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<title>第 3 章 基本统计分析 | R语言数据分析组队学习</title>
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<meta name="author" content="张晋、杨佳达、牧小熊、杨杨卓然、姚昱君" />
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<ul class="summary">
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<li><a href="./">R语言数据分析组队学习</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>欢迎！</a>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html#贡献者信息"><i class="fa fa-check"></i>贡献者信息</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html#课程简介"><i class="fa fa-check"></i>课程简介</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html#课程大纲"><i class="fa fa-check"></i>课程大纲</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html#关于-datawhale"><i class="fa fa-check"></i>关于 Datawhale</a></li>
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</ul></li>
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<li class="part"><span><b>I 准备工作</b></span></li>
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<li class="chapter" data-level="" data-path="task-00.html"><a href="task-00.html"><i class="fa fa-check"></i>熟悉规则与R语言入门</a>
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<ul>
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<li class="chapter" data-level="0.1" data-path="task-00.html"><a href="task-00.html#安装"><i class="fa fa-check"></i><b>0.1</b> 安装</a>
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<ul>
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<li class="chapter" data-level="0.1.1" data-path="task-00.html"><a href="task-00.html#r"><i class="fa fa-check"></i><b>0.1.1</b> R</a></li>
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<li class="chapter" data-level="0.1.2" data-path="task-00.html"><a href="task-00.html#rstudio"><i class="fa fa-check"></i><b>0.1.2</b> RStudio</a></li>
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<li class="chapter" data-level="0.1.3" data-path="task-00.html"><a href="task-00.html#r语言程辑包r-package"><i class="fa fa-check"></i><b>0.1.3</b> R语言程辑包（R Package）</a></li>
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</ul></li>
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<li class="chapter" data-level="0.2" data-path="task-00.html"><a href="task-00.html#环境配置"><i class="fa fa-check"></i><b>0.2</b> 环境配置</a>
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<ul>
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<li class="chapter" data-level="0.2.1" data-path="task-00.html"><a href="task-00.html#项目project"><i class="fa fa-check"></i><b>0.2.1</b> 项目（Project）</a></li>
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<li class="chapter" data-level="0.2.2" data-path="task-00.html"><a href="task-00.html#用户界面"><i class="fa fa-check"></i><b>0.2.2</b> 用户界面</a></li>
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<li class="chapter" data-level="0.2.3" data-path="task-00.html"><a href="task-00.html#r-markdown"><i class="fa fa-check"></i><b>0.2.3</b> R Markdown</a></li>
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<li class="chapter" data-level="0.2.4" data-path="task-00.html"><a href="task-00.html#帮助"><i class="fa fa-check"></i><b>0.2.4</b> 帮助</a></li>
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</ul></li>
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<li class="chapter" data-level="0.3" data-path="task-00.html"><a href="task-00.html#happy-coding"><i class="fa fa-check"></i><b>0.3</b> Happy Coding!</a></li>
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<li class="chapter" data-level="" data-path="task-00.html"><a href="task-00.html#本章作者"><i class="fa fa-check"></i>本章作者</a></li>
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<li class="chapter" data-level="" data-path="task-00.html"><a href="task-00.html#关于datawhale"><i class="fa fa-check"></i>关于Datawhale</a></li>
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</ul></li>
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<li class="part"><span><b>II 开始干活</b></span></li>
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<li class="chapter" data-level="1" data-path="task-01.html"><a href="task-01.html"><i class="fa fa-check"></i><b>1</b> 数据结构与数据集</a>
