487 lines
183 KiB
HTML
487 lines
183 KiB
HTML
<!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="杨佳达" />
|
||
|
||
<meta name="date" content="2021-07-15" />
|
||
|
||
<title>Task03_Statistics.knit</title>
|
||
|
||
<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
|
||
// v0.0.1
|
||
// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
|
||
|
||
document.addEventListener('DOMContentLoaded', function() {
|
||
const codeList = document.getElementsByClassName("sourceCode");
|
||
for (var i = 0; i < codeList.length; i++) {
|
||
var linkList = codeList[i].getElementsByTagName('a');
|
||
for (var j = 0; j < linkList.length; j++) {
|
||
if (linkList[j].innerHTML === "") {
|
||
linkList[j].setAttribute('aria-hidden', 'true');
|
||
}
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
|
||
|
||
<style type="text/css">code{white-space: pre;}</style>
|
||
<style type="text/css" data-origin="pandoc">
|
||
code.sourceCode > span { display: inline-block; line-height: 1.25; }
|
||
code.sourceCode > span { color: inherit; text-decoration: inherit; }
|
||
code.sourceCode > span: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; }
|
||
code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
|
||
}
|
||
pre.numberSource code
|
||
{ counter-reset: source-line 0; }
|
||
pre.numberSource code > span
|
||
{ position: relative; left: -4em; counter-increment: source-line; }
|
||
pre.numberSource code > span > a:first-child::before
|
||
{ content: counter(source-line);
|
||
position: relative; left: -1em; text-align: right; vertical-align: baseline;
|
||
border: none; display: inline-block;
|
||
-webkit-touch-callout: none; -webkit-user-select: none;
|
||
-khtml-user-select: none; -moz-user-select: none;
|
||
-ms-user-select: none; user-select: none;
|
||
padding: 0 4px; width: 4em;
|
||
color: #aaaaaa;
|
||
}
|
||
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
|
||
div.sourceCode
|
||
{ }
|
||
@media screen {
|
||
code.sourceCode > span > a:first-child::before { text-decoration: underline; }
|
||
}
|
||
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
|
||
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
|
||
code span.at { color: #7d9029; } /* Attribute */
|
||
code span.bn { color: #40a070; } /* BaseN */
|
||
code span.bu { } /* BuiltIn */
|
||
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
|
||
code span.ch { color: #4070a0; } /* Char */
|
||
code span.cn { color: #880000; } /* Constant */
|
||
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
|
||
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
|
||
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
|
||
code span.dt { color: #902000; } /* DataType */
|
||
code span.dv { color: #40a070; } /* DecVal */
|
||
code span.er { color: #ff0000; font-weight: bold; } /* Error */
|
||
code span.ex { } /* Extension */
|
||
code span.fl { color: #40a070; } /* Float */
|
||
code span.fu { color: #06287e; } /* Function */
|
||
code span.im { } /* Import */
|
||
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
|
||
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
|
||
code span.op { color: #666666; } /* Operator */
|
||
code span.ot { color: #007020; } /* Other */
|
||
code span.pp { color: #bc7a00; } /* Preprocessor */
|
||
code span.sc { color: #4070a0; } /* SpecialChar */
|
||
code span.ss { color: #bb6688; } /* SpecialString */
|
||
code span.st { color: #4070a0; } /* String */
|
||
