStD6ZhZ8c0MaF+3LYFlpOTw+fzX7x4oaenZ2Zm5uzsrARdfCC77du3b9mypfvfphStD6b/1Ve0jNtaYKWlpW5ubmVlZZaWlhwOp6amhsfjcTicjIyMYcOG0RIOUOzChQvl5eVffPEF3UGUR2uBeXp6zp8/39/fX0Xl/Y4ZjuOxsbHLly/Pzs6mKR6gTnNz88aNG3fv3q2qKofr3BWtD9ZUWamipqaiqUnxuK0HOfLy8ry9vVuqCyGEYZiXlxePx6M4E6DF0aNHDQwMZs+eLZe1KVof7HlwMM33B3N0dIyMjBQ/nUQkEsXExIwaNYr6WIBi7969CwoKCg0NldcKFa0PRv/9weLj493d3aOiokaPHs1mswUCAY/HMzY2Tk1NpT4WoNihQ4ccHBzs6ZgWRrm1FpipqSmXy+VyuXw+/+XLl8RRRKU51xNI0djYGB4eDmefkqHt7qy9vT38b6y3SUxMHD16tK2trRzXCX0wAsyL2NsJhcJdu3adOHFCvquFPhgBJr3p7Y4ePWpubu7k5ER3EOUEW7Bera6uLiQkhJjZRr6gD0aALVivFhkZOX78eAcHB7mvGfpgBNiC9V6vX7/eu3dvbm4uGSuHPhgBtmC91549e9zd3Tu8nTnoDtiC9VKVlZXx8fF3796lO4iSgwLrpQ4ePOjq6jpw4ECS1g99MAIUWG8kFApjY2PPnz9P3hDQByPAPlhvlJycPGLECDiNmwJQYL1RdHT0mjVrSB1C0e4P1lRZKaqtpX5cKLBe59atW+Xl5bNmzSJ1FOiDEaDAep3Q0ND169eTPYcZ9MEIcJCjdykoKLh+/fqPdPy/vHeCLVjv8u23327evFlDQ4PuIL0FbMF6kWvXrhUUFCQlJVEwFvTBCFBgvQWO45s2bQoJCVFTU6NgOOiDEeArYm+RkpLy7t07uO82xaDAeoWmpqaAgIDQ0FDxaflIBX0wAhRYrxAfHz9o0KDp06dTNiL0wQiwD6b8qqurg4KCSD3zUBL0wQiwBVN+33///ezZs62trekO0hvBFkzJFRQUnDhx4v79+3QH6aWgwJSZSCTy8vIKDg42NDSkeGjogxGgwJRZXFyciorKihUrqB8a+mAEKDCl9eTJk23btmVnZ1N2aB5Igo9eaXl7e69du3bkyJG0jA59MAIUmHI6ffr0kydPNm/eTFcA6IMR4CuiEqqurvb19f35558ZDAZdGaAPRoAtmBIKDAycPXv22LFj6Q4CYAumdB48eHDmzJnCwkK6gwCEoMCUj6+vb2BgoJ6eHr0xoA9GgAJTKunp6X///beXlxfdQaAP9t4/9sFu3bq1e/fuK1euiEQi4pHa2trdu3fTEQx0WkNDw4YNG/bu3auqCv/fVBStBXb8+PEpU6bk5uYuWbKk5X+BAoHA39+fpmygc3bt2mVpaTljxgy6gyAEfbAPWgvs+++/T05O/uWXX4qKirhcbkJCAvVpQJfl5eXFxsbu37+f7iDvQR+M0Fpgz58/J+4jqqmpmZCQEBgYWFNTQ30g0AV1dXWLFy+OiIgYMGAA3Vnegz4YobXAbGxsjhw5QvxsbW39n//8x9PTs0mRPiPwMf7+/nZ2dosU7LgCQOIFFh0dHRQU1L9//6dPnyKEdu7c+erVK0dHR/qyAZlcu3bt7Nmz0dHRdAcB7Wg93GRtbf348eNr165xOByEkKam5uXLl9PT02/dukVfPNCBxsbGlStX7t+/X0eRmk4I+mAf/ON4LofD+fzzz1t+VVFRmTVrFtl3CQDdsWvXLgsLizlz5tAdpC3ogxE6aJjw+fz09PR169ZJWQbH8aqqKrmmAjK5f/9+TEzMnTt36A4CPqqDAispKQkLC5NeYCkpKStXrpR8vLa2trKyslvpwMc1NjYuWbJk165dJnR88+lQ85s3GIOhoqVFd5D3miorVdTUVDQ1KR63g7PpXVxcnjx5In0Zd3f3yvZoair6HoIAAA0uSURBVGrSfkacEgsICBgyZMiyZcvoDtI+6IMR2m7BcnJy+Hz+ixcv9PT0zMzMnJ2dMQyjPhaQ7tq1aydPnrx37x7dQT4K+mCE1gIrLS11c3MrKyuztLTkcDg1NTU8Ho/D4WRkZAwbNoz6ZOBj3r17t2LFitjYWH19fbqzgA60Fpinp+f8+fP9/f1b5kjBcTw2Nnb58uXZ2dk0xQPtCAoKsrGxmT17Nt1BQMdaCywvL+/s2bPiMxBhGObl5bV9+3YacoGPuHXrVkJCgiJ/OSRAH4zQWk6Ojo6RkZHi50aJRKKYmJhRo0ZRHwu0q7q6evHixTExMcbGxnRn6YDeokUcFxe6U7TS/+or9tSp1I/bugWLj493d3ePiooaPXo0m80WCAQ8Hs/Y2Dg1NZX6WECSSCRavHixi4uLm5sb3VmArFoLzNTUlMvlcrlcPp//8uVL4iginIuoOLZv315XVxceHk53EJlAH4zQ9jC9vb29vb09xSFAh3777bfExEQul9tTrlauCA1VNTIy8vGhO8h7z4OD1czNDb29KR63Z/y1erkXL14sW7bs5MmTRkZGdGeRFfTBCDAvoqITiURLly5dtmzZxIkT6c4COg0KTNHt2LFDIBBAs6SHgq+ICu3s2bNxcXG3bt3qKbteLaAPRuhhf7Ze5caNG15eXpmZmf369aM7S6fB9WAE+IqooHg83ty5c48dO2ZnZ0d3FtB1UGCKqLi4+NNPPw0PD1eQSQ67AOZFJECBKZwHDx5MmTIlJCSkR88SBdeDEWAfTLHw+fzp06eHhYV5eHjQnaVboA9GgC2YAqmsrJw5c+a2bdt6enWBFlBgikIoFC5YsODzzz9vd4IT0EPBV0SFgOP4ypUrWSxWaGgo3VnkA/pgBCgw+olEolWrVpWWlp4/f75Pnz50x5EP6IMRoMBoJhQKly5dWlFRkZGRoaGhQXccIGdQYHRqaGhYsGCBSCRKT09nsVh0x5EnuB6MAAc5aFNVVfXZZ5+xWKyUlBQlqy4EfbAPoMDowefznZycrK2tT548yWAw6I4jf9AHI0CB0eDWrVuffPKJj49PRESE+DReQPnAPhjVsrKyPDw8EhISXBRp0iVAEigwSp08eXLDhg1JSUkTJkygOwu5oA9GgAKjSHNz85YtW1JSUi5fvmxpaUl3HNJBH4wABUaFioqKL774QkVF5fbt23DHmV4F9rBJl5SUZGtrO378+IyMjN5TXXA9GAG2YCR6+fLlypUr+Xx+SkqKk5MT3XEoBfMiEmALRpY//vjDzs7OwsLizp07va26EPTBPoAtmPwJhcKdO3cePHjwyJEjn332Gd1xAJ1gCyZnqamp1tbWt2/fvn37NlQXgC2Y3JSUlHzzzTcVFRXh4eFQWtAHI0CByUFTU1NERMSePXsCAgK++eabHjdJKBmgD0aAfwrdguN4Zmbm1q1bjYyMbt68OWTIELoTAcUCBdZFjY2Nx48f37dvn4qKyrZt2+bOnUt3IsUC14MR4CBHp9XU1ERERAwbNiwpKSkiIuLu3btQXZLgejACbMFkJRKJcnNzf/rpp6SkpOnTp6emptra2tIdSnFBH4wABdaxe/fuJSQkJCcn6+npeXh4FBQU9O/fn+5QoGdoW2A5OTl8Pv/FixfEPZqdnZ0xDKMlGe3q6upOnz596NCh8vLyZcuWZWVlmZub0x0K9DCtBVZaWurm5lZWVmZpacnhcGpqang8HofDycjIGDZsGI0RqffgwYPDhw//9NNPzs7OAQEBn332mdLMpkYZ6IMRWgvM09Nz/vz5/v7+LRex4zgeGxu7fPny7Oxs6pNRrKSk5MaNGzdu3MjKyqqrq1uyZEleXt7AgQPpztVTQR+M0FpgeXl5Z8+eFZ8iAsMwLy8vZb15aXNz8927d7Ozs7Ozs3Nzc9XV1cePHz9u3LgVK1bY2tr22i/GQL5aC8zR0TEyMjIgIKDlRASRSBQTEzNq1CiasslfdXV1YWFhbm5uVlbWH3/8MWDAgEmTJi1atCg2NnbAgAF0p1Mq0AcjtBZYfHy8u7t7VFTU6NGj2Wy2QCDg8XjGxsapqakUZ5KLioqK8vLyR48eFRYW5ufnFxcXP378uLm5ecSIEWPHjl2yZMnRo0cNDQ3pjqm04HowQmuBmZqacrlcLpfL5/NfvnxJHEV0dHSkOFBn1dXVlZWV/fXXXyUlJfn5+UVFRY8ePSovL9fV1e3Xr5+pqamFhYWrq6uFhcXQoUN1FGm3W7lBH4zQ9jC9vb29vb099TnENTU11dTUVFVVCT6orq6uqakRCARVVVWvX79+9uxZeXn506dPX79+jeN4v379Bg8ebG5uPmrUqHnz5g0dOnTAgAFKOZsn6HE6aDTz+fz09PR169ZJWaaxsfHp06eSj+M43u7yBQUF0dHRCKGGhoba2tqqqqrq6mqBQNDQ0FBdXV1fX19fX6+tra2rq6upqamlpaWlpaWjo8Nms4kfhg0bNnHixH79+pmYmOjr62tS/q0aANl1UGAlJSVhYWHSCywjI8PX11fy8aamJgzDmiS2ywYGBsQ3TyaTqaGh0VI8TCaTzWazWKxO1Yzk+oEiYI4e3UdHR3H+OiwbG8aAAe3mIbXJiX1sO9N9Tk5OERER48aNI2n9AHTfjRs3fHx8cnNzSVo/nCoFAIngVClACuiDEeBUKUAK6IMRWk+MysvL8/b2ljxVisfjUZwJKAHogxFay4k4VUr8MIvynSoFAMXIPVWqqKhIsuGbl5dH0h1TKysrSZr8/c2bNzo6OmQc7yFvzVVVVdra2mTc4K+mpkZdXV16K9+Yx2tis18fP96pNQsEAiaTyWQyuxewHfr372s2Nf19506bx4uKiuQ+lri2h+nleKqUr69vuztveXl5ampqZPyTqq+vJ2/NTCaTjH+sDQ0NDAaDpDWrqqqS0eRpbGzs06eP9DVPbWoSYNiNTo7e2NiooqJCxrx30xsa6rS0Xg8fLvnU5MmTQ0ND5T7iezjljIyMKioqyFjz8OHDHz58SMaa7ezs7ty5Q8aaJ06cePXqVTLWPGvWrNTUVDLWvHjx4uPHj5OxZm9v79jYWDLWvGnTpt27d5OxZulgVikASAQFBgCJoMAAIBEUGAAkggIDgERQYACQqA/1k0YNGTJkzJgxZHSrBg0aZG9vT0bnZ8CAAQ4ODmRcJd23b18HBwcyOu/Gxsb29vYaGhpyX7ORkZGdnR2bzZb7mg0NDW1sbMiY2cHQ0HDUqFEGBgZyX7N0JF4PBgCAr4gAkAgKDAASQYEBQCIoMABIBAUGAImgwAAgERQYACSCAgOARFBgyubFixfPnj2jOwV4j4oCS0lJGTlypKampp2d3ZUrVyQXmD9/PiamoqKCglTS3bt3b8KECdra2ubm5sfbm1hCATMTAgICkpOT231KATM/fvx42rRpOjo6//73v0tLSyUXUMDMnUP2JdOPHz9ms9nnzp17/fp1SEiIjo5OXV1dm2VsbGxOnz79+IPm5mayU0lXVVWlo6MTHh5eVVWVmZmpqal58+bNNssoWmYcx0+cODF58mQMw/bt29fuAgqYecyYMdu2bausrAwJCbG2tpZcQAEzdwrpBZaZmenm5kb8XFdXh2EYn89vs4yWltbTp0/JTiK7X3/9tV+/fi2/urq67tq1q80yipYZx/G0tLTo6GgrK6uPFZiiZeZyubq6ukKhEMfx5uZmQ0PDnJycNssoWubOomjSm/r6+qtXr27cuNHJyanNU+Xl5RiGTZ8+XVNT09LSMjk5mZpIUtTU1Dx58oT4WSAQmJiYZGVliS+ggJlbzJkzp90CU8DMp06dmjBhQsuvkyZNOnbsmPgCCpi5sygqsL/++mvSpEksFisiIkIkEok/lZ+f7+Tk9Ntvv719+zY5OZnFYuXl5VGTqkN37961s7NbsWJFU1OT+OOKnPljBaaAmaOiombOnNnyq6ur6549e8QXUMDMnUVKgR0+fNjAwMDAwGD16tXij1dXV5uYmFy5ckXKa11dXYODg8lIJV2bzLW1tWvXru3fv/+RI0c6fK2CZCZ8rMDaUITMx48fnzhxYstT//73v6V/2nRl7g75z/CIEPL09PT09CR+jouLKysr+/777xFC2traZmZmxcXFkyZNaln4zp07FRUVLi4uxK+qqqrq6upkpJJOPLNIJJo5c6a+vn5hYaG2trbkwgqYuUMKmPnWrVs8Hk8kEqmoqOA4XlhYaGZmJr6wgmTuFrIrODc318DA4Pbt242NjRkZGWw2u7i4GMfx9PR0YpJQYqLf9PT0urq6tLQ0HR2d0tJSslNJl5qa2r9//6dPnz7/QCAQKHjmFm22YAqe2dLSMioqqrm5OSYmxtzcnNh9UPDMnULFPtixY8esrKw0NTVtbW3T09OJB01MTFr+HaSkpFhbW2tpaTk4OEj/AkmNwMDANv8bIo4iKnLmFm0KTMEzl5aWTpw40cDA4JNPPmmZlVnBM3cKTBkAAIngVCkASAQFBgCJ/j9TmKznNE3qXgAAAABJRU5ErkJggg==" /><img 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" 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GjQIKKYO5fLtbGxcXR0tLe3t7S0bGpqcsrPZ6tUdqdOTZkyRYvnPVFVVXXr1i2RSERoM83NzU1NTXV1dQqFws7ObsSIERMnTvT19dVymVZXVz969KimpkapVEokkqqqqsrKytraWgzDnJyc5o4YcevXX+0cHBBCNjY2FhYWjY2Nd+/eTU1NLS0tnTBhgo2NzePHjx8+fIhhmKOjY0tLC2Hdzc1t2rRpYWFh8+bN43K5OTk5x44dU925c2TBgooBAyZNmhQeHh4eHk4U1VQoFGfOnPnss8/a2to+/PDDsRMnFqpUu77+evv27a+//rq/v3/guHFkHazx6FFE0TtcvULHq1JCoTAlJWX9+vVa+hw/fpyoIN2NxsbGuLi4d/R7rwfH8aioqAsXLixfvtzb25u4sIhLzdLSsra29tixY3v27PHw8Fi8eLGTk9P9+/evXLly9+5dV1dXZ2dnOzs7Ho83ffr0JUuWEIVvCRQKxeeffx4bG/vmm29OmDBBJBJduXLl8uXLbDbbxsamsbHR0tLylVdeCQ0NnTVrljp+mpqaDh8+vGfPHmdn55CQEDabfe7cuTt37kRERISEhHh5edXU1GRnZ1+4cCElJWXo0KHr169funRpT9cljuP37t0rLCy0tbUdOXIkUYRYIpEUFBQ0NDTY2to+//zzap8VCkVhYWFRUZFUKm1qapJKpfX19Y2NjUql0tra2szMrKOj49GjR9evXx82bNi8efMmTpzI4/GsrKwQQg4ODlKptL29vbq6uqysTCwW19XVEYUHq6urb9y4UVFRERgY6OnpyWKxzMzMrK2t7ezsnJyc2Gx2Q0PDw4cPr1+/XldXN3fu3Pnz57/00kvqimgFBQXHjh377bffysrKfH19nZ2dzc3N7ezs3Nzc3N3dnZyccByvq6urrKysrq6WSCQIIalUKpfLORyOj4/P3Llzp06d2vVWua6urqmpydra2tHRkaitfe7cuePHj2dkZHR0dHh6ei5atOi1114bN25cW1vbpUuXDh8+fObMmUGDBllZWRUVFY0aNYp8zi9dunT69Om8vLybN2/u379/4cKF+lx4dOtgOgIsNTV11apV5bqWXxsaGsiNzz333Lfffrty5Up9/Ni3bx+fz79w4YJNzy/aKBSK06dPX7hwQSqVDhs2bNq0aYGBgVZ6vPlWWlq6f//+vLw8Nze3KVOmzJ49myisjRAqLCxMTk4+ceLE7du3R44c6eDgUFtbW1hY+PLLL69fvz4oKEg9SEFBwU8//XTu3LmKigoul+vv7z9z5swFCxbweDx9DpBaFArF1atXz58/LxAIysvLZTIZQogIVzab7ebm5unpSWyKRVTPdXJyGjdu3JgxY3Q+EIpEojNnzhBTt6Ojo4ODQ1VVlUqlWrx48fLlyydPnkz3bZhCodBYDlkulz969Ki9vZ3H4xFTWU/cvHlz4cKFRUVFxNYf2qE7wGjc/MHW1pbP55PbFU1NisbGri0qlcrLyysrK4s+Z3TS3Nx848aNP//8UyAQtLa2MuiJFhQSiaKhwTi2lEplfn6+QCAoKirS8yvtZWUUOtAhFqvkcp3d5DU1StLva9msWUeOHMFxvOPxY1yplBUWFr322uOdO8lfv379+qRJkyhxWCMMBFjF7t3iTz/t2pKVleXj40OfJ/2Gyi+/FG/bxrQXPXKHyyVf6wYjnD9f8uefOruVrlpVm5jYtUUpld60sVmxYgWO44+Cghr++OP+4MG3ELqFYeSv0x1gDKy6kHWw8+fP96o8vckCOpgGNOlgZggRO2P1FR1MJBIR2zRpRPsix9OTlZVl5IrhQP/GzMysqKiIeDRllv8FGJvNzsnJ2bdvn5+fn5eXV7dOdAfYvXv39N/cAAD0YfDgwSUlJQwszD/J/wLM1dWVz+cTL9SvWbOGVpPddDCpVFpXV8fIWtwzhxWlL/tRTp/KB/OqqCgqKhrLtA72xHpoZGQkee9DyrHvmt2NUHFxMY/H64MafB+ES91OaHQw5MmNhZ4S961b9enmTNJgMTZ7SGKi1zvvFBcXz9+9GyE09PBhdPgwhb7pzxMB9sYbbxjfg5KSEpi+AMrx8PAgdvRkFgbmjW75YKWlpRBgegL5YGR6ygdzd3evqKjoQ/lgRqNbPphIJFJvkg1oB/LByPSUD+bu7l5ZWdmH8sGMRjcdrKqqyt3d3fhuPIuADqaBHvLBBg4cWFlZybgOxvzSQlVVlfrNQACgCldX16qqKqa96AMBVllZCTMYQDkODg719fVMe9EH6iJWV1cbkNpkmkBdRDI96WDEq/RW06YxWxeRgZd9u2FlZdXS0kKfG4DJMnDgwMePH2vv0w9f9u1Ke3s7kUrIrBtAv8TR0VFjpqIxYVgHq6+vp6Q8g4kAOhgZLXURHR0dG/PzTVoHa2hocOirm/H0QUAHI6OlLqK9vb35tm0mrYPBDNYrQAfTQM91ETkcjlIuN2kdrLGx0QivFwOmiZ2dnZLpv0cMB5hEIulaBAoAKITD4ZhigHXVwXpVVBQAHYyMlv3BOBxOpYcHszoYvTtcaqRrPlhzczPMYPoD+WBkesoHQwhxOJwbAQHL/f0ZzAdj+BYRZjCAPuzs7IgqqAzCsA4GAdYrQAcjo0UHs7Ozw6uqTFoHk0gkEGD6AzoYGS06mI2NzcLr101aB4NVxF4BOpgGetbBbGxsTD0frKWlpV/u1gP0BWxsbExxmb4rLS0tWnZ7AICnwdraWkXdjGoYDOtgLS0t8Cq9/oAORkaLDmZtbX2XzTbpfLDnn3/+wYMH9PkAmDI1NTXOzs7a+/TzfDCYwQD6sLa2bmlpYdaHJ97kUKlU6enp+fn5dXV1zs7OI0eOnD17NuUmlRIJwnEzLhch1NraCs9g+qNsbkZKpVlffT26o7zcwtOTqtHkFRXmLi6Yps34uqKorWXZ2LCe3IeR8MTKyorb3q5SKORlZeItWyx8fQfr93YIhXR6LxQK582bZ2lpOWDAgNu3b0+bNq2kpMTNzS0tLY3ajJKauDhcoRj4738jmMF6Se0PP6ik0oE7djDtiGby/Pz8KipYeuw5qg9lK1e6btjAmTNHezfx1q02kyY5rVihbiF0MP+6OgzDvsCwmuPHa95/v0MkQseOGT/AOm8RV65c+a9//SsnJ+fWrVt79+7l8XglJSXBwcF6brKsP2odDMdxmUymzx6wAAHoYBroWQdDCJmzWDKplPn9wRBCt2/fPn36NPHz8uXLN23axGKxNmzYMHToUJpst7W1WVpawrYPAH2wWKyOjg4mHVD/NGzYsLNnzxI/X7lyhSgGevnyZfpS+uH+EKAbFosll8sZdKBzBtuzZ09ISEhiYiKLxUpPT//vf/9bV1cXHh7+yy+/UGtSvT9YW1sbBFivgP3ByGjRwRBC+XZ2IwcN4jK3P9gTOlhpaWlCQsKePXtycnKIB6SSkhKDFQCdOtjDhw9Hjhxp8PgAoJNJkyZdv35dSwe6dbAn1kCfe+651atXq/87YMCAIXr8CTEY4hmMvvEBwMrKqo3ROV+HyCAUClNSUrTv0VxdXZ2RkUFuVyqVGl+1VOtgsITYW0AHI6NFB0MIuSLU3tbWXlTEvA6mkfz8/OjoaO0BlpeX99tvv5Hb5XK5Rh1drYPJZDKYwXoF6GBktOhgCKHl9+6hjIyCt99mSgfTEWDz588v15WmOmPGjBkzZpDbORyOxlwvtQ7W1tYGM1ivAB1MA1p1MDMMk8tkfUIHI8jMzBQKhdXV1Y6OjsOHDw8KCsJoW3uBGQygGxaL1caoDtYZYIWFhaGhoWKx2MfHh8vlSiSS3NxcLpebmprq7e1Nh22YwQC66UM6WGRk5NKlSzdv3qx+tQLH8YSEhIiICI1rGAaj1sFgBustoIOR0a6D1QwZ0mpvb9sXdDA7O7v6+vpuq/gKhUJnRk1P6NTB4uLi1qxZY9jgAKAPW7du3blzp5YOxssHCwwMjImJUXR5XlSpVPHx8b6+vjTFtkwmG0DdHzwAIGNpaSmTyRh0oDPA+Hx+cnKyq6vrjBkzXn311ZkzZ7q7u/P5/P3791NrUl0XEZ7BegvURSSjpS4iQshOJuuQyRisi9j5DMbj8QQCgUAgEAqFNTU1xCpiYGAg5SbVOlh7ezs8g/UK0MHIaNfB/A4dah89umD69L6igwUEBAQEBNBqUq2DyWQyLpdLq61+BuhgGtCqg7EQUra3m2hdxPb2dngGA2iFxWIp9LjPpNEBBm1DgAF0g2GYvI8kXBoNdV1ECLDeAnURyWjXwTr8/autrEx0fzAIsN4C+4OR0bI/GEKo9a23yn74wUT3B4MAA+hmwIAB7e3tDDrA5P5gEGC9BXQwMtp1MEuJRN7ebqL7g0GA9RbYH4yMlv3BEEJWO3aMrKw00f3BIMB6C+hgGtCqg2E4ruzoAB0MAGgBdDAIMIBGWCyWgtF8MNDBniVAByOjXQezDAoqwXET3R9s6NChRUVF9DkAALW1tU5OTlo69Of9wSCjGaAbNpvdV2rTGw21DtbR0WFhYWF8B55dQAcjo10HM6urU3R0mKgOBgHWW0AHI6NdBxP94x+T5XIT1cEgwHoL6GAa0KqD4UolG8NUpqmDyeVyNpvNoAOAKWCuq/g2rTAWYHK53MzMDHbfA+jG3MyMkbmLgDEdDO4PDQB0MDLadTDOrFmVlpbsKVNMTgerr693cHCgzzoAEHh4eJSXl/f0ab/VwWAGA4wDs1IYYzoYBJgBgA5GRrsOJheJLC0sTFEHgwAzANDByGjXwYqXLQtsa5O98YbJ6WAdHR2wRt9bQAfTgC4dzNLcHCe8YlwHa2lpuX//PnHDKhAIdu7cefnyZZoMwwwGGAczc/M+sUyfkZHh6ek5fvx4b2/vP/74Y/HixXl5eaGhoUeOHKHDsFwuhwADjADb3BzvCwG2YcOGTZs2NTc3r1u3bsmSJQcOHDh06FBiYuJnn31GrUnQwQwGdDAyOnWwBi63Y8wY5nUwa2vrqqoqHMerqqowDGttbcVxvKGhwdra2jAFQLsOdvHixZkzZxo2MgDoz4IFC5KTk3v61Hg62KhRo/h8fktLCxEVV69eRQhdunRp2LBhdAQ2zGCAcbCwsOgTOlhMTMw333xjZ2fH5/OTkpJWrFgRGhoaERGxleodXwgdDN70NQDQwcjo1MEGsNl4eTnzOlhQUNDjx49zcnKEQmFwcHBqaurEiRNTUlLCw8OpNUnoYFCQwwBAByOjUwcbWVXlsX07UzrYE2/yW1lZjR49mvh5zJgxY+h5niZ0MJjBDAB0MA3o0sEszM0RczqYjlQZoVCYkpKyfv16LX2uXLny/fffk9tlMllzc3NP3wKhGTAOLDMzBq3rCLD8/Pzo6GjtATZs2DCNt5GnT5/WsgUz6GCAcTA3M8OZ08F0BNj8+fPLdT25Dhw4UGOARUREaEwmtfL3R0qlvLYWZrDeYkXpQw7l9E0drLW0tMHb2+3OHYRhVPmmP90DIDMzUygUVldXE5ugBwUFYVS7RewPJo+LgwDrLbA/GBnt+4MN2r1b+uGH98eMmXf7NoW+6U9ngBUWFoaGhorFYh8fHy6XK5FIcnNzuVxuamqqt7c35YZhkQMwDmw2W85c9ezOZfrIyMilS5dWVlZevHjx5MmT6enpFRUV69ati4iIoNYk6GAGAzoYGZ06mIW5ObuqinkdLDs7e/Xq1V2r0GAYFhUVlZubS61JQgeDADMA0MHI6NTBBhcWTk1MZD4fLDAwMCYmputeLyqVKj4+3tfXl1qToIMZDOhgGtBZFxGhPqGD8fn8sLCw2NhYPz8/DocjlUpzc3Pd3NySkpLoMNzR0WFra0vHyADQFbM+ooPxeDyBQCAQCIRCYU1NDbGKGBgYSJNh0MEA42DGaD5Y92X6gICAgIAAWk3+Twe7cgVuEXsL6GBkdOpgCjMz0aBBwwsL+4QOZgT+p4Olp0OA9RbQwcjo1MEUiYkXp01bWlBAoW/6w2TpbAgwwAj0FR3MaIAOZjCgg5HRRwezaWhgXgczGqCDGQzoYGR06tOqRSkAABPGSURBVGB22dmvp6czr4MZDXVdRFhF7C2gg2lAlw5mjmEM6mDwDAb0c8zMzBhbpGcwwBQKBbM7owEmAruPFB41GlAX0WCgLiIZnToYNnRoAZfLfF1EytFeF3HWrFkXLlygzzoAEGRmZk6ePLmnT/vt/mDwqhRgHMzNzRV6LPfTBGM6GDyDGQDoYGR06mBsMzP7lhbQwQDdgA5GRqcOxrp6dXNBgcnpYHK5HGaw3gI6mAZ06mA4jhGriCalgykUCpjBACPAYrRsGwjNQD/H3NSEZkIHg0UOAwAdjIxOHcxixIh7FhYmp4N5eHiUl5fTZx0ACKqrq11cXHr6tN/qYDCDAcbB3Nwc8sEAvQAdjIw+OpirXA46GKAb0MHI6NTB2tLSvm9tNTkdDG4RDQB0MA3o0sHMVCpT1MFgBgOMA4uRxUO1daYMwwwGGA8mCrYRdAbY8uXL09LSjGDSyt9/gK8vi8WifGOkfg/oYGR06mADRo7MZrGY18EQQuPGjXvnnXeampooUQC06GBtbW2WlpaUWAEAnXC53MbGRo0fGVUH+/3336dOnTp16tRvvvlGKpXSF9XwAAYYEwZTwp4IMAzDXn/99WvXrpWWlnp6ekZERJw4caK6uppak0qJpKO+Hh7ADAB0MDI6dTCkUnmyWJUREX1FB7Ozs4uJicnPz584cWJCQsLgwYOpNVkTF9eQkAAzmAGADkZGpw7WmJT0dX19e1JS39LBXFxcVq1adf78ebFYTK1JXKFQQTKYQYAOpgFdOhiSy/93lTOrg8XFxTk6OpJ7uLi4UG5VpVLBDAaYAp3TyJo1a4xmValUwgwGGA0GBSEdyoBQKIyJiaHWpJW/Pxo+HGYwAwAdjIw+OthDW1vEuA6mkZSUFA8PD+19jh071tPgX331lcav3Lt3z9fX10BlAQB6yfjx4wUCgcaP6NbBdNynzZ8/v1zX2mt4eDiu6fGRw+E4ODho/AoU5ACMCYM6WPcAy8zMFAqF1dXVxB7NQUFBlN+/KiUSRWMjPIMZgLK5GSmVZvb2TDuimY7ycgtPT6pGk1dUmLu4YLquE0VtLcvGhmVlpdETuUjEHjjQTaFAH38smjlzsH67ZlJI511pYWHh2LFjQ0JCEhMTr169evDgwfDw8FGjRhUWFlJrsiYuDj90CALMAEAHI6OPDrYhJ4d94QIjOljnVR4ZGbl06dLNmzerX+/HcTwhISEiIiIjI4NCk7hCgcOr9AYBOpgG9NDB+kQ+WHZ29urVq7smz2AYFhUVlZubS7lVlUoFAQYYj76wTB8YGBgTE9P1WVClUsXHx/v6+lJuValUwiIHYDQYTIvqnEb4fH5YWFhsbKyfnx+Hw5FKpbm5uW5ubklJSdSatPL372hvN29ooHZYU8CK0occyumzOliJs7OvWMyI3NwZYDweTyAQCAQCoVBYU1NDrCIGBgZSbtI+JETKYpnn5FA+cr+HGxzMtAvaGHLgAIWjueu34uf89tvdWjA2e0hiIvHzoN27EUKnJ0+2fu21sLAwCt3Tk+4PQgEBAQEBAXRbhXoBgDHpK/lgxkEpkagkEggwA4B8MDL65IM5y2RuMTF9JR+Mbmri4jjJybDIYQCgg5HRRwdbeu4cNyurb+WD0QfoYAYDOpgGnhUdzJiADgYYk76brkITEGCAicDM/mDSgQMhwAwA8sHI6KODVXp6MpUPxsBVbh8SUllSwi4tNb7pZx3Qwcjoo4PdWLiwyMXlgw8+oNA9PWHmFhF0MMCYmJwOhqRSCDADAB2MjD46GLe52f/QIRPSwTyuXIEAMwDQwcjoo4NN//nngTk5JqSDIaXSzMzM+KafdUAH0wDoYGSgLiJgIoAOBgA0wowOVu/kBAFmAKCDkdFHB2saPty0dLCiS5eGQoD1HtDByOijgxWtXHnq/v2EhAQK3dMT0MGA/o+ZmZmSocUhZnQwVmsrBJgBgA5GRh8dzLqhYfaff5qQDvb83bsQYAYAOhgZfXSwEV9+Oby42LR0MAgwAwAdTAN66GDIBHUwCDDAaDBYtg0CDABohBkdrJLDgQAzANDByOijg7X7+fXR/cGeBltbWz6fr/GjRYsWnTx5kj7TANCVlJSU+fPna/yI7v3BQAcD+j99RQfLz89va2tDCN26dWvXrl179uwpKCig3KRSIjGXyeBtegMAHYyMPjrYgJqa1+7cYVgHO3z48NixY5ubmw8dOjR9+vTs7OwrV66MGzcuLS2NWpM1cXGTiothBjMA0MHI6KODcTZsGFNVxfD+YFu2bPnjjz9cXV137Nhx6tSpOXPmIISSk5M3b9788ssvU2gSVygwWEU0CNDBNPCs5INJpdKJEycihOrr6ydNmkQ0zp49u6ioiHKrsEwPmAidAbZgwYIvv/xSoVAsXLhw3759RGNcXBwde0FAgAFGhbnCo51X+ffff79o0SIPDw8vL699+/b98MMPHR0d1tbWycnJ1Jq08vcvGzAAAswAYH8wMvroYCggAKWlMVLfF8OfvDH9v//7v/z8/Lq6OgcHh2HDhk2ePNngoTkczrfffrty5UryRy+88EJiYuLYv55EAYBWHjx48Nprr+Vo2pIuKytrw4YN169fp8l092lkwoQJEyZMoMmYGtDBAGPCYF1EHVe5UChMSUlZv369lj4ikSgzM5PcrlAoNB6VUiKx6OiAADMAZXMzUirN7O2ZdkQzHeXlFp6eVI0mr6gwd3HBdF0nitpalo0Ny8pKoydykYg9cCBLLF77+LFo167B+mVJU4gO7/Pz86Ojo7UHWFlZ2W+//UZuVygUMpmM3F4TFze3thYCzABqf/hBJZUO3LGDaUc0k+fn51dR0e1aN5iylStdN2zgzJmjvZt461abSZOcVqxQtxA6mH9dHUKoeNky140bW6OiJre0VP37330uwObPn1+uS56fPHmyxkc1Dodja2tLbgcdzGBAB9OAHjoYzpwO1v0qz8zMFAqF1dXVxCboQUFBdKy9wDI9YEwYzAfrvMoLCwtDQ0PFYrGPjw+Xy5VIJLm5uVwuNzU11dvbm1qrEGCAidB5lUdGRi5dunTz5s2sv9JmcBxPSEiIiIjIyMig0KSVv78QwyDADAB0MDL66GCWQUEtf/zBzD6X6sQVOzu7+vr6btksCoXC2dnZsEwYLflg9vb2ZFsAQBPNzc22trYaPzJePlhgYGBMTEzXhXWVShUfH+/r60t5VCuh6A1gRPrE/mB8Pj85OdnV1XXGjBmvvvrqzJkz3d3d+Xz+/v37qTWplEgGyOUQYAYA+WBk9MkHU5aVbVcoGM4H4/F4AoHg3Llzq1evfvnll//5z38mJyffv39/6NCh1JqsiYsLgwAzCMgHI6NPPljx7NlzFQqG88EIAgIC6Hh9vitQF9FgQAfTgD46GDGOidRFVKlUGIYxs6QDAMaFmQBjMVJACwCMDgMXOvv554vh/tAgoC4iGX10MNuZM02oLmJTU5OdnR19dgGAjJOTU21tLbm9H9ZFhGQwwPgwVRqRgQDraGjgQlFEgwAdjIw+Olh7UdEWiaQ+OppC9/SEgQCT8PmLNeWJAToBHYyMPjpYwfTp02SyVhMJMJVczoZVRIMAHUwDoIN1A5bpAdOBMaHZ+HYBwPgwMZMMHy6ytmbA7rMP6GBk9NfBkInoYPfu3fPz86PPLgCQeeGFF27fvk1uBx0MACiAqZQwJgKsqYkDz2AGAToYGT11sLeLi5V/7bhgTBgIMPzIkTk1Nca32w8AHYyMnjrYuNpaix9/pMo3/WFGBzOHGcwgQAfTgN46GG4iOhiO46CDASYCCM0AQCNMvOw7ZEg1l2t8u/0A0MHI9EIHY+LBhIHl8pYJEx7evm18u/0AbnAw0y5oY8iBAxSO5q7fRg3Ob7/drQVjs4ckJhI/D9q9GyE09PDh92SyN998czyF/ukH6GCASWBCOpiqudmGifWcfgDoYGT01MEW37xpc/w4he7pCQMBxklNDSwsNL7dfgDoYGT01MFGlZW5HTtGlW/6AzrYswToYBro2zpY57NQVVWVi4sLsYB+586d5ORka2vrkJAQLy8vak1CugpgOnTOYO7u7sRmlsePH588ebJAIMjMzAwICKB27yIEOhjAEIz8UdewmvfJJ5/8+uuvS5YsQQglJydv2rQpKyuLQpM+ISEe9fUUDmg6wP5gZPTUwRqPHmVEB+vMB0MIlZSU4DhubW0tlUqJxpaWlp42VtKJlv3BAKCPYNR8sKysLJFINHXq1OvXrxMtly5dGjZsGANxDwD9gs5bxPDw8C+++EIoFLa0tEil0jlz5qSmpi5ZsuTw4cPUmlRKJAjHzeBtqd6jbG5GSqWZvT3Tjmimo7zcwtOTqtHkFRXmLi6YrncSFLW1LBsblpWVRk/kIhF74MD2khLxli0Wvr6D9Xs7hEI6Z7Bjx45lZ2c3NzeLxeK9e/cihNzc3LKyshYvXkytyZq4uOqYGGrHNBFAByOjpw7WcPRon9gfDCHk7u7u7u6OEBo/npZXt4j9wegYud8DOpgG+nZdRB3zr1AoTElJWb9+vZY+6enpn3/+ObldJpNJJJKn8g4AnnF0BFh+fn50dLT2ABszZsymTZvI7cXFxRwO59atW93aWwoKmhsbGw4e7K2vlNDW1oYQsnrylv1Zse585w6rvT3d0FNH97GPUiiOHDmi6mGlvrfWnxOLc9LTpbqqSwwSCltxvNHSUt3CamsbIZcfPHgQIcSrqcm/csVNJjNHCEeIfDU+fPhQT38MA8Npmzc3btyoUaQe+OhRR1vbVeoEk14hl8sRQmw2+1m0PlWpHIBQuqFbZ9B97B93dHxhYdHTXV1vrb8pl180MyvT9U7CQoWilMW626WbOUIfdnR8ZmGBEHpbLk83M3tDLp+jUikRWj1uHHmE2bNnf/XVV3p61Vu6B1hmZqZQKKyurnZ0dBw+fHhQUBDlrzXt2LFDqVR++umn1A6rJ59++imO49u3bzdB64RdBq1jGPbJJ5+YlPXOW8TCwsLQ0FCxWOzj48PlciUSSW5uLpfLTU1N9fb2NrJbANA/6AywyMjIpUuXbt68Wf2iII7jCQkJERERlL+OCAAmQueda3Z29urVq7u+hothWFRUVG5uLhOOAUB/oDOcAgMDY2JiuqZVq1Sq+Ph4X19fJhwDgP5A5y0in88PCwuLjY318/PjcDhSqTQ3N9fNzS0pKYlB/wDgmaYzwHg8nkAgEAgEQqGwpqaGWEUMDAxk0DkAeNahUQfridzcXJVKxdSd54MHD3AcN1nrCCEfHx9GrOfk5GAYZmrWGQgwADAdIHUfAGgEAgwAaAQCDABoBAIMAGgEAgwAaAQCDABoBAIMAGgEAgwAaAQCDOifVFdXi0Qipr2gOcBKSkrmzp1rb28/a9asQk1bFunsQKv1u3fvTp8+3c7ObsSIEQeprhGi56G1traOGjXq1KlTRrbe3Ny8YsUKV1dXb2/vI0eOGNn6tWvXAgMDbW1tAwICLl++TK11gq1btx7vYUMwWq+6btAbYGFhYVOnTi0uLp4zZ05oaKgBHeiz3tTUNHPmzJCQkPLy8tjY2KioqJs3bxrNupr33nvv0aNHFNrV03pISIi7u/ujR4/i4+MjIiLy8/ONZr2trW3hwoWLFi0qLCwMDw8PCQlpbW2l0PqhQ4defPHFn376yTD3KIa+qtwCgcDBwUEul+M4rlQqXVxcMjMze9WBVuunT58eOHCg+r+LFi364osvjGad4OTJkzNmzBg9evTJkyepMq2P9Zs3b7q5uREdcByvqqpqbW01mvXc3FwWi0XsfyCTydhs9r1796iyjuN4cnJyXFycv7//d999Z4B71ELjDFZQUODn50dsx8xisXx8fAoKCnrVgVbrM2bMUE9ZLS0tt27dojA3R59Dq6ysfO+99w4cOGBmaJUog63n5+d7eXmtXr3a1dV19OjRFy5coLCWm07rw4cP9/T0jImJqa6u/v77793c3EY+uT3KU7JgwYI1a9b0tK8drVcdGRoDrKamhsPhqP/L5XKrq6t71YFW6xwOx8PDA/31JDZv3rzp06cbzTqO4ytWrNi2bRuPx6PKqP7W6+rqrl+/7uPjU1hYGBsbu3Llytu3bxvNurm5eVxc3Mcff+zu7v7BBx/ExsZaWFhQZf3p3aMWGgPM0dFRKpWq/yuRSBwdHXvVgVbrCKHW1tb169fPnz9/zZo1fD6fwplEp/X4+HhLS8sVK1ZQZbFX1h0cHEaMGPHuu+9yOJy5c+fOmzcvNTXVaNbv3r27atWqtLS0lpaWP//8c+3atQKBgCrrT+8etdAYYMOHDydyKxFCOI7n5eUNHz68Vx1ota5SqRYsWCASifLy8iIiIqiyq6f1a9eunTp1CsMwDMNycnJCQkKWL19uNOvDhg3rWnxFqVRadqmMS7f1tLS0SZMmvfTSS1ZWVnPmzJk+ffqZM2eosv707lEMfY93OI77+PjExsYqlcr4+PgRI0aoVCocx1NSUgoKCrR0MI71pKSkQYMGPX78uPIv1NsOGsF6V3x9fald5NDHuo+Pz1dffdXS0pKcnMzhcPLz841mPTMz09HR8dSpU42NjcnJyc7OzhkZGRRaJwgJCem6yGG0q64b9AZYYWHhjBkznJ2dp02bpj48Dw8P9ZFr7GAc69u2bev2t4bCVUSd1rtCR4DptF5cXDx79mwOh+Pv73/27FkjWz927Jifn5+1tbWvr++hQ4eotU7QLcCMdtV1A0oGAACNwKtSAEAjEGAAQCP/D3S7R1beQb+7AAAAAElFTkSuQmCC" 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" /></p> <p>The implementation works as expected, with a good performance given the size of the sample, and also compares well with another current implementations on <code>classIntervals</code>.