如何在R中绘制剖面图（主成分分析）？

我现在在做主成分分析。对于解释，我想创建一个剖面（模式）图，以可视化每个主成分和原始变量之间的相关性。是否有人熟悉在R中创建此文件的包或代码？我在R中使用prcomp（）函数

见示例：

这是与我的数据库类似的数据：

db <- structure(list(T025 = c(20, 60, 20, 10, 85, 5, 15, 10, 10, 25, 
15, 5, 15, 30, 15, 15, 10, 25, 45, 25, 55, 20, 65, 20, 10, 10, 
15, 15, 30, 35, 10, 50, 20, 15, 30, 15, 20, 35, 30, 20, 10, 20, 
30, 15, 40, 15, 10, 10, 20, 25, -5, 10, 40, 0, 15, 5, 15, 30, 
15, 80, 15, 35, 10, 50, 25, 10, 15, 20, 20, 20, 25, 20, 30, 10, 
20, 50, 25, 25, 55, 30, 20, 30, 15, 10, 15, 15, 35, 20, 30, 15, 
40, 20, 25, 15, 20, 35, 15, 25, 20, 40, 0, 20, 10, 10, 15, 10, 
20, 10, 35, 35, 25, 30, 20, 25, 15, 30, 35, 25, 30, 5, 20, 30, 
15, 25, 10), T05 = c(0, 25, 0, 5, 25, 5, 0, 0, 5, 5, 5, -5, 5, 
15, 15, 5, 0, 15, 25, 15, 50, 20, 45, 5, 5, 5, 0, 10, 10, 10, 
5, 20, 15, 10, 20, 10, -5, 10, 30, -5, 0, 10, 35, 5, 40, 0, 0, 
-5, 15, 25, 0, 5, 35, -5, 5, 0, 5, 5, 10, 70, 0, 20, 5, 30, 10, 
10, 5, 5, 25, 10, 20, 5, 25, 5, 10, 35, 15, 10, 45, 15, 15, 25, 
10, 5, 10, 5, 20, 15, 15, 5, 10, 10, 20, 5, 15, 25, 5, 20, 10, 
35, -10, 5, 0, -5, 0, 5, 15, 5, 15, 35, 20, 25, 10, 15, 15, 25, 
45, 0, 25, 0, 5, 25, 0, 20, 5), T1 = c(25, 20, 25, 20, 50, 10, 
15, 20, 25, 25, 25, 25, 15, 45, 25, 25, 20, 35, 40, 35, 65, 45, 
45, 30, 25, 20, 5, 20, 30, 25, 20, 35, 25, 25, 35, 15, 15, 25, 
45, 20, 25, 35, 40, 25, 60, 15, 15, 15, 25, 45, 20, 20, 60, 15, 
20, 25, 45, 45, 25, 75, 10, 45, 15, 50, 20, 25, 20, 15, 40, 30, 
50, 20, 40, 20, 35, 50, 35, 15, 50, 30, 20, 45, 25, 25, 20, 45, 
30, 35, 30, 30, 15, 15, 30, 25, 25, 25, 15, 40, 25, 55, 20, 30, 
10, 15, 50, 15, 40, 20, 20, 55, 35, 45, 20, 50, 35, 20, 65, 10, 
35, 15, 30, 55, 25, 15, 25), T2 = c(20, 20, 15, 25, 70, 10, 15, 
45, 50, 30, 20, 25, 10, 40, 20, 40, 30, 40, 25, 30, 45, 25, 50, 
20, 20, 20, 10, 10, 45, 10, 5, 40, 20, 15, 50, 25, 15, 20, 25, 
30, 20, 30, 35, 15, 65, 20, 25, 10, 10, 60, 25, 20, 70, 5, 15, 
15, 15, 25, 15, 60, 25, 55, 5, 50, 30, 35, 5, 10, 30, 10, 55, 
25, 40, 35, 40, 45, 25, 20, 35, 40, 5, 40, 10, 25, 10, 40, 30, 
20, 25, 25, 10, 25, 30, 45, 20, 25, 10, 55, 40, 60, 5, 10, 10, 
5, 20, 0, 40, 20, 35, 80, 25, 40, 15, 55, 25, 15, 65, 5, 25, 
5, 35, 45, 10, 5, 10), T4 = c(10, 25, 35, 35, 70, 20, 15, 70, 
55, 30, 50, 35, 40, 40, 35, 45, 60, 50, 15, 25, 70, 10, 60, 40, 
30, 15, 15, 15, 50, 5, 20, 70, 5, 35, 65, 40, 20, 65, 50, 30, 
45, 55, 65, 35, 45, 35, 40, 20, 5, 65, 20, 25, 75, 10, 25, 25, 
10, 25, 20, 55, 20, 65, 5, 60, 70, 45, 15, 25, 35, 5, 70, 55, 
65, 40, 35, 55, 35, 45, 45, 45, 20, 40, 25, 50, 15, 55, 55, 40, 
30, 60, 10, 60, 40, 35, 30, 65, 5, 75, 55, 80, 15, 30, 55, 15, 
50, 25, 45, 30, 45, 90, 20, 45, 20, 40, 35, 20, 70, 20, 30, 45, 
50, 55, 45, 5, 45), T8 = c(5, 55, 55, 40, 75, 40, 5, 70, 25, 
10, 50, 55, 5, 35, 10, 30, 40, 55, 20, 20, 65, -5, 55, 50, -10, 
45, 5, 50, 65, 20, 0, 75, 15, 30, 50, 50, 30, 70, 45, 25, 35, 
40, 85, 30, 60, 50, 55, 15, 10, 75, 60, 20, 90, 0, 20, 55, -10, 
20, 10, 45, 20, 65, 0, 70, 85, 0, -5, 30, 35, 5, 80, 45, 60, 
25, 35, 55, 30, 45, 65, 45, -5, 35, 35, 40, 50, 55, 50, 70, 45, 
40, 0, 55, 45, 30, 0, 56, 0, 45, 50, 70, 15, 20, 45, -10, 45, 
55, 45, 20, 50, 85, 5, 50, 10, 20, 25, 0, 70, 0, 25, 5, 45, 35, 
40, -5, 25)), row.names = c("1", "2", "3", "4", "5", "6", 
"7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", 
"18", "19", "20", "21", "22", "23", "24", "25", "26", 
"177", "191", "200", "205", "208", "212", "231", "236", "240", 
"246", "250", "259", "263", "264", "275", "276", "282", "293", 
"303", "304", "307", "309", "315", "316", "320", "322", "324", 
"327", "333", "338", "343", "356", "365", "377", "379", "393", 
"395", "399", "405", "411", "426", "428", "439", "448", "451", 
"459", "490", "495", "498", "513", "515", "521", "524", "528", 
"532", "550", "552", "559", "566", "570", "577", "583", "587", 
"595", "624", "638", "641", "645", "647", "650", "660", "668", 
"677", "683", "688", "691", "702", "704", "710", "719", "730", 
"732", "748", "752", "758", "766", "772", "780", "782", "790", 
"810", "828", "830", "836", "853", "862", "880", "889", "896"
), class = "data.frame")

