R Corrplot软件包简介,绘制相关热图

翻译最新推荐文章于 2026-04-30 09:07:22 发布 3.6w 阅读 AI写同款

收录于

R语言

作图结果解释

gavin_cdc

码龄9年墨衍会员 · AI 创作全网分发

93粉丝33篇原创

关注

AI摘要

本文详细介绍R语言中Corrplot软件包的功能和使用方法，包括相关矩阵的可视化、矩阵重新排序、处理非相关矩阵及缺失值等。此外，文章还展示了如何结合显著性检验和置信区间来增强相关图的表现力。

Corrplot软件包简介

介绍

所述corrplot包是相关矩阵，置信区间的图形显示。它还包含一些进行矩阵重新排序的算法。另外，corrplot擅长细节，包括选择颜色，文本标签，颜色标签，布局等。

可视化方法

有七个可视化方法（参数method中）corrplot包，命名"circle"，"square"，"ellipse"，"number"，"shade"，"color"，"pie"。

正相关以蓝色显示，负相关以红色显示。颜色强度和圆圈的大小与相关系数成正比。

library(corrplot)

## corrplot 0.84 loaded

M <- cor(mtcars)
corrplot(M, method = "circle")

corrplot(M, method = "square")

corrplot(M, method = "ellipse")

corrplot(M, method = "number") # Display the correlation coefficient

corrplot(M, method = "shade")

corrplot(M, method = "color")

corrplot(M, method = "pie")

布局

共有三种布局类型（参数type）：

"full"（默认）：显示完整的相关矩阵
"upper"：显示相关矩阵的上三角
"lower"：显示相关矩阵的下三角

corrplot(M, type = "upper")
corrplot(M, type = "upper")

corrplot.mixed() 是混合可视化样式的包装函数。

corrplot.mixed(M)

corrplot.mixed(M, lower.col = "black", number.cex = .7)

corrplot.mixed(M, lower = "ellipse", upper = "circle")

corrplot.mixed(M, lower = "square", upper = "circle", tl.col = "black")

重新排序相关矩阵

相关矩阵可以根据相关系数重新排序。这对于确定矩阵中隐藏的结构和图案很重要。有在corrplot（参数四种方法order）的名字命名 "AOE"，"FPC"，"hclust"，"alphabet"。在序列化包中可以找到更多算法。

您还可以通过function手动“重新排序”矩阵corrMatOrder()。

"AOE"一个我ai

a i = { 棕褐色（e i 2 / e i 1），如果 ë 我1 > 0 ;棕褐色（e i 2 / e i 1）+ π ，除此以外。ai={tan⁡(ei2/ei1),if ei1>0;tan⁡(ei2/ei1)+π,otherwise.

Ë 1e1Ë 2e2
"FPC" 对于第一个主成分订单。
"hclust"层次聚类顺序，以及"hclust.method"要使用的聚集方法。"hclust.method"应该是一个 "ward"，"single"，"complete"，"average"，"mcquitty"，"median"或 "centroid"。
"alphabet" 按字母顺序排列。

corrplot(M, order = "AOE")

corrplot(M, order = "hclust")

corrplot(M, order = "FPC")

corrplot(M, order = "alphabet")

如果使用"hclust"，则corrplot()可以基于层次聚类的结果在相关矩阵图的周围绘制矩形。

corrplot(M, order = "hclust", addrect = 2)

corrplot(M, order = "hclust", addrect = 3)

# Change background color to lightblue
corrplot(M, type = "upper", order = "hclust",
         col = c("black", "white"), bg = "lightblue")

使用不同的色谱

如上节所示，可以自定义相关图的颜色。该功能colorRampPalette()非常便于生成色谱。

col1 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "white",
                           "cyan", "#007FFF", "blue", "#00007F"))
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582",
                           "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE",
                           "#4393C3", "#2166AC", "#053061"))
col3 <- colorRampPalette(c("red", "white", "blue")) 
col4 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "#7FFF7F",
                           "cyan", "#007FFF", "blue", "#00007F"))
whiteblack <- c("white", "black")