</p> </div> <div id="references" class="section level1 unnumbered"> <h1>References</h1> <div id="refs" class="references"> <div id="ref-Falk_2011"> <p>Falk, Michael, Juerg Huesler, and Rolf-Dieter Reiss. 2011. <em>Laws of Small Numbers: Extremes and Rare Events</em>. <em>Laws of Small Numbers: Extremes and Rare Events</em>. <a href="https://doi.org/10.1007/978-3-0348-0009-9">https://doi.org/10.1007/978-3-0348-0009-9</a>.</p> </div> <div id="ref-Fraga_2009"> <p>Fraga Alves, Maria, L.D. Haan, and Claudia Neves. 2009. “Statistical Inference for Heavy and Super-Heavy Tailed Distributions.” <em>J. Stat. Plan. Inf.</em> 139 (January): 213–27.</p> </div> <div id="ref-Jiang_2013"> <p>Jiang, Bin. 2013. “Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution.” <em>The Professional Geographer</em> 65 (3): 482–94. <a href="https://doi.org/10.1080/00330124.2012.700499">https://doi.org/10.1080/00330124.2012.700499</a>.</p> </div> <div id="ref-Jiang_2019"> <p>———. 2019. “A Recursive Definition of Goodness of Space for Bridging the Concepts of Space and Place for Sustainability.” <em>Sustainability</em> 11 (15): 4091. <a href="https://doi.org/10.3390/su11154091">https://doi.org/10.3390/su11154091</a>.</p> </div> <div id="ref-Jiang3_2013"> <p>Jiang, Bin, Xintao Liu, and Tao Jia. 2013. “Scaling of Geographic Space as a Universal Rule for Map Generalization.” <em>Annals of the Association of American Geographers</em> 103 (4): 844–55. <a href="https://doi.org/10.1080/00045608.2013.765773">https://doi.org/10.1080/00045608.2013.765773</a>.</p> </div> <div id="ref-Jiang2_2013"> <p>Jiang, Bin, and Junjun Yin. 2013. “Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features.” <em>Annals of the Association of American Geographers</em> 104 (3): 530–40. <a href="https://doi.org/10.1080/00045608.2013.834239">https://doi.org/10.1080/00045608.2013.834239</a>.</p> </div> <div id="ref-taleb_black_2008"> <p>Taleb, Nassim Nicholas. 2008. <em>The Black Swan: The Impact of the Highly Improbable</em>. 1st ed. London: Random House.</p> </div> <div id="ref-vasicek2012"> <p>Vasicek, Oldrich. 2002. “Loan Portfolio Value.” <em>Risk</em>, December, 160–62.</p> </div> </div> </div> <div class="footnotes"> <hr /> <ol> <li id="fn1"><p>The method implemented on <code>classInt</code> corresponds to head/tails 1.0 as named on this article.<a href="#fnref1" class="footnote-back">↩</a></p></li> </ol> </div> <!-- code folding --> <!-- dynamically load mathjax for compatibility with self-contained --> <script> (function () { var script = document.createElement("script"); script.type = "text/javascript"; script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; document.getElementsByTagName("head")[0].appendChild(script); })(); </script> </body> </html>