code span.va { color: #19177c; } /* Variable */
|
||
code span.vs { color: #4070a0; } /* VerbatimString */
|
||
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
|
||
|
||
/* A workaround for https://github.com/jgm/pandoc/issues/4278 */
|
||
a.sourceLine {
|
||
pointer-events: auto;
|
||
}
|
||
|
||
</style>
|
||
<script>
|
||
// apply pandoc div.sourceCode style to pre.sourceCode instead
|
||
(function() {
|
||
var sheets = document.styleSheets;
|
||
for (var i = 0; i < sheets.length; i++) {
|
||
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
|
||
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
|
||
for (var j = 0; j < rules.length; j++) {
|
||
var rule = rules[j];
|
||
// check if there is a div.sourceCode rule
|
||
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
|
||
var style = rule.style.cssText;
|
||
// check if color or background-color is set
|
||
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
|
||
// replace div.sourceCode by a pre.sourceCode rule
|
||
sheets[i].deleteRule(j);
|
||
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
|
||
}
|
||
}
|
||
})();
|
||
</script>
|
||
|
||
|
||
|
||
<style type="text/css">@font-face{font-family:"Open Sans";font-style:normal;font-weight:400;src:local("Open Sans"),local("OpenSans"),url(data:application/font-woff;base64,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) format("woff")}@font-face{font-family:"Open Sans";font-style:normal;font-weight:700;src:local("Open Sans Bold"),local("OpenSans-Bold"),url(data:application/font-woff;base64,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) format("woff")}*{box-sizing:border-box}body{padding:0;margin:0;font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.5;color:#606c71}a{color:#1e6bb8;text-decoration:none}a:hover{text-decoration:underline}.page-header{color:#fff;text-align:center;background-color:#159957;background-image:linear-gradient(120deg,#155799,#159957);padding:1.5rem 2rem}.page-header :last-child{margin-bottom:.5rem}@media screen and (max-width:42em){.page-header{padding:1rem 1rem}}.project-name{margin-top:0;margin-bottom:.1rem;font-size:2rem}@media screen and (max-width:42em){.project-name{font-size:1.75rem}}.project-tagline{margin-bottom:2rem;font-weight:400;opacity:.7;font-size:1.5rem}@media screen and (max-width:42em){.project-tagline{font-size:1.2rem}}.project-author,.project-date{font-weight:400;opacity:.7;font-size:1.2rem}@media screen and (max-width:42em){.project-author,.project-date{font-size:1rem}}.main-content,.toc{max-width:64rem;padding:2rem 4rem;margin:0 auto;font-size:1.1rem}.toc{padding-bottom:0}.toc .toc-box{padding:1.5rem;background-color:#f3f6fa;border:solid 1px #dce6f0;border-radius:.3rem}.toc .toc-box .toc-title{margin:0 0 .5rem;text-align:center}.toc .toc-box>ul{margin:0;padding-left:1.5rem}@media screen and (min-width:42em) and (max-width:64em){.toc{padding:2rem 2rem 0}}@media screen and (max-width:42em){.toc{padding:2rem 1rem 0;font-size:1rem}}.main-content :first-child{margin-top:0}@media screen and (min-width:42em) and (max-width:64em){.main-content{padding:2rem}}@media screen and (max-width:42em){.main-content{padding:2rem 1rem;font-size:1rem}}.main-content img{max-width:100%}.main-content h1,.main-content h2,.main-content h3,.main-content h4,.main-content h5,.main-content h6{margin-top:2rem;margin-bottom:1rem;font-weight:400;color:#159957}.main-content p{margin-bottom:1em}.main-content code{padding:2px 