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<ul>
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<li class="chapter" data-level="1.1" data-path="task-01.html"><a href="task-01.html#准备工作"><i class="fa fa-check"></i><b>1.1</b> 准备工作</a></li>
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<li class="chapter" data-level="1.2" data-path="task-01.html"><a href="task-01.html#编码基础"><i class="fa fa-check"></i><b>1.2</b> 编码基础</a>
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<ul>
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<li class="chapter" data-level="1.2.1" data-path="task-01.html"><a href="task-01.html#算术"><i class="fa fa-check"></i><b>1.2.1</b> 算术</a></li>
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<li class="chapter" data-level="1.2.2" data-path="task-01.html"><a href="task-01.html#赋值"><i class="fa fa-check"></i><b>1.2.2</b> 赋值</a></li>
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<li class="chapter" data-level="1.2.3" data-path="task-01.html"><a href="task-01.html#函数"><i class="fa fa-check"></i><b>1.2.3</b> 函数</a></li>
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<li class="chapter" data-level="1.2.4" data-path="task-01.html"><a href="task-01.html#循环loop"><i class="fa fa-check"></i><b>1.2.4</b> 循环（loop）</a></li>
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<li class="chapter" data-level="1.2.5" data-path="task-01.html"><a href="task-01.html#管道pipe"><i class="fa fa-check"></i><b>1.2.5</b> 管道（pipe）</a></li>
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</ul></li>
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<li class="chapter" data-level="1.3" data-path="task-01.html"><a href="task-01.html#数据类型"><i class="fa fa-check"></i><b>1.3</b> 数据类型</a>
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<ul>
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<li class="chapter" data-level="1.3.1" data-path="task-01.html"><a href="task-01.html#基础数据类型"><i class="fa fa-check"></i><b>1.3.1</b> 基础数据类型</a></li>
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<li class="chapter" data-level="1.3.2" data-path="task-01.html"><a href="task-01.html#向量vector"><i class="fa fa-check"></i><b>1.3.2</b> 向量（vector）</a></li>
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<li class="chapter" data-level="1.3.3" data-path="task-01.html"><a href="task-01.html#特殊数据类型"><i class="fa fa-check"></i><b>1.3.3</b> 特殊数据类型</a></li>
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||
</ul></li>
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<li class="chapter" data-level="1.4" data-path="task-01.html"><a href="task-01.html#多维数据类型"><i class="fa fa-check"></i><b>1.4</b> 多维数据类型</a>
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<ul>
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<li class="chapter" data-level="1.4.1" data-path="task-01.html"><a href="task-01.html#矩阵matrix"><i class="fa fa-check"></i><b>1.4.1</b> 矩阵（matrix）</a></li>
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<li class="chapter" data-level="1.4.2" data-path="task-01.html"><a href="task-01.html#列表list"><i class="fa fa-check"></i><b>1.4.2</b> 列表（list）</a></li>
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||
<li class="chapter" data-level="1.4.3" data-path="task-01.html"><a href="task-01.html#数据表data-frame-与-tibble"><i class="fa fa-check"></i><b>1.4.3</b> 数据表（data frame 与 tibble）</a></li>
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||
</ul></li>
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||
<li class="chapter" data-level="1.5" data-path="task-01.html"><a href="task-01.html#读写数据"><i class="fa fa-check"></i><b>1.5</b> 读写数据</a>
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||
<ul>
|
||
<li class="chapter" data-level="1.5.1" data-path="task-01.html"><a href="task-01.html#内置数据集"><i class="fa fa-check"></i><b>1.5.1</b> 内置数据集</a></li>
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<li class="chapter" data-level="1.5.2" data-path="task-01.html"><a href="task-01.html#表格类型数据csv-excel"><i class="fa fa-check"></i><b>1.5.2</b> 表格类型数据（csv, excel)</a></li>
