请生成一段MK检验Python代码
时间: 2023-12-04 14:02:39 浏览: 39
以下是一段使用Python进行MK检验的示例代码:
```python
import numpy as np
from scipy.stats import norm
def mk_test(x):
n = len(x)
# 计算符号函数s
s = np.zeros(n)
for i in range(n-1):
for j in range(i+1, n):
s[i] += np.sign(x[j] - x[i])
# 计算MK统计量
mk = np.sum(s)
# 计算方差
var_s = (n*(n-1)*(2*n+5))/18
# 计算标准正态分布的z值
z = (mk - 1)/np.sqrt(var_s)
# 计算p值
p = 2*(1 - norm.cdf(abs(z)))
return mk, var_s, z, p
```
该代码使用了SciPy库中的norm函数来计算标准正态分布的累积分布函数。使用该函数时,只需将待检验的数据作为输入参数传入mk_test函数即可,该函数将返回MK统计量、方差、z值和p值。
相关问题
mk趋势检验python代码
以下是使用Python进行Mann-Kendall趋势检验的示例代码:
```python
import numpy as np
from scipy.stats import kendalltau
def mk_test(data):
n = len(data)
s = 0
for k in range(n-1):
for j in range(k+1, n):
s += np.sign(data[j] - data[k])
uniq, counts = np.unique(data, return_counts=True)
if len(uniq) == len(data):
var_s = (n*(n-1)*(2*n+5))/18
else:
var_s = (n*(n-1)*(2*n+5) - sum(counts*(counts-1)*(2*counts+5)))/18
if s > 0:
z = (s - 1) / np.sqrt(var_s)
elif s < 0:
z = (s + 1) / np.sqrt(var_s)
else:
z = 0
p = 2*(1 - norm.cdf(abs(z)))
trend = "increasing" if z > 0 else "decreasing" if z < 0 else "no trend"
return z, p, trend
# 示例数据
data = [10, 13, 15, 7, 8, 11, 9]
z, p, trend = mk_test(data)
print("Mann-Kendall Test:")
print("Z-statistic:", z)
print("P-value:", p)
print("Trend:", trend)
```
在上面的示例代码中,我们首先定义了一个`mk_test`函数,该函数接受一个数据列表作为输入,并返回Mann-Kendall趋势检验的结果:Z统计量,P值以及趋势(增加、减少或无趋势)。
然后,我们提供了一个示例数据列表`data`,并调用`mk_test`函数进行趋势检验。最后,打印出检验结果。
请注意,示例代码中使用了`numpy`和`scipy.stats`库来进行计算和统计。确保在运行代码之前安装这些库。
mk趋势检验Python代码
Sure! Here's an example of how you can perform the Mann-Kendall trend test in Python using the `pymannkendall` library:
```python
import numpy as np
from pymannkendall import mk_test
# Generate a sample dataset
data = np.random.random(100)
# Perform Mann-Kendall test
result = mk_test(data)
# Print the test statistic and p-value
print("Mann-Kendall test statistic:", result[0])
print("Mann-Kendall p-value:", result[1])
```
In this code, we first import the necessary libraries, `numpy` for generating sample data and `pymannkendall` for performing the Mann-Kendall test. Then, we generate a sample dataset, `data`, which could be your actual data.
Next, we call the `mk_test` function from `pymannkendall` and pass our data as an argument. The function returns a tuple containing the test statistic and p-value.
Finally, we print the test statistic and p-value to see the results of the Mann-Kendall trend test.
Make sure you have the `pymannkendall` library installed before running this code. You can install it using pip: `pip install pymannkendall`.