4px;font-family:Consolas,"Liberation Mono",Menlo,Courier,monospace;color:#567482;background-color:#f3f6fa;border-radius:.3rem}.main-content pre{padding:.8rem;margin-top:0;margin-bottom:1rem;font:1rem Consolas,"Liberation Mono",Menlo,Courier,monospace;color:#567482;word-wrap:normal;background-color:#f3f6fa;border:solid 1px #dce6f0;border-radius:.3rem;line-height:1.45;overflow:auto}@media screen and (max-width:42em){.main-content pre{font-size:.9rem}}.main-content pre>code{padding:0;margin:0;color:#567482;word-break:normal;white-space:pre;background:0 0;border:0}@media screen and (max-width:42em){.main-content pre>code{font-size:.9rem}}.main-content pre code,.main-content pre tt{display:inline;max-width:initial;padding:0;margin:0;overflow:initial;line-height:inherit;word-wrap:normal;background-color:transparent;border:0}.main-content pre code:after,.main-content pre code:before,.main-content pre tt:after,.main-content pre tt:before{content:normal}.main-content ol,.main-content ul{margin-top:0}.main-content blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; }
|
||
code span.dt { color: #795da3; }
|
||
code span.dv { color: #0086b3; }
|
||
code span.bn { color: #0086b3; }
|
||
code span.fl { color: #0086b3; }
|
||
code span.ch { color: #4070a0; }
|
||
code span.st { color: #183691; }
|
||
code span.co { color: #969896; font-style: italic; }
|
||
code span.ot { color: #007020; }
|
||
</style>
|
||
|
||
|
||
|
||
|
||
|
||
</head>
|
||
|
||
<body>
|
||
|
||
|
||
|
||
|
||
<section class="page-header">
|
||
<h1 class="title toc-ignore project-name"><div class="line-block">DataWhale 组队学习 R语言数据分析<br />
|
||
Task03 基础统计分析</div></h1>
|
||
<h4 class="author project-author">杨佳达</h4>
|
||
<h4 class="date project-date">2021-07-15</h4>
|
||
</section>
|
||
|
||
|
||
|
||
<section class="main-content">
|
||
<p><img src="data:image/png;base64,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" style="width:100.0%" /></p>
|
||
<div id="基础统计分析" class="section level1">
|
||
<h1>基础统计分析</h1>
|
||
<div id="准备工作" class="section level2">
|
||
<h2>准备工作</h2>
|
||
<p>如果没有相关的包，则使用<code>install.packages('package_name')</code>进行安装以下包。</p>
|
||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(pastecs)</span>
|
||
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(psych)</span>
|
||
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(ggm)</span></code></pre></div>
|
||
<p>读取数据，使用H1N1流感数据集和波士顿房价数据集。</p>
|
||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>flu =<span class="st"> </span><span class="kw">read.table</span>(<span class="st">"./datasets/h1n1_flu.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>, <span class="dt">sep =</span> <span class="st">","</span>)</span>
|
||
<span id="cb2-2"><a href="#cb2-2"></a>housing =<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"./datasets/BostonHousing.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</span></code></pre></div>
|
||
</div>
|
||
<div id="多种方法获取描述性统计量" class="section level2">
|
||
<h2>1 多种方法获取描述性统计量</h2>
|
||
<div id="基础方法" class="section level3">
|
||
<h3>1.1 基础方法</h3>
|
||
<p>通过summary计算数值型变量的最大值、最小值、分位数以及均值，类别变量计算频数统计。</p>
|
||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">summary</span>(flu[<span class="kw">c</span>(<span class="st">"household_children"</span>, <span class="st">"sex"</span>)])</span></code></pre></div>
|
||
<pre><code>## household_children sex
|
||
## Min. :0.0000 Length:26707
|
||
## 1st Qu.:0.0000 Class :character
|