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||
<li class="chapter" data-level="1.5.3" data-path="task-01.html"><a href="task-01.html#r的专属类型数据rdata-rds"><i class="fa fa-check"></i><b>1.5.3</b> R的专属类型数据（RData, rds）</a></li>
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<li class="chapter" data-level="1.5.4" data-path="task-01.html"><a href="task-01.html#其他软件spss-stata-sas"><i class="fa fa-check"></i><b>1.5.4</b> 其他软件（SPSS, Stata, SAS）</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="1.6" data-path="task-01.html"><a href="task-01.html#练习题"><i class="fa fa-check"></i><b>1.6</b> 练习题</a>
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||
<ul>
|
||
<li class="chapter" data-level="1.6.1" data-path="task-01.html"><a href="task-01.html#了解数据集"><i class="fa fa-check"></i><b>1.6.1</b> 了解数据集</a></li>
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<li class="chapter" data-level="1.6.2" data-path="task-01.html"><a href="task-01.html#创造数据集"><i class="fa fa-check"></i><b>1.6.2</b> 创造数据集</a></li>
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||
</ul></li>
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||
<li class="chapter" data-level="" data-path="task-01.html"><a href="task-01.html#本章作者-1"><i class="fa fa-check"></i>本章作者</a></li>
|
||
<li class="chapter" data-level="" data-path="task-01.html"><a href="task-01.html#关于datawhale-1"><i class="fa fa-check"></i>关于Datawhale</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="2" data-path="task-02.html"><a href="task-02.html"><i class="fa fa-check"></i><b>2</b> 数据清洗与准备</a>
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||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#环境配置-1"><i class="fa fa-check"></i>环境配置</a></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#案例数据"><i class="fa fa-check"></i>案例数据</a>
|
||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#数据集1-h1n1流感问卷数据集"><i class="fa fa-check"></i>数据集1 h1n1流感问卷数据集</a></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#数据集2-波士顿房价数据集"><i class="fa fa-check"></i>数据集2 波士顿房价数据集</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="2.1" data-path="task-02.html"><a href="task-02.html#重复值处理"><i class="fa fa-check"></i><b>2.1</b> 重复值处理</a></li>
|
||
<li class="chapter" data-level="2.2" data-path="task-02.html"><a href="task-02.html#缺失值识别与处理"><i class="fa fa-check"></i><b>2.2</b> 缺失值识别与处理</a>
|
||
<ul>
|
||
<li class="chapter" data-level="2.2.1" data-path="task-02.html"><a href="task-02.html#缺失值识别"><i class="fa fa-check"></i><b>2.2.1</b> 缺失值识别</a></li>
|
||
<li class="chapter" data-level="2.2.2" data-path="task-02.html"><a href="task-02.html#缺失值处理"><i class="fa fa-check"></i><b>2.2.2</b> 缺失值处理</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="2.3" data-path="task-02.html"><a href="task-02.html#异常值识别与处理"><i class="fa fa-check"></i><b>2.3</b> 异常值识别与处理</a>
|
||
<ul>
|
||
<li class="chapter" data-level="2.3.1" data-path="task-02.html"><a href="task-02.html#异常值识别"><i class="fa fa-check"></i><b>2.3.1</b> 异常值识别</a></li>
|
||
<li class="chapter" data-level="2.3.2" data-path="task-02.html"><a href="task-02.html#可视化图形分布"><i class="fa fa-check"></i><b>2.3.2</b> 可视化图形分布</a></li>
|
||
<li class="chapter" data-level="2.3.3" data-path="task-02.html"><a href="task-02.html#z-score"><i class="fa fa-check"></i><b>2.3.3</b> z-score</a></li>
|
||
<li class="chapter" data-level="2.3.4" data-path="task-02.html"><a href="task-02.html#局部异常因子法"><i class="fa fa-check"></i><b>2.3.4</b> 局部异常因子法</a></li>
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||
<li class="chapter" data-level="2.3.5" data-path="task-02.html"><a href="task-02.html#异常值处理"><i class="fa fa-check"></i><b>2.3.5</b> 异常值处理</a></li>
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||
</ul></li>
|
||
<li class="chapter" data-level="2.4" data-path="task-02.html"><a href="task-02.html#特征编码"><i class="fa fa-check"></i><b>2.4</b> 特征编码</a>
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<ul>
|
||
<li class="chapter" data-level="2.4.1" data-path="task-02.html"><a href="task-02.html#独热编码哑编码"><i class="fa fa-check"></i><b>2.4.1</b> 独热编码/哑编码</a></li>
