如何在 Python 中获得 R 的 ccf?
How do I get R's ccf in Python?
我不是统计学家,我只是将一些 R 代码翻译成 Python。
回复:
a = 1:1000
b = 1000:1
ccf(a, b, max.lag=100, plot=FALSE)
Autocorrelations of series ‘X’, by lag
-26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16
-0.922 -0.925 -0.928 -0.931 -0.934 -0.937 -0.940 -0.943 -0.946 -0.949 -0.952
-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5
-0.955 -0.958 -0.961 -0.964 -0.967 -0.970 -0.973 -0.976 -0.979 -0.982 -0.985
-4 -3 -2 -1 0 1 2 3 4 5 6
-0.988 -0.991 -0.994 -0.997 -1.000 -0.997 -0.994 -0.991 -0.988 -0.985 -0.982
7 8 9 10 11 12 13 14 15 16 17
-0.979 -0.976 -0.973 -0.970 -0.967 -0.964 -0.961 -0.958 -0.955 -0.952 -0.949
18 19 20 21 22 23 24 25 26
-0.946 -0.943 -0.940 -0.937 -0.934 -0.931 -0.928 -0.925 -0.922
Python:
import scipy.signal as ss
import numpy as np
x = np.array(range(1, 1001))
y = np.array(range(1000, 0, -1))
ss.correlate(x, y)
# array([ 1, 4, 10, ..., 2994001, 1998000, 1000000])
ss.correlate(x - np.mean(x), y - np.mean(y), method='direct')/(np.std(x)*np.std(y)*len(x))
# array([0.00299401, 0.00597602, 0.00894607, ..., 0.00894607, 0.00597602,
0.00299401])
None 这些答案都接近 R 结果。如何在 Python 中获得相同的结果?
这是一个功能相同的函数:
import scipy.signal as ss
def ccf(x, y, lag_max = 100):
result = ss.correlate(y - np.mean(y), x - np.mean(x), method='direct') / (np.std(y) * np.std(x) * len(y))
length = (len(result) - 1) // 2
lo = length - lag_max
hi = length + (lag_max + 1)
return result[lo:hi]
我不是统计学家,我只是将一些 R 代码翻译成 Python。
回复:
a = 1:1000
b = 1000:1
ccf(a, b, max.lag=100, plot=FALSE)
Autocorrelations of series ‘X’, by lag
-26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16
-0.922 -0.925 -0.928 -0.931 -0.934 -0.937 -0.940 -0.943 -0.946 -0.949 -0.952
-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5
-0.955 -0.958 -0.961 -0.964 -0.967 -0.970 -0.973 -0.976 -0.979 -0.982 -0.985
-4 -3 -2 -1 0 1 2 3 4 5 6
-0.988 -0.991 -0.994 -0.997 -1.000 -0.997 -0.994 -0.991 -0.988 -0.985 -0.982
7 8 9 10 11 12 13 14 15 16 17
-0.979 -0.976 -0.973 -0.970 -0.967 -0.964 -0.961 -0.958 -0.955 -0.952 -0.949
18 19 20 21 22 23 24 25 26
-0.946 -0.943 -0.940 -0.937 -0.934 -0.931 -0.928 -0.925 -0.922
Python:
import scipy.signal as ss
import numpy as np
x = np.array(range(1, 1001))
y = np.array(range(1000, 0, -1))
ss.correlate(x, y)
# array([ 1, 4, 10, ..., 2994001, 1998000, 1000000])
ss.correlate(x - np.mean(x), y - np.mean(y), method='direct')/(np.std(x)*np.std(y)*len(x))
# array([0.00299401, 0.00597602, 0.00894607, ..., 0.00894607, 0.00597602,
0.00299401])
None 这些答案都接近 R 结果。如何在 Python 中获得相同的结果?
这是一个功能相同的函数:
import scipy.signal as ss
def ccf(x, y, lag_max = 100):
result = ss.correlate(y - np.mean(y), x - np.mean(x), method='direct') / (np.std(y) * np.std(x) * len(y))
length = (len(result) - 1) // 2
lo = length - lag_max
hi = length + (lag_max + 1)
return result[lo:hi]