||
## Median :0.0000 Mode :character
|
||
## Mean :0.5346
|
||
## 3rd Qu.:1.0000
|
||
## Max. :3.0000
|
||
## NA's :249</code></pre>
|
||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">summary</span>(flu[<span class="kw">c</span>(<span class="st">"h1n1_concern"</span>, <span class="st">"h1n1_knowledge"</span>)])</span></code></pre></div>
|
||
<pre><code>## h1n1_concern h1n1_knowledge
|
||
## Min. :0.000 Min. :0.000
|
||
## 1st Qu.:1.000 1st Qu.:1.000
|
||
## Median :2.000 Median :1.000
|
||
## Mean :1.618 Mean :1.263
|
||
## 3rd Qu.:2.000 3rd Qu.:2.000
|
||
## Max. :3.000 Max. :2.000
|
||
## NA's :92 NA's :116</code></pre>
|
||
<p>通过 sapply() 计算描述性统计量，先定义统计函数，在进行聚合计算。</p>
|
||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>mystats <-<span class="st"> </span><span class="cf">function</span>(x, <span class="dt">na.omit =</span> <span class="ot">FALSE</span>) {</span>
|
||
<span id="cb7-2"><a href="#cb7-2"></a> <span class="cf">if</span> (na.omit)</span>
|
||
<span id="cb7-3"><a href="#cb7-3"></a> x <-<span class="st"> </span>x[<span class="op">!</span><span class="kw">is.na</span>(x)]</span>
|
||
<span id="cb7-4"><a href="#cb7-4"></a> m <-<span class="st"> </span><span class="kw">mean</span>(x)</span>
|
||
<span id="cb7-5"><a href="#cb7-5"></a> n <-<span class="st"> </span><span class="kw">length</span>(x)</span>
|
||
<span id="cb7-6"><a href="#cb7-6"></a> s <-<span class="st"> </span><span class="kw">sd</span>(x)</span>
|
||
<span id="cb7-7"><a href="#cb7-7"></a> skew <-<span class="st"> </span><span class="kw">sum</span>((x <span class="op">-</span><span class="st"> </span>m)<span class="op">^</span><span class="dv">3</span><span class="op">/</span>s<span class="op">^</span><span class="dv">3</span>)<span class="op">/</span>n</span>
|
||
<span id="cb7-8"><a href="#cb7-8"></a> kurt <-<span class="st"> </span><span class="kw">sum</span>((x <span class="op">-</span><span class="st"> </span>m)<span class="op">^</span><span class="dv">4</span><span class="op">/</span>s<span class="op">^</span><span class="dv">4</span>)<span class="op">/</span>n <span class="op">-</span><span class="st"> </span><span class="dv">3</span></span>
|
||
<span id="cb7-9"><a href="#cb7-9"></a> <span class="kw">return</span>(<span class="kw">c</span>(<span class="dt">n =</span> n, <span class="dt">mean =</span> m, <span class="dt">stdev =</span> s, <span class="dt">skew =</span> skew, <span class="dt">kurtosis =</span> kurt))</span>
|
||
<span id="cb7-10"><a href="#cb7-10"></a>}</span>
|
||
<span id="cb7-11"><a href="#cb7-11"></a></span>
|
||
<span id="cb7-12"><a href="#cb7-12"></a><span class="kw">sapply</span>(flu[<span class="kw">c</span>(<span class="st">"h1n1_concern"</span>, <span class="st">"h1n1_knowledge"</span>)], mystats)</span></code></pre></div>
|
||
<pre><code>## h1n1_concern h1n1_knowledge
|
||
## n 26707 26707
|
||
## mean NA NA
|
||
## stdev NA NA
|
||
## skew NA NA
|
||
## kurtosis NA NA</code></pre>
|
||
</div>
|
||
<div id="拓展包方法" class="section level3">
|
||
<h3>1.2 拓展包方法</h3>
|
||
<p>通过pastecs包中的 stat.desc()函数计算描述性统计量，可以得到中位数、平均数、平均数的标准误、平均数置信度为95%的置信区间、方差、标准差以及变异系数。</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">stat.desc</span>(flu[<span class="kw">c</span>(<span class="st">"household_children"</span>, <span class="st">"sex"</span>)])</span></code></pre></div>
|
||
<pre><code>## household_children sex
|
||
## nbr.val 2.645800e+04 NA
|
||
