Xueyd's Blog

0%

corrplot绘制相关性图

发表于 更新于 分类于
本文字数： 258 阅读时长 ≈ 1 分钟

总结在R中使用corrplot绘制相关性图。

1. 加载数据

1
2
3
4
5
6
7
8
9
10
# 加载数据，数据需转换成矩阵
data(mtcars)
head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

2. 计算相关性系数

corrpolt用法：

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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
corrplot(
  corr,
  method = c("circle", "square", "ellipse", "number", "shade", "color", "pie"),
  type = c("full", "lower", "upper"),
  col = NULL,
  col.lim = NULL,
  bg = "white",
  title = "",
  is.corr = TRUE,
  add = FALSE,
  diag = TRUE,
  outline = FALSE,
  mar = c(0, 0, 0, 0),
  addgrid.col = NULL,
  addCoef.col = NULL,
  addCoefasPercent = FALSE,
  order = c("original", "AOE", "FPC", "hclust", "alphabet"),
  hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single", "average",
    "mcquitty", "median", "centroid"),
  addrect = NULL,
  rect.col = "black",
  rect.lwd = 2,
  tl.pos = NULL,
  tl.cex = 1,
  tl.col = "red",
  tl.offset = 0.4,
  tl.srt = 90,
  cl.pos = NULL,
  cl.length = NULL,
  cl.cex = 0.8,
  cl.ratio = 0.15,
  cl.align.text = "c",
  cl.offset = 0.5,
  number.cex = 1,
  number.font = 2,
  number.digits = NULL,
  addshade = c("negative", "positive", "all"),
  shade.lwd = 1,
  shade.col = "white",
  p.mat = NULL,
  sig.level = 0.05,
  insig = c("pch", "p-value", "blank", "n", "label_sig"),
  pch = 4,
  pch.col = "black",
  pch.cex = 3,
  plotCI = c("n", "square", "circle", "rect"),
  lowCI.mat = NULL,
  uppCI.mat = NULL,
  na.label = "?",
  na.label.col = "black",
  win.asp = 1,
  ...
)
1
M <- cor(mtcars)

3. 绘图

展示方式：

1
corrplot(M)

alt 图标

1
corrplot(M,method="number")

alt 图标

1
corrplot(M,method="color")

alt 图标

method还有其他选项：square、shade、pie、ellipse等。

排序：

1
corrplot(M,order = "hclust")

alt 图标

1
corrplot(M,order = "AOE")

alt 图标

添加相关性系数数值：

1
corrplot(M,order = "hclust",addCoef.col = "grey")

alt 图标

添加聚类框：

1
corrplot(M, order = "hclust",addrect = 2,rect.col='red')

alt 图标

修改颜色：

1
corrplot(M, order = "hclust",tl.col='black')

alt 图标

设置展示类型

type可以选upper、lower、full。

1
corrplot(M, order = "hclust", type='upper')

alt 图标

4. pvalue不显著的不显示

1
2
3
# 先进行显著性检验
corrl <- cor.mtest(mtcars)
corrplot(M, order = "hclust", p.mat = corrl$p)

默认的会对不显著的打叉：
alt 图标

添加insig参数，指定不显著的为空

1
corrplot(M, order = "hclust", p.mat = corrl$p,insig = "blank")

alt 图标

显示pvalue的*。

1
2
3
4
corrplot(M, order = "hclust", 
         p.mat = corrl$p,insig='label_sig',
         sig.level = c(0.001,0.01,0.05),
         pch.cex = 0.9,pch.col = 'green')

alt 图标

以上是corrplot主要功能。注意，corrplot包只适用于正方形，也就是说横向和纵向一样的去比较。