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<li class="chapter" data-level="2.4.2" data-path="task-02.html"><a href="task-02.html#标签编码"><i class="fa fa-check"></i><b>2.4.2</b> 标签编码</a></li>
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<li class="chapter" data-level="2.4.3" data-path="task-02.html"><a href="task-02.html#手动编码"><i class="fa fa-check"></i><b>2.4.3</b> 手动编码</a></li>
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<li class="chapter" data-level="2.4.4" data-path="task-02.html"><a href="task-02.html#日期特征转换"><i class="fa fa-check"></i><b>2.4.4</b> 日期特征转换</a></li>
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||
</ul></li>
|
||
<li class="chapter" data-level="2.5" data-path="task-02.html"><a href="task-02.html#规范化与偏态数据"><i class="fa fa-check"></i><b>2.5</b> 规范化与偏态数据</a>
|
||
<ul>
|
||
<li class="chapter" data-level="2.5.1" data-path="task-02.html"><a href="task-02.html#规范化"><i class="fa fa-check"></i><b>2.5.1</b> 0-1规范化</a></li>
|
||
<li class="chapter" data-level="2.5.2" data-path="task-02.html"><a href="task-02.html#z-score标准化"><i class="fa fa-check"></i><b>2.5.2</b> Z-score标准化</a></li>
|
||
<li class="chapter" data-level="2.5.3" data-path="task-02.html"><a href="task-02.html#对数转换log-transform"><i class="fa fa-check"></i><b>2.5.3</b> 对数转换(log transform)</a></li>
|
||
<li class="chapter" data-level="2.5.4" data-path="task-02.html"><a href="task-02.html#box-cox"><i class="fa fa-check"></i><b>2.5.4</b> Box-Cox</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="2.6" data-path="task-02.html"><a href="task-02.html#小拓展"><i class="fa fa-check"></i><b>2.6</b> 小拓展</a></li>
|
||
<li class="chapter" data-level="2.7" data-path="task-02.html"><a href="task-02.html#思考与练习"><i class="fa fa-check"></i><b>2.7</b> 思考与练习</a></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#附录参考资料"><i class="fa fa-check"></i>附录：参考资料</a>
|
||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#理论资料"><i class="fa fa-check"></i>理论资料</a></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#r语言函数用法示例"><i class="fa fa-check"></i>R语言函数用法示例</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#本章作者-2"><i class="fa fa-check"></i>本章作者</a></li>
|
||
<li class="chapter" data-level="" data-path="task-02.html"><a href="task-02.html#关于datawhale-2"><i class="fa fa-check"></i>关于Datawhale</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="3" data-path="task-03.html"><a href="task-03.html"><i class="fa fa-check"></i><b>3</b> 基本统计分析</a>
|
||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-03.html"><a href="task-03.html#准备工作-1"><i class="fa fa-check"></i>准备工作</a></li>
|
||
<li class="chapter" data-level="3.1" data-path="task-03.html"><a href="task-03.html#多种方法获取描述性统计量"><i class="fa fa-check"></i><b>3.1</b> 多种方法获取描述性统计量</a>
|
||
<ul>
|
||
<li class="chapter" data-level="3.1.1" data-path="task-03.html"><a href="task-03.html#基础方法"><i class="fa fa-check"></i><b>3.1.1</b> 基础方法</a></li>
|
||
<li class="chapter" data-level="3.1.2" data-path="task-03.html"><a href="task-03.html#拓展包方法"><i class="fa fa-check"></i><b>3.1.2</b> 拓展包方法</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="3.2" data-path="task-03.html"><a href="task-03.html#分组计算描述性统计"><i class="fa fa-check"></i><b>3.2</b> 分组计算描述性统计</a>
|
||
<ul>
|
||
<li class="chapter" data-level="3.2.1" data-path="task-03.html"><a href="task-03.html#基础方法-1"><i class="fa fa-check"></i><b>3.2.1</b> 基础方法</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="3.3" data-path="task-03.html"><a href="task-03.html#频数表和列联表"><i class="fa fa-check"></i><b>3.3</b> 频数表和列联表</a></li>
|
||
<li class="chapter" data-level="3.4" data-path="task-03.html"><a href="task-03.html#相关"><i class="fa fa-check"></i><b>3.4</b> 相关</a>
|
||
<ul>
|
||
<li class="chapter" data-level="3.4.1" data-path="task-03.html"><a href="task-03.html#相关的类型"><i class="fa fa-check"></i><b>3.4.1</b> 相关的类型</a></li>
|
||
<li class="chapter" data-level="3.4.2" data-path="task-03.html"><a href="task-03.html#相关性的显著性检验"><i class="fa fa-check"></i><b>3.4.2</b> 相关性的显著性检验</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="3.5" data-path="task-03.html"><a href="task-03.html#方差分析"><i class="fa fa-check"></i><b>3.5</b> 方差分析</a>