db.pca <- prcomp(db, center= TRUE, scale.=TRUE)
summary(db.pca)
str(db.pca)
ggbiplot(db.pca)
screeplot(db.pca, type="line")

---

db使用您的数据，我做到了：

comp = prcomp(db, center=T, scale.=T)

b =matrix(ncol = 3)[-1,]
for(i in 1:ncol(comp$x)){
  for(j in colnames(db)){
    b = rbind(b, c(i,j,cor.test(comp$x[,i], db[,j])$estimate))
  }
}
b= as.data.frame(b)
b$cor= as.numeric(b$cor)


ggplot(b,aes(x=V2,y=cor, group = V1, col= V1))+
  geom_line()+
  theme_classic()

我得到了这个：


它有用吗？
下面是一种使用package
FactoMineR
获取相关性的方法。该图为基准R图

library(FactoMineR)

res.pca <- PCA(iris[-5], graph = FALSE)
cos2 <- res.pca$var$cos2

old_par <- par(xpd = TRUE)
matplot(
  cos2, 
  type = "l", 
  xlab = "variable", 
  ylab = "correlation",
  main = "Component Pattern Profiles",
  xaxt = "n"
)
axis(1, at = 1:nrow(cos2), labels = rownames(cos2))
legend(
  x = "bottom",
  inset = c(0, -0.2),
  legend = colnames(cos2),
  col = 1:ncol(cos2),
  lty = 1:ncol(cos2),
  bty = "n",
  horiz = TRUE
  )
par(old_par)

库（FactoMineR）
嘿，kim，你能展示一下你的数据吗？是的，我会发布一些类似的数据！谢谢，我用你的数据更新了我的答案谢谢，效果非常好，给了我我希望看到的图表！不客气：）总是很高兴得到多个解决方案！谢谢