如何根據相關性生成分組變量？

Question

 library(magrittr)
 library(dplyr)
 V1 <- c("A","A","A","A","A","A","B","B","B","B", "B","B","C","C","C","C","C","C","D","D","D","D","D","D","E","E","E","E","E","E")
 V2 <- c("A","B","C","D","E","F","A","B","C","D","E","F","A","B","C","D","E","F","A","B","C","D","E","F","A","B","C","D","E","F")
 cor <- c(1,0.8,NA,NA,NA,NA,0.8,1,NA,NA,NA,NA,NA,NA,1,0.8,NA,NA,NA,NA,0.8,1,NA,NA,NA,NA,NA,NA,1,0.9)


 df <- data.frame(V1,V2,cor)

 # exclude rows where cor=NA
 df <- df[complete.cases(df)==TRUE,]

這是完整的數據幀，cor = NA表示小於0.8的相關性

 df

   V1 V2 cor
1   A  A 1.0
2   A  B 0.8
7   B  A 0.8
8   B  B 1.0
15  C  C 1.0
16  C  D 0.8
21  D  C 0.8
22  D  D 1.0
29  E  E 1.0
30  E  F 0.9

在上面的df中，F不在V1中，這意味着F不重要

所以在這里我刪除了V2 = F的行（通常，V2等於不在V1中的值）

 V1.LIST <- unique(df$V1)
 df.gp <- df[which(df$V2 %in% V1.LIST),]

 df.gp

   V1 V2 cor
1   A  A 1.0
2   A  B 0.8
7   B  A 0.8
8   B  B 1.0
15  C  C 1.0
16  C  D 0.8
21  D  C 0.8
22  D  D 1.0
29  E  E 1.0

所以現在，df.gp是我需要處理的數據集

我在V2中刪除了未使用的級別（在示例中為F）

 df.gp$V2 <- droplevels(df.gp$V2)

我不想排除自相關變量，以防某些V1與其他變量不相關，並且我想將每個變量放在一個單獨的組中

通過查看cor，可以將A和B關聯起來，將C和D關聯起來，而E本身屬於一個組。

因此，此處的示例應分為三組。

Answer 1

我認為，將數據直接處理為data.frame可能會使事情復雜化。 我自由地將其轉換回矩陣。

library(reshape2)
cormat <- as.matrix(dcast(data = df,formula = V1~V2))[,-1]
row.names(cormat) <- colnames(cormat)[-length(colnames(cormat))]
cormat

獲得相關矩陣后，可以輕松查看與其他變量共享哪些索引或非NA值。

a <- apply(cormat, 1, function(x) which(!is.na(x)))
a <- data.frame(t(a))
a$var <- row.names(a)
row.names(a) <- NULL
a

  X1 X2 var
1  1  2   A
2  1  2   B
3  3  4   C
4  3  4   D
5  5  6   E

現在， X1或X2確定您的唯一分組。

cyrusjan編輯：

假設我們已經選擇了cor >= a的行，則上述腳本是一種可能的解決方案，其中a是上述問題中的閾值為0.8。

由alexis_laz貢獻：

通過使用cutree和hclust ，我們可以將腳本中的閾值（即h = 0.8）設置為打擊。

 cor.gp <- data.frame(cor.gp =
      cutree(hclust(1 - as.dist(xtabs(cor ~ V1 + V2, df.gp))), h = 0.8))