|
||
<ul>
|
||
<li class="chapter" data-level="3.5.1" data-path="task-03.html"><a href="task-03.html#单因素方差分析"><i class="fa fa-check"></i><b>3.5.1</b> 单因素方差分析</a></li>
|
||
<li class="chapter" data-level="3.5.2" data-path="task-03.html"><a href="task-03.html#多因素方差分析"><i class="fa fa-check"></i><b>3.5.2</b> 多因素方差分析</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="" data-path="task-03.html"><a href="task-03.html#本章作者-3"><i class="fa fa-check"></i>本章作者</a></li>
|
||
<li class="chapter" data-level="" data-path="task-03.html"><a href="task-03.html#关于datawhale-3"><i class="fa fa-check"></i>关于Datawhale</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="4" data-path="task-04.html"><a href="task-04.html"><i class="fa fa-check"></i><b>4</b> 数据可视化</a>
|
||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-04.html"><a href="task-04.html#ggplot2包介绍"><i class="fa fa-check"></i>ggplot2包介绍</a></li>
|
||
<li class="chapter" data-level="4.1" data-path="task-04.html"><a href="task-04.html#环境配置-2"><i class="fa fa-check"></i><b>4.1</b> 环境配置</a>
|
||
<ul>
|
||
<li class="chapter" data-level="" data-path="task-04.html"><a href="task-04.html#案例数据-1"><i class="fa fa-check"></i>案例数据</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="4.2" data-path="task-04.html"><a href="task-04.html#散点图"><i class="fa fa-check"></i><b>4.2</b> 散点图</a></li>
|
||
<li class="chapter" data-level="4.3" data-path="task-04.html"><a href="task-04.html#直方图"><i class="fa fa-check"></i><b>4.3</b> 直方图</a></li>
|
||
<li class="chapter" data-level="4.4" data-path="task-04.html"><a href="task-04.html#柱状图"><i class="fa fa-check"></i><b>4.4</b> 柱状图</a></li>
|
||
<li class="chapter" data-level="4.5" data-path="task-04.html"><a href="task-04.html#饼状图"><i class="fa fa-check"></i><b>4.5</b> 饼状图</a></li>
|
||
<li class="chapter" data-level="4.6" data-path="task-04.html"><a href="task-04.html#折线图"><i class="fa fa-check"></i><b>4.6</b> 折线图</a></li>
|
||
<li class="chapter" data-level="4.7" data-path="task-04.html"><a href="task-04.html#ggplot2扩展包主题"><i class="fa fa-check"></i><b>4.7</b> ggplot2扩展包主题</a></li>
|
||
<li class="chapter" data-level="" data-path="task-04.html"><a href="task-04.html#本章作者-4"><i class="fa fa-check"></i>本章作者</a></li>
|
||
<li class="chapter" data-level="" data-path="task-04.html"><a href="task-04.html#关于datawhale-4"><i class="fa fa-check"></i>关于Datawhale</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="5" data-path="task-05.html"><a href="task-05.html"><i class="fa fa-check"></i><b>5</b> 模型</a>
|
||
<ul>
|
||
<li class="chapter" data-level="5.1" data-path="task-05.html"><a href="task-05.html#前言"><i class="fa fa-check"></i><b>5.1</b> 前言</a>
|
||
<ul>
|
||
<li class="chapter" data-level="5.1.1" data-path="task-05.html"><a href="task-05.html#linear-regression"><i class="fa fa-check"></i><b>5.1.1</b> Linear Regression</a></li>
|
||
<li class="chapter" data-level="5.1.2" data-path="task-05.html"><a href="task-05.html#stepwise-regression"><i class="fa fa-check"></i><b>5.1.2</b> Stepwise Regression</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="5.2" data-path="task-05.html"><a href="task-05.html#分类模型"><i class="fa fa-check"></i><b>5.2</b> 分类模型</a>
|
||
<ul>
|
||
<li class="chapter" data-level="5.2.1" data-path="task-05.html"><a href="task-05.html#logistics-regression"><i class="fa fa-check"></i><b>5.2.1</b> Logistics Regression</a></li>
|
||
<li class="chapter" data-level="5.2.2" data-path="task-05.html"><a href="task-05.html#knn"><i class="fa fa-check"></i><b>5.2.2</b> KNN</a></li>
|
||
<li class="chapter" data-level="5.2.3" data-path="task-05.html"><a href="task-05.html#decision-tree"><i class="fa fa-check"></i><b>5.2.3</b> Decision Tree</a></li>
|
||
<li class="chapter" data-level="5.2.4" data-path="task-05.html"><a href="task-05.html#random-forest"><i class="fa fa-check"></i><b>5.2.4</b> Random Forest</a></li>
|
||
</ul></li>
|
||
<li class="chapter" data-level="" data-path="task-05.html"><a href="task-05.html#思考与练习-1"><i class="fa fa-check"></i>思考与练习</a></li>
|
||
<li class="chapter" data-level="" data-path="task-05.html"><a href="task-05.html#本章作者-5"><i class="fa fa-check"></i>本章作者</a></li>