## nbr.null 1.867200e+04 NA
|
||
## nbr.na 2.490000e+02 NA
|
||
## min 0.000000e+00 NA
|
||
## max 3.000000e+00 NA
|
||
## range 3.000000e+00 NA
|
||
## sum 1.414400e+04 NA
|
||
## median 0.000000e+00 NA
|
||
## mean 5.345831e-01 NA
|
||
## SE.mean 5.706247e-03 NA
|
||
## CI.mean.0.95 1.118455e-02 NA
|
||
## var 8.615057e-01 NA
|
||
## std.dev 9.281733e-01 NA
|
||
## coef.var 1.736256e+00 NA</code></pre>
|
||
<p>通过psych包中的describe()计算描述性统计量。</p>
|
||
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="kw">describe</span>(flu[<span class="kw">c</span>(<span class="st">"household_children"</span>, <span class="st">"sex"</span>)])</span></code></pre></div>
|
||
<pre><code>## vars n mean sd median trimmed mad min max range skew
|
||
## household_children 1 26458 0.53 0.93 0 0.34 0 0 3 3 1.54
|
||
## sex* 2 26707 1.41 0.49 1 1.38 0 1 2 1 0.38
|
||
## kurtosis se
|
||
## household_children 1.04 0.01
|
||
## sex* -1.85 0.00</code></pre>
|
||
</div>
|
||
</div>
|
||
<div id="分组计算描述性统计" class="section level2">
|
||
<h2>2 分组计算描述性统计</h2>
|
||
<div id="基础方法-1" class="section level3">
|
||
<h3>2.1 基础方法</h3>
|
||
<div id="使用aggregate分组获取描述性统计" class="section level4">
|
||
<h4>使用aggregate()分组获取描述性统计</h4>
|
||
<ol style="list-style-type: decimal">
|
||
<li>分组计算不同性别收入贫困计数。</li>
|
||
<li>是否属于查尔斯河的房价中位数平均值。</li>
|
||
</ol>
|
||
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="kw">aggregate</span>(flu[<span class="kw">c</span>(<span class="st">"income_poverty"</span>)], <span class="dt">by =</span> <span class="kw">list</span>(<span class="dt">sex =</span> flu<span class="op">$</span>sex), length)</span></code></pre></div>
|
||
<pre><code>## sex income_poverty
|
||
## 1 Female 15858
|
||
## 2 Male 10849</code></pre>
|
||
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a><span class="kw">aggregate</span>(housing<span class="op">$</span>medv, <span class="dt">by =</span> <span class="kw">list</span>(<span class="dt">medv =</span> housing<span class="op">$</span>chas), <span class="dt">FUN =</span> mean)</span></code></pre></div>
|
||
<pre><code>## medv x
|
||
## 1 0 22.09384
|
||
## 2 1 28.44000</code></pre>
|
||
</div>
|
||
<div id="使用-by-分组计算描述性统计量" class="section level4">
|
||
<h4>使用 by() 分组计算描述性统计量</h4>
|
||
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a><span class="kw">by</span>(flu[<span class="kw">c</span>(<span class="st">"income_poverty"</span>, <span class="st">"sex"</span>)], flu<span class="op">$</span>sex, length)</span></code></pre></div>
|
||
<pre><code>## flu$sex: Female
|
||
## [1] 2
|
||
## ------------------------------------------------------------
|
||
## flu$sex: Male
|
||
## [1] 2</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div id="频数表和列联表" class="section level2">
|
||
<h2>3 频数表和列联表</h2>
|
||
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="kw">table</span>(flu<span class="op">$</span>sex)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Female Male
|
||
## 15858 10849</code></pre>
|
||
</div>
|
||
<div id="相关" class="section level2">
|
||
<h2>4 相关</h2>
|
||
<div id="相关的类型" class="section level3">
|
||
<h3>4.1 相关的类型</h3>
|
||
<div id="pearsonspearman和kendall相关" class="section level4">
|
||
<h4>Pearson、Spearman和Kendall相关</h4>
|
||
<p>R可以计算多种相关系数，包括Pearson相关系数、Spearman相关系数、Kendall相关系数、偏相关系数、多分格（polychoric）相关系数和多系列（polyserial）相关系数。 1. 计算房价数据的相关系数，默认是Pearson相关系数。</p>
|
||
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="kw">cor</span>(housing)</span></code></pre></div>