## using these color spectra
corrplot(M, order = "hclust", addrect = 2, col = col1(100))

corrplot(M, order = "hclust", addrect = 2, col = col2(50))

corrplot(M, order = "hclust", addrect = 2, col = col3(20))

corrplot(M, order = "hclust", addrect = 2, col = col4(10))

corrplot(M, order = "hclust", addrect = 2, col = whiteblack, bg = "gold2")

您还可以使用标准调色板（包grDevices）

corrplot(M, order = "hclust", addrect = 2, col = heat.colors(100))

corrplot(M, order = "hclust", addrect = 2, col = terrain.colors(100))

corrplot(M, order = "hclust", addrect = 2, col = cm.colors(100))

corrplot(M, order = "hclust", addrect = 2, col = gray.colors(100))

其他选择是使用RcolorBrewer包。

library(RColorBrewer)

corrplot(M, type = "upper", order = "hclust",
         col = brewer.pal(n = 8, name = "RdBu"))

corrplot(M, type = "upper", order = "hclust",
         col = brewer.pal(n = 8, name = "RdYlBu"))

corrplot(M, type = "upper", order = "hclust",
         col = brewer.pal(n = 8, name = "PuOr"))

更改文本标签和图例的颜色和旋转

参数cl.*用于颜色图例，tl.*如果用于文本图例。对于文本标签，tl.col（文本标签颜色）和tl.srt（文本标签字符串旋转）用于更改文本颜色和旋转。

这里有些例子。

## remove color legend and text legend 
corrplot(M, order = "AOE", cl.pos = "n", tl.pos = "n")

## bottom  color legend, diagonal text legend, rotate text label
corrplot(M, order = "AOE", cl.pos = "b", tl.pos = "d", tl.srt = 60)

## a wider color legend with numbers right aligned
corrplot(M, order = "AOE", cl.ratio = 0.2, cl.align = "r")

## text labels rotated 45 degrees
corrplot(M, type = "lower", order = "hclust", tl.col = "black", tl.srt = 45)

处理非相关矩阵

corrplot(abs(M),order = "AOE", col = col3(200), cl.lim = c(0, 1))

## visualize a  matrix in [-100, 100]
ran <- round(matrix(runif(225, -100,100), 15))
corrplot(ran, is.corr = FALSE, method = "square")

## a beautiful color legend 
corrplot(ran, is.corr = FALSE, method = "ellipse", cl.lim = c(-100, 100))

如果矩阵是矩形，则可以使用win.asp参数调整纵横比，以使矩阵呈现为正方形。

ran <- matrix(rnorm(70), ncol = 7)
corrplot(ran, is.corr = FALSE, win.asp = .7, method = "circle")

处理缺失（NA）值

默认情况下，corrplot将NA值呈现为"?"字符。使用na.label 参数，可以使用不同的值（最多支持两个字符）。

M2 <- M
diag(M2) = NA
corrplot(M2)

corrplot(M2, na.label = "o")

corrplot(M2, na.label = "NA")

在标签中使用“ plotmath”表达式

从version开始0.78，可以在变量名称中使用 plotmath表达式。要激活plotmath渲染，前缀的人物之一的标签":"，"="或"$"。

M2 <- M[1:5,1:5]
colnames(M2) <- c("alpha", "beta", ":alpha+beta", ":a[0]", "=a[beta]")
rownames(M2) <- c("alpha", "beta", NA, "$a[0]", "$ a[beta]")
corrplot(M2)

将相关图与显着性检验相结合

res1 <- cor.mtest(mtcars, conf.level = .95)
res2 <- cor.mtest(mtcars, conf.level = .99)

## specialized the insignificant value according to the significant level
corrplot(M, p.mat = res1$p, sig.level = .2)

corrplot(M, p.mat = res1$p, sig.level = .05)

corrplot(M, p.mat = res1$p, sig.level = .01)

