python - R和Python的cov和cor的区别

Question

我经常使用 R，但我是 Python 的新手。 在 R 中，计算给定矩阵的均值、cov 和 cor 的演示 给出如下:

X = matrix(c(1,0.5,3,7,9,6,2,8,4), nrow=3, ncol=3, byrow=FALSE)
X
    # [,1] [,2] [,3]
# [1,]  1.0    7    2
# [2,]  0.5    9    8
# [3,]  3.0    6    4
M = colMeans(X) # apply(X,2,mean)
M
# [1] 1.500000 7.333333 4.666667
S = cov(X)
S
    # [,1]      [,2]      [,3]
# [1,]  1.75 -1.750000 -1.500000
# [2,] -1.75  2.333333  3.666667
# [3,] -1.50  3.666667  9.333333
R = cor(X)
R
        # [,1]       [,2]       [,3]
# [1,]  1.0000000 -0.8660254 -0.3711537
# [2,] -0.8660254  1.0000000  0.7857143
# [3,] -0.3711537  0.7857143  1.0000000

我想用 Python 重现上面的内容，我尝试:

import numpy as np
X = np.array([1,0.5,3,7,9,6,2,8,4]).reshape(3, 3)
X = np.transpose(X) # byrow=FALSE
X
# array([[ 1. ,  7. ,  2. ],
    # [ 0.5,  9. ,  8. ],
    # [ 3. ,  6. ,  4. ]])

M = X.mean(axis=0) # colMeans
M
# array([ 1.5       ,  7.33333333,  4.66666667])
S = np.cov(X)
S
# array([[ 10.33333333,  10.58333333,   4.83333333],
    # [ 10.58333333,  21.58333333,   5.83333333],
    # [  4.83333333,   5.83333333,   2.33333333]])
R = np.corrcoef(X)
R
# array([[ 1.        ,  0.70866828,  0.98432414],
    # [ 0.70866828,  1.        ,  0.82199494],
    # [ 0.98432414,  0.82199494,  1.        ]])

那么cov和cor的结果就不一样了。为什么？

Answer 1

这是因为 numpy 按行计算，R 按列计算。注释掉 X = np.transpose(X) # byrow=FALSE，或使用 np.cov(X, rowvar=False)。

np.cov(X, rowvar=False)
array([[ 1.75      , -1.75      , -1.5       ],
       [-1.75      ,  2.33333333,  3.66666667],
       [-1.5       ,  3.66666667,  9.33333333]])

不同之处在各自的文档中有解释(重点是我的):

python :

help(np.cov)

rowvar : bool, optional If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.

回复:

?cov

var, cov and cor compute the variance of x and the covariance or correlation of x and y if these are vectors. If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.