|
||
<pre><code>## X crim zn indus chas
|
||
## X 1.000000000 0.40740717 -0.10339336 0.39943885 -0.003759115
|
||
## crim 0.407407172 1.00000000 -0.20046922 0.40658341 -0.055891582
|
||
## zn -0.103393357 -0.20046922 1.00000000 -0.53382819 -0.042696719
|
||
## indus 0.399438850 0.40658341 -0.53382819 1.00000000 0.062938027
|
||
## chas -0.003759115 -0.05589158 -0.04269672 0.06293803 1.000000000
|
||
## nox 0.398736174 0.42097171 -0.51660371 0.76365145 0.091202807
|
||
## rm -0.079971150 -0.21924670 0.31199059 -0.39167585 0.091251225
|
||
## age 0.203783510 0.35273425 -0.56953734 0.64477851 0.086517774
|
||
## dis -0.302210959 -0.37967009 0.66440822 -0.70802699 -0.099175780
|
||
## rad 0.686001976 0.62550515 -0.31194783 0.59512927 -0.007368241
|
||
## tax 0.666625924 0.58276431 -0.31456332 0.72076018 -0.035586518
|
||
## ptratio 0.291074227 0.28994558 -0.39167855 0.38324756 -0.121515174
|
||
## b -0.295041232 -0.38506394 0.17552032 -0.35697654 0.048788485
|
||
## lstat 0.258464770 0.45562148 -0.41299457 0.60379972 -0.053929298
|
||
## medv -0.226603643 -0.38830461 0.36044534 -0.48372516 0.175260177
|
||
## nox rm age dis rad
|
||
## X 0.39873617 -0.07997115 0.20378351 -0.30221096 0.686001976
|
||
## crim 0.42097171 -0.21924670 0.35273425 -0.37967009 0.625505145
|
||
## zn -0.51660371 0.31199059 -0.56953734 0.66440822 -0.311947826
|
||
## indus 0.76365145 -0.39167585 0.64477851 -0.70802699 0.595129275
|
||
## chas 0.09120281 0.09125123 0.08651777 -0.09917578 -0.007368241
|
||
## nox 1.00000000 -0.30218819 0.73147010 -0.76923011 0.611440563
|
||
## rm -0.30218819 1.00000000 -0.24026493 0.20524621 -0.209846668
|
||
## age 0.73147010 -0.24026493 1.00000000 -0.74788054 0.456022452
|
||
## dis -0.76923011 0.20524621 -0.74788054 1.00000000 -0.494587930
|
||
## rad 0.61144056 -0.20984667 0.45602245 -0.49458793 1.000000000
|
||
## tax 0.66802320 -0.29204783 0.50645559 -0.53443158 0.910228189
|
||
## ptratio 0.18893268 -0.35550149 0.26151501 -0.23247054 0.464741179
|
||
## b -0.38005064 0.12806864 -0.27353398 0.29151167 -0.444412816
|
||
## lstat 0.59087892 -0.61380827 0.60233853 -0.49699583 0.488676335
|
||
## medv -0.42732077 0.69535995 -0.37695457 0.24992873 -0.381626231
|
||
## tax ptratio b lstat medv
|
||
## X 0.66662592 0.2910742 -0.29504123 0.2584648 -0.2266036
|
||
## crim 0.58276431 0.2899456 -0.38506394 0.4556215 -0.3883046
|
||
## zn -0.31456332 -0.3916785 0.17552032 -0.4129946 0.3604453
|
||
## indus 0.72076018 0.3832476 -0.35697654 0.6037997 -0.4837252
|
||
## chas -0.03558652 -0.1215152 0.04878848 -0.0539293 0.1752602
|
||
## nox 0.66802320 0.1889327 -0.38005064 0.5908789 -0.4273208
|
||
## rm -0.29204783 -0.3555015 0.12806864 -0.6138083 0.6953599
|
||
## age 0.50645559 0.2615150 -0.27353398 0.6023385 -0.3769546
|
||
## dis -0.53443158 -0.2324705 0.29151167 -0.4969958 0.2499287
|
||
## rad 0.91022819 0.4647412 -0.44441282 0.4886763 -0.3816262
|
||
## tax 1.00000000 0.4608530 -0.44180801 0.5439934 -0.4685359
|
||
## ptratio 0.46085304 1.0000000 -0.17738330 0.3740443 -0.5077867
|
||
## b -0.44180801 -0.1773833 1.00000000 -0.3660869 0.3334608
|
||
## lstat 0.54399341 0.3740443 -0.36608690 1.0000000 -0.7376627
|
||
## medv -0.46853593 -0.5077867 0.33346082 -0.7376627 1.0000000</code></pre>
|
||
<ol start="2" style="list-style-type: decimal">
|
||
<li>指定计算Spearman相关系数</li>
|
||
</ol>
|
||
