将相关表显示为降序列表 [英] Display Correlation Tables as Descending List

问题描述

在具有很多变量的时间序列上运行cor（）时，我得到一个表格表，其中每个变量都有行和列，显示了它们之间的相关性。

When running cor() on a times series with a lot of variables, I get a table back that has a row and column for each variable, showing the correlation between them.

我如何查看此表为从最相关到​​最不相关的列表（消除所有NA结果和映射回自身的结果（即A与A的相关性））。我还想将反向（负）结果视为绝对值，但仍将它们显示为负。

How can I view this table as a list from most correlated to least correlated (eliminating all NA results and results that map back to themselves (i.e. the correlation of A to A)). I would also like to count inverse (negative) results as absolute values, but still show them as negative.

因此，所需的输出将类似于：

So the desired output would be something like:

A,B,0.98
A,C,0.9
C,R,-0.8
T,Z,0.5

推荐答案

这是我能想到的许多方法之一去做这个。我使用了reshape软件包，因为 melt（）语法很容易记住，但是 melt（）命令可以很容易地用基本的R命令完成：

Here's one of many ways I could think to do this. I used the reshape package because the melt() syntax was easy for me to remember, but the melt() command could pretty easily be done with base R commands:

require(reshape)
## set up dummy data
a <- rnorm(100)
b <- a + (rnorm(100, 0, 2))
c <- a + b + (rnorm(100)/10)
df <- data.frame(a, b, c)
c <- cor(df)
## c is the correlations matrix

## keep only the lower triangle by 
## filling upper with NA
c[upper.tri(c, diag=TRUE)] <- NA

m <- melt(c)

## sort by descending absolute correlation
m <- m[order(- abs(m$value)), ]

## omit the NA values
dfOut <- na.omit(m)

## if you really want a list and not a data.frame
listOut <- split(dfOut, 1:nrow(dfOut))

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