|
||
<li class="chapter" data-level="" data-path="task-05.html"><a href="task-05.html#关于datawhale-5"><i class="fa fa-check"></i>关于Datawhale</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
|
||
<div class="book-body">
|
||
<div class="body-inner">
|
||
<div class="book-header" role="navigation">
|
||
<h1>
|
||
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">R语言数据分析组队学习</a>
|
||
</h1>
|
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</div>
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|
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<div class="page-wrapper" tabindex="-1" role="main">
|
||
<div class="page-inner">
|
||
|
||
<section class="normal" id="section-">
|
||
<div id="task-03" class="section level1" number="3">
|
||
<h1><span class="header-section-number">第 3 章</span> 基本统计分析</h1>
|
||
<p><img src="image/task03_structure.png" style="width:100.0%" /></p>
|
||
<div id="准备工作-1" class="section level2 unnumbered">
|
||
<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="cb280"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb280-1"><a href="task-03.html#cb280-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(pastecs)</span>
|
||
<span id="cb280-2"><a href="task-03.html#cb280-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(psych)</span>
|
||
<span id="cb280-3"><a href="task-03.html#cb280-3" aria-hidden="true" tabindex="-1"></a><span class="fu">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="cb281"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb281-1"><a href="task-03.html#cb281-1" aria-hidden="true" tabindex="-1"></a>flu <span class="ot"><-</span> <span class="fu">read.table</span>(<span class="st">"./datasets/h1n1_flu.csv"</span>, <span class="at">header =</span> <span class="cn">TRUE</span>, <span class="at">sep =</span> <span class="st">","</span>)</span>
|
||
<span id="cb281-2"><a href="task-03.html#cb281-2" aria-hidden="true" tabindex="-1"></a>housing <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"./datasets/BostonHousing.csv"</span>, <span class="at">header =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
|
||
</div>
|
||
<div id="多种方法获取描述性统计量" class="section level2" number="3.1">
|
||
<h2><span class="header-section-number">3.1</span> 多种方法获取描述性统计量</h2>
|
||
<div id="基础方法" class="section level3" number="3.1.1">
|
||
<h3><span class="header-section-number">3.1.1</span> 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="cb282"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb282-1"><a href="task-03.html#cb282-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(flu[<span class="fu">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="cb284"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb284-1"><a href="task-03.html#cb284-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(flu[<span class="fu">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="cb286"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb286-1"><a href="task-03.html#cb286-1" aria-hidden="true" tabindex="-1"></a>mystats <span class="ot"><-</span> <span class="cf">function</span>(x, <span class="at">na.omit =</span> <span class="cn">FALSE</span>) {</span>
|
||
<span id="cb286-2"><a href="task-03.html#cb286-2" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (na.omit) {</span>
|
||
<span id="cb286-3"><a href="task-03.html#cb286-3" aria-hidden="true" tabindex="-1"></a> x <span class="ot"><-</span> x[<span class="sc">!</span><span class="fu">is.na</span>(x)]</span>
|
||
<span id="cb286-4"><a href="task-03.html#cb286-4" aria-hidden="true" tabindex="-1"></a> }</span>
|
||
<span id="cb286-5"><a href="task-03.html#cb286-5" aria-hidden="true" tabindex="-1"></a> m <span class="ot"><-</span> <span class="fu">mean</span>(x)</span>
|
||
<span id="cb286-6"><a href="task-03.html#cb286-6" aria-hidden="true" tabindex="-1"></a> n <span class="ot"><-</span> <span class="fu">length</span>(x)</span>
|
||
<span id="cb286-7"><a href="task-03.html#cb286-7" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> <span class="fu">sd</span>(x)</span>
|
||
