文章目录
相关二维列联表相关协方差Pearson相关Spearman相关相关性检验相关可视化相关
二维列联表相关
data <- xtabs(~Treatment+Improved, data = Arthritis) ## 生成二维列联表assocstats(data)
协方差
states <- state.x77[,1:6]cov(states)
Pearson相关
cor(states)x <- states[,c("Population","Income","Illiteracy","HS Grad")]y <- states[,c("Life Exp","Murder")]cor(x,y)
Spearman相关
cor(states,method = "spearman")
相关性检验
cor.test(states[,1],states[,2],alternative = "two.side",method = "spearman") ## 只能计算单个变量相关library(psych)corr.test(states,use = "complete") ## use 表示对缺失值的成行删除corr.test(states,use = "pairwise") ## use 表示对缺失值的成对删除pcor(c(1,5,2,3,6),cov(states))pcor.test(pcor(c(1,5,2,3,6),cov(states)) ,c(2,3,6),50) ## 偏相关检验,c(2,3,6)代表要控制的变量,50代表样本量
相关可视化
library(corrplot)res <- cor(states)cols <- colorRampPalette(c("red", "white", "blue"))corrplot.mixed(res,lower.col = cols(100),upper.col = cols(100),tl.pos="lt",tl.col="black")