## leave blank on no significant coefficient
corrplot(M, p.mat = res1$p, insig = "blank")

## add p-values on no significant coefficient
corrplot(M, p.mat = res1$p, insig = "p-value")

## add all p-values
corrplot(M, p.mat = res1$p, insig = "p-value", sig.level = -1)

## add cross on no significant coefficient 
corrplot(M, p.mat = res1$p, order = "hclust", insig = "pch", addrect = 3)

可视化置信区间

corrplot(M, low = res1$lowCI, upp = res1$uppCI, order = "hclust",
         rect.col = "navy", plotC = "rect", cl.pos = "n")

corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,
         order = "hclust", pch.col = "red", sig.level = 0.01,
         addrect = 3, rect.col = "navy", plotC = "rect", cl.pos = "n")

res1 <- cor.mtest(mtcars, conf.level = .95)

corrplot(M, p.mat = res1$p, insig = "label_sig",
         sig.level = c(.001, .01, .05), pch.cex = .9, pch.col = "white")

corrplot(M, p.mat = res1$p, method = "color",
         insig = "label_sig", pch.col = "white")

corrplot(M, p.mat = res1$p, method = "color", type = "upper",
         sig.level = c(.001, .01, .05), pch.cex = .9,
         insig = "label_sig", pch.col = "white", order = "AOE")

corrplot(M, p.mat = res1$p, insig = "label_sig", pch.col = "white",
         pch = "p<.05", pch.cex = .5, order = "AOE")

自定义相关图

# matrix of the p-value of the correlation
p.mat <- cor.mtest(mtcars)$p
head(p.mat[, 1:5])

##              [,1]         [,2]         [,3]         [,4]         [,5]
## [1,] 0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07 1.776240e-05
## [2,] 6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09 8.244636e-06
## [3,] 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08 5.282022e-06
## [4,] 1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00 9.988772e-03
## [5,] 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 0.000000e+00
## [6,] 1.293959e-10 1.217567e-07 1.222320e-11 4.145827e-05 4.784260e-06

# Specialized the insignificant value according to the significant level
corrplot(M, type = "upper", order = "hclust", 
         p.mat = p.mat, sig.level = 0.01)

# Leave blank on no significant coefficient
corrplot(M, type = "upper", order = "hclust", 
         p.mat = p.mat, sig.level = 0.01, insig = "blank")

在上图中，p值> 0.01的相关被认为是无关紧要的。在这种情况下，相关系数值留为空白或添加叉号。

col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(M, method = "color", col = col(200),
         type = "upper", order = "hclust", number.cex = .7,
         addCoef.col = "black", # Add coefficient of correlation
         tl.col = "black", tl.srt = 90, # Text label color and rotation
         # Combine with significance
         p.mat = p.mat, sig.level = 0.01, insig = "blank", 
         # hide correlation coefficient on the principal diagonal
         diag = FALSE)

注意：其中一些情节摘自该博客。

探索大型功能矩阵

# generating large feature matrix (cols=features, rows=samples)
num_features <- 60 # how many features
num_samples <- 300 # how many samples
DATASET <- matrix(runif(num_features * num_samples),
               nrow = num_samples, ncol = num_features)

# setting some dummy names for the features e.g. f23
colnames(DATASET) <- paste0("f", 1:ncol(DATASET))

# let's make 30% of all features to be correlated with feature "f1"
num_feat_corr <- num_features * .3
idx_correlated_features <- as.integer(seq(from = 1,
                                          to = num_features,
                                          length.out = num_feat_corr))[-1]
for (i in idx_correlated_features) {
  DATASET[,i] <- DATASET[,1] + runif(num_samples) # adding some noise
}

corrplot(cor(DATASET), diag = FALSE, order = "FPC",
         tl.pos = "td", tl.cex = 0.5, method = "color", type = "upper")