<span id="cb286-8"><a href="task-03.html#cb286-8" aria-hidden="true" tabindex="-1"></a> skew <span class="ot"><-</span> <span class="fu">sum</span>((x <span class="sc">-</span> m)<span class="sc">^</span><span class="dv">3</span> <span class="sc">/</span> s<span class="sc">^</span><span class="dv">3</span>) <span class="sc">/</span> n</span>
|
||
<span id="cb286-9"><a href="task-03.html#cb286-9" aria-hidden="true" tabindex="-1"></a> kurt <span class="ot"><-</span> <span class="fu">sum</span>((x <span class="sc">-</span> m)<span class="sc">^</span><span class="dv">4</span> <span class="sc">/</span> s<span class="sc">^</span><span class="dv">4</span>) <span class="sc">/</span> n <span class="sc">-</span> <span class="dv">3</span></span>
|
||
<span id="cb286-10"><a href="task-03.html#cb286-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(<span class="fu">c</span>(<span class="at">n =</span> n, <span class="at">mean =</span> m, <span class="at">stdev =</span> s, <span class="at">skew =</span> skew, <span class="at">kurtosis =</span> kurt))</span>
|
||
<span id="cb286-11"><a href="task-03.html#cb286-11" aria-hidden="true" tabindex="-1"></a>}</span>
|
||
<span id="cb286-12"><a href="task-03.html#cb286-12" aria-hidden="true" tabindex="-1"></a></span>
|
||
<span id="cb286-13"><a href="task-03.html#cb286-13" aria-hidden="true" tabindex="-1"></a><span class="fu">sapply</span>(flu[<span class="fu">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" number="3.1.2">
|
||
<h3><span class="header-section-number">3.1.2</span> 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="cb288"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb288-1"><a href="task-03.html#cb288-1" aria-hidden="true" tabindex="-1"></a><span class="fu">stat.desc</span>(flu[<span class="fu">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="cb290"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb290-1"><a href="task-03.html#cb290-1" aria-hidden="true" tabindex="-1"></a><span class="fu">describe</span>(flu[<span class="fu">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" number="3.2">
|
||
<h2><span class="header-section-number">3.2</span> 分组计算描述性统计</h2>
|
||
<div id="基础方法-1" class="section level3" number="3.2.1">
|
||
<h3><span class="header-section-number">3.2.1</span> 基础方法</h3>
|
||
<div id="使用aggregate分组获取描述性统计" class="section level4 unnumbered">
|
||
<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="cb292"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb292-1"><a href="task-03.html#cb292-1" aria-hidden="true" tabindex="-1"></a><span class="fu">aggregate</span>(flu[<span class="fu">c</span>(<span class="st">"income_poverty"</span>)], <span class="at">by =</span> <span class="fu">list</span>(<span class="at">sex =</span> flu<span class="sc">$</span>sex), length)</span></code></pre></div>
|
||
<pre><code>## sex income_poverty
|
||
## 1 Female 15858
|
||
## 2 Male 10849</code></pre>
|
||
<div class="sourceCode" id="cb294"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb294-1"><a href="task-03.html#cb294-1" aria-hidden="true" tabindex="-1"></a><span class="fu">aggregate</span>(housing<span class="sc">$</span>medv, <span class="at">by =</span> <span class="fu">list</span>(<span class="at">medv =</span> housing<span class="sc">$</span>chas), <span class="at">FUN =</span> mean)</span></code></pre></div>
|
||
<pre><code>## medv x
|
||
## 1 0 22.09384
|
||
## 2 1 28.44000</code></pre>
|
||
</div>
|
||
<div id="使用-by-分组计算描述性统计量" class="section level4 unnumbered">
|
||
<h4>使用 by() 分组计算描述性统计量</h4>
|
||
<div class="sourceCode" id="cb296"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb296-1"><a href="task-03.html#cb296-1" aria-hidden="true" tabindex="-1"></a><span class="fu">by</span>(flu[<span class="fu">c</span>(<span class="st">"income_poverty"</span>, <span class="st">"sex"</span>)], flu<span class="sc">$</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" number="3.3">
|
||
<h2><span class="header-section-number">3.3</span> 频数表和列联表</h2>
|
||
<div class="sourceCode" id="cb298"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb298-1"><a href="task-03.html#cb298-1" aria-hidden="true" tabindex="-1"></a><span class="fu">table</span>(flu<span class="sc">$</span>sex)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Female Male