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a><span class="kw">cor</span>(housing, <span class="dt">method =</span> <span class="st">"spearman"</span>)</span></code></pre></div>
|
||
<pre><code>## X crim zn indus chas
|
||
## X 1.000000000 0.46103705 -0.1605047 0.32462127 -0.003759115
|
||
## crim 0.461037054 1.00000000 -0.5716602 0.73552374 0.041536888
|
||
## zn -0.160504702 -0.57166021 1.0000000 -0.64281060 -0.041936998
|
||
## indus 0.324621271 0.73552374 -0.6428106 1.00000000 0.089841379
|
||
## chas -0.003759115 0.04153689 -0.0419370 0.08984138 1.000000000
|
||
## nox 0.432491886 0.82146466 -0.6348284 0.79118913 0.068426283
|
||
## rm -0.035641354 -0.30911647 0.3610737 -0.41530129 0.058812916
|
||
## age 0.208323439 0.70413998 -0.5444226 0.67948671 0.067791779
|
||
## dis -0.373498683 -0.74498614 0.6146265 -0.75707970 -0.080248080
|
||
## rad 0.588480705 0.72780697 -0.2787672 0.45550745 0.024578885
|
||
## tax 0.536928176 0.72904490 -0.3713945 0.66436139 -0.044485772
|
||
## ptratio 0.297897432 0.46528319 -0.4484754 0.43371046 -0.136064621
|
||
## b -0.154474321 -0.36055532 0.1631351 -0.28583984 -0.039810497
|
||
## lstat 0.257542491 0.63476026 -0.4900739 0.63874741 -0.050574829
|
||
## medv -0.273633481 -0.55889095 0.4381790 -0.57825539 0.140612154
|
||
## nox rm age dis rad tax
|
||
## X 0.43249189 -0.03564135 0.20832344 -0.37349868 0.58848071 0.53692818
|
||
## crim 0.82146466 -0.30911647 0.70413998 -0.74498614 0.72780697 0.72904490
|
||
## zn -0.63482840 0.36107373 -0.54442256 0.61462654 -0.27876717 -0.37139450
|
||
## indus 0.79118913 -0.41530129 0.67948671 -0.75707970 0.45550745 0.66436139
|
||
## chas 0.06842628 0.05881292 0.06779178 -0.08024808 0.02457888 -0.04448577
|
||
## nox 1.00000000 -0.31034391 0.79515291 -0.88001486 0.58642870 0.64952656
|
||
## rm -0.31034391 1.00000000 -0.27808202 0.26316822 -0.10749220 -0.27189846
|
||
## age 0.79515291 -0.27808202 1.00000000 -0.80160979 0.41798261 0.52636644
|
||
## dis -0.88001486 0.26316822 -0.80160979 1.00000000 -0.49580647 -0.57433641
|
||
## rad 0.58642870 -0.10749220 0.41798261 -0.49580647 1.00000000 0.70487572
|
||
## tax 0.64952656 -0.27189846 0.52636644 -0.57433641 0.70487572 1.00000000
|
||
## ptratio 0.39130908 -0.31292257 0.35538428 -0.32204056 0.31832966 0.45334546
|
||
## b -0.29666158 0.05366004 -0.22802200 0.24959532 -0.28253261 -0.32984308
|
||
## lstat 0.63682829 -0.64083156 0.65707079 -0.56426219 0.39432245 0.53442319
|
||
## medv -0.56260883 0.63357643 -0.54756169 0.44585685 -0.34677626 -0.56241063
|
||
## ptratio b lstat medv
|
||
## X 0.29789743 -0.15447432 0.25754249 -0.2736335
|
||
## crim 0.46528319 -0.36055532 0.63476026 -0.5588909
|
||
## zn -0.44847543 0.16313510 -0.49007389 0.4381790
|
||
## indus 0.43371046 -0.28583984 0.63874741 -0.5782554
|
||
## chas -0.13606462 -0.03981050 -0.05057483 0.1406122
|
||
## nox 0.39130908 -0.29666158 0.63682829 -0.5626088
|
||
## rm -0.31292257 0.05366004 -0.64083156 0.6335764
|
||
## age 0.35538428 -0.22802200 0.65707079 -0.5475617
|
||
## dis -0.32204056 0.24959532 -0.56426219 0.4458569
|
||
## rad 0.31832966 -0.28253261 0.39432245 -0.3467763
|
||
## tax 0.45334546 -0.32984308 0.53442319 -0.5624106
|
||
## ptratio 1.00000000 -0.07202734 0.46725885 -0.5559047
|
||
## b -0.07202734 1.00000000 -0.21056185 0.1856641
|
||
## lstat 0.46725885 -0.21056185 1.00000000 -0.8529141
|
||
## medv -0.55590468 0.18566412 -0.85291414 1.0000000</code></pre>
|
||
<ol start="3" style="list-style-type: decimal">
|
||
<li>城镇人均犯罪率与房价的相关系数</li>
