为什么我只从R中的cor()函数中得到1和-1?

为什么我只从R中的cor()函数中得到1和-1?,r,correlation,R,Correlation,我试图用cor()函数执行Pearson关联,但输出只给出1和-1,而不是系数本身。因此,当我用corrplot()绘制矩阵时,我只看到那些1和-1值。我该如何解决这个问题? 可以找到我的数据集,请参见下面的脚本: ##Must load the libraries we will need! IF you have not installed the packages, do that before you start. library("corrplot") ##Load in your

我试图用cor()函数执行Pearson关联,但输出只给出1和-1,而不是系数本身。因此,当我用corrplot()绘制矩阵时,我只看到那些1和-1值。我该如何解决这个问题? 可以找到我的数据集,请参见下面的脚本:

##Must load the libraries we will need! IF you have not installed the packages, do that before you start. 
library("corrplot")
##Load in your datasets 
D1=BPT5test
##if you don't have a Y (i.e, you want the same thing to be in both axis), leave this blank
D2=
##Run the spearman correlation. If you want to do a Pearson, change "spearman to "pearson"
##If you have 0s in your dataset, set use = "complete.obs", if you have no 0s, set use = "everything"
CorTest=cor(D1, use = "everything", method = "pearson")
##Let's get to plotting! 
##Lots of changing you can do! 
#Method can be "circle" "square" "pie" "color"
#ColorRampPalette can be changed, "blue" being the negative, "White" being '0', and "red" being the positive
#Change the title to whatever you want it to be 
#tl.col is the color of your labels, this can be set to anything.. default is red
CorGraph=corrplot(CorTest, method = "circle", col = colorRampPalette(c("blue","white","red"))(200), title = "Pearson's Correlation of High-Fat Sugar at 8 weeks", tl.cex = .5, tl.col = "Black",diag = TRUE, cl.ratio = 0.2)

这是因为按列只有两个观察值

test <- data.frame(a=c(1,2),b=c(2,3),c=c(4,-2))
cor(test, use = "everything", method = "pearson")
   a  b  c
a  1  1 -1
b  1  1 -1
c -1 -1  1

test您的数据集每个变量只包含2个观察值。仅由两个观测值组成的任何两个变量之间的相关性始终为-1或1。要亲自查看,请尝试运行
replicate(1e2,cor(rnorm(2),rnorm(2))
,它计算由两个观察值组成的两个变量之间的100个相关性。结果总是-1或1。

谢谢你,很明显,我缺乏统计学知识,这让我受益匪浅。
test <- data.frame(a=c(1,2,3),b=c(2,3,5),c=c(4,-2,-10))
cor(test, use = "everything", method = "pearson")
           a          b          c
a  1.0000000  0.9819805 -0.9966159
b  0.9819805  1.0000000 -0.9941916
c -0.9966159 -0.9941916  1.0000000