|
||
## 15858 10849</code></pre>
|
||
</div>
|
||
<div id="相关" class="section level2" number="3.4">
|
||
<h2><span class="header-section-number">3.4</span> 相关</h2>
|
||
<div id="相关的类型" class="section level3" number="3.4.1">
|
||
<h3><span class="header-section-number">3.4.1</span> 相关的类型</h3>
|
||
<div id="pearsonspearman和kendall相关" class="section level4 unnumbered">
|
||
<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="cb300"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb300-1"><a href="task-03.html#cb300-1" aria-hidden="true" tabindex="-1"></a><span class="fu">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="cb302"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb302-1"><a href="task-03.html#cb302-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cor</span>(housing, <span class="at">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="cb304"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb304-1"><a href="task-03.html#cb304-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot"><-</span> housing</span>
|
||
<span id="cb304-2"><a href="task-03.html#cb304-2" aria-hidden="true" tabindex="-1"></a>y <span class="ot"><-</span> housing[<span class="fu">c</span>(<span class="st">"medv"</span>)]</span>
|
||
<span id="cb304-3"><a href="task-03.html#cb304-3" aria-hidden="true" tabindex="-1"></a><span class="fu">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 unnumbered">
|
||
<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" number="3.4.2">
|
||
<h3><span class="header-section-number">3.4.2</span> 相关性的显著性检验</h3>
|
||
<div class="sourceCode" id="cb306"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb306-1"><a href="task-03.html#cb306-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cor.test</span>(housing[, <span class="fu">c</span>(<span class="st">"crim"</span>)], housing[, <span class="fu">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" number="3.5">
|
||
<h2><span class="header-section-number">3.5</span> 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" number="3.5.1">
|
||
<h3><span class="header-section-number">3.5.1</span> 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="cb308"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb308-1"><a href="task-03.html#cb308-1" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">aov</span>(housing<span class="sc">$</span>medv <span class="sc">~</span> housing<span class="sc">$</span>chas)</span>
|
||
<span id="cb308-2"><a href="task-03.html#cb308-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(fit)</span></code></pre></div>
|
||
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
|
||
## housing$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" number="3.5.2">
|
||
<h3><span class="header-section-number">3.5.2</span> 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="cb310"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb310-1"><a href="task-03.html#cb310-1" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">aov</span>(housing<span class="sc">$</span>medv <span class="sc">~</span> housing<span class="sc">$</span>crim <span class="sc">*</span> housing<span class="sc">$</span>b)</span>
|
||
<span id="cb310-2"><a href="task-03.html#cb310-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(fit)</span></code></pre></div>
|
||
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
|
||
## housing$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 id="本章作者-3" class="section level2 unnumbered">
|
||
<h2>本章作者</h2>
|
||
<p><strong>杨佳达</strong></p>
|
||
<blockquote>
|
||
<p>数据挖掘师，Datawhale成员，目前在国内某第三方数据服务公司做数据分析挖掘及数据产品<br />
|
||
<a href="https://github.com/yangjiada" class="uri">https://github.com/yangjiada</a></p>
|
||
</blockquote>
|
||
</div>
|
||
<div id="关于datawhale-3" class="section level2 unnumbered">
|
||
<h2>关于Datawhale</h2>
|
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<p>Datawhale 是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注 Datawhale：</p>
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