|
||
</ol>
|
||
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a>x <-<span class="st"> </span>housing</span>
|
||
<span id="cb25-2"><a href="#cb25-2"></a>y <-<span class="st"> </span>housing[<span class="kw">c</span>(<span class="st">"medv"</span>)]</span>
|
||
<span id="cb25-3"><a href="#cb25-3"></a><span class="kw">cor</span>(x, y)</span></code></pre></div>
|
||
<pre><code>## medv
|
||
## X -0.2266036
|
||
## crim -0.3883046
|
||
## zn 0.3604453
|
||
## indus -0.4837252
|
||
## chas 0.1752602
|
||
## nox -0.4273208
|
||
## rm 0.6953599
|
||
## age -0.3769546
|
||
## dis 0.2499287
|
||
## rad -0.3816262
|
||
## tax -0.4685359
|
||
## ptratio -0.5077867
|
||
## b 0.3334608
|
||
## lstat -0.7376627
|
||
## medv 1.0000000</code></pre>
|
||
</div>
|
||
<div id="偏相关" class="section level4">
|
||
<h4>偏相关</h4>
|
||
<p>偏相关是指在控制一个或多个定量变量时，另外两个定量变量之间的相互关系。使用ggm 包中的 pcor() 函数计算偏相关系数。</p>
|
||
</div>
|
||
</div>
|
||
<div id="相关性的显著性检验" class="section level3">
|
||
<h3>4.2 相关性的显著性检验</h3>
|
||
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="kw">cor.test</span>(housing[, <span class="kw">c</span>(<span class="st">"crim"</span>)], housing[, <span class="kw">c</span>(<span class="st">"medv"</span>)])</span></code></pre></div>
|
||
<pre><code>##
|
||
## Pearson's product-moment correlation
|
||
##
|
||
## data: housing[, c("crim")] and housing[, c("medv")]
|
||
## t = -9.4597, df = 504, p-value < 2.2e-16
|
||
## alternative hypothesis: true correlation is not equal to 0
|
||
## 95 percent confidence interval:
|
||
## -0.4599064 -0.3116859
|
||
## sample estimates:
|
||
## cor
|
||
## -0.3883046</code></pre>
|
||
</div>
|
||
</div>
|
||
<div id="方差分析" class="section level2">
|
||
<h2>5 方差分析</h2>
|
||
<p>方差分析（ANOVA）又称“变异数分析”或“F检验”，用于两个及两个以上样本均数差别的显著性检验。</p>
|
||
<div id="单因素方差分析" class="section level3">
|
||
<h3>5.1 单因素方差分析</h3>
|
||
<p>从输出结果的F检验值来看，p<0.05比较显著，说明是否在查尔斯河对房价有影响。</p>
|
||
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a>fit <-<span class="st"> </span><span class="kw">aov</span>(housing<span class="op">$</span>medv <span class="op">~</span><span class="st"> </span>housing<span class="op">$</span>chas)</span>
|
||
<span id="cb29-2"><a href="#cb29-2"></a><span class="kw">summary</span>(fit)</span></code></pre></div>
|
||
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
|
||
## housing$chas 1 1312 1312.1 15.97 7.39e-05 ***
|
||
## Residuals 504 41404 82.2
|
||
## ---
|
||
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
|
||
</div>
|
||
<div id="多因素方差分析" class="section level3">
|
||
<h3>5.2 多因素方差分析</h3>
|
||
<p>构建多因素方差分析，查看因子对房价的影响是否显著。</p>
|
||
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a>fit <-<span class="st"> </span><span class="kw">aov</span>(housing<span class="op">$</span>medv <span class="op">~</span><span class="st"> </span>housing<span class="op">$</span>crim <span class="op">*</span><span class="st"> </span>housing<span class="op">$</span>b)</span>
|
||
<span id="cb31-2"><a href="#cb31-2"></a><span class="kw">summary</span>(fit)</span></code></pre></div>
|
||
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
|
||
## housing$crim 1 6441 6441 96.05 < 2e-16 ***
|
||
## housing$b 1 1697 1697 25.30 6.83e-07 ***
|
||
## housing$crim:housing$b 1 917 917 13.68 0.000241 ***
|
||
## Residuals 502 33662 67
|
||
## ---
|
||
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
|
||
|
||
<!-- 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>
|