R平台相干系数教程

本教程先容相干系数的常识，包罗三方面的内容：<br /> 首先，R平台相干系数计较<br /> 其次，R平台相关矩阵可视化<br /> 最后，R平台相干系数的范例和如何影响后续阐明事情</p> <p>### 数据导入<br /> 选自[**Kaggle**](https://www.kaggle.com/ "Kaggle")的影戏数据集movies。<br /> R代码<br /> “`<br /> ## 数据导入<br /> data.url <- "http://s3.amazonaws.com/dcwoods2717/movies.csv"<br /> movies.data <- read.csv(<br /> url(data.url)<br /> )<br /> “`</p> <p>### 数据领略<br /> 一旦数据乐成导入后，利用***str()函数***核查数据布局。<br /> R代码<br /> “`<br /> ### 1 数据布局核查<br /> str(movies.data)<br /> names(movies.data)<br /> “`<br /> <br /> 结论：<br /> 影戏数据集包括2961个调查（样本）和11个变量（属性）</p> <p>### 特征工程<br /> 特征衍生，利润=收入-本钱。<br /> R代码<br /> “`<br /> movies.data$profit <- movies.data$gross – movies.data$budget<br /> summary(movies.data$profit)<br /> “`<br /> 功效：<br /> </p> <p>### 相关性<br /> 视察影戏利润与影戏评分的干系。(**散点图+回归线**)<br /> R代码<br /> “`<br /> plot(movies.data$rating, movies.data$profit)<br /> abline(lm(movies.data$profit ~ movies.data$rating), col = ‘red’, lwd = 2)<br /> “`<br /> 功效<br /> </p> <p>凡是环境下，影戏评级越高，利润越大，利润与影戏评级存在**正相关性**。<br /> 可是，也存在一些评级较低，利润蛮高的影戏。</p> <p>**【温馨提示】：相关性并不料味着因果性！**</p> <p>### 相干系数计较<br /> 影戏评级与利润的相关水平毕竟怎么样，可以用相干系数举办怀抱。<br /> R代码<br /> “`<br /> #计较Pearson correlation<br /> cor(movies.data$rating, movies.data$profit)<br /> # 计较多变量的两两之间变量相干系数矩阵<br /> cor(movies.data[,5:8])<br /> “`<br /> 功效<br /> </p> <p>默认要领Pearson相关性假设您的变量是正态漫衍的，每个变量之间存在直线干系，而且数据凡是在回归线上漫衍。您可以利用Hmisc包的rcorr函数，用于计较Pearson和spearman相关性的明显性程度。<br /> 相关矩阵实际上是显示变量集之间的相干系数的表。</p> <p>### 相关矩阵可视化<br /> 影戏数据集的变量绘制相关矩阵，相关矩阵图可以利便地发明变量之间的正负性和强弱性。<br /> 相关矩阵利用GGally包的ggcorr()函数。<br /> R代码<br /> “`<br /> ## 相关矩阵图<br /> if(!require(GGally))<br /> {<br /> install.packages("GGally")<br /> require(GGally)<br /> }<br /> ggcorr(movies.data,<br /> label = TRUE,<br /> label_alpha = TRUE)<br /> “`<br /> 功效<br /> </p> <p>通过上图，可以清晰地发明，那些变量之间是强相关，那些变量是弱相关，以及相关性的正负性和相关性详细量化值。</p> <p>### 相关范例<br /> 由相关矩阵图可知，变量votes和变量reviews强相关，相干系数为0.8。<br /> R代码<br /> “`<br /> qplot(movies$votes,<br /> movies$reviews,<br /> data = movies,<br /> geom = c("point", "smooth"),<br /> method = "lm",<br /> alpha = I(1 / 5),<br /> se = FALSE)<br /> “`<br /> 功效<br /> <br /> 同理，变量profit与变量year弱相关，相干系数为-0.1。<br /> R代码<br /> “`<br /> qplot(movies.data$year,<br /> movies.data$profit,<br /> data = movies.data,<br /> geom = c("point", "smooth"),<br /> alpha = I(1 / 5))<br /> “`<br /> 功效<br /> </p> <p>### 综合<br /> 操作GGally中的ggpairs函数，可以很好地把上面先容的内容以简捷易懂的方法举办综合。<br /> R代码<br /> “`<br /> names(movies.data)<br /> ggpairs(movies.data,<br /> columns = c("budget", "rating", "profit"),<br /> upper = list(continuous = wrap("cor",<br /> size = 10)),<br /> lower = list(continuous = "smooth"))<br /> “`<br /> 功效<br /> </p> <p>但愿这些常识可以或许应用于您的阐明里，这些是R平台做数据摸索的基本常识，它是让你在数据阐明和建模之前对数据有更好地认识。</p> <p>您在阅读中，有什么发起可能想法，请参加评论。<br /> 想插手圈子，请加微信。</p> </div><!-- .entry-content --> </article><!-- #post-## -->  <div class="tagcloud"><ul class='wp-tag-cloud' role='list'><h3>关键字：</h3> </ul> </div> <div class="col-sm-6 col-box bg-gray-light-2 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