Python中创建相关系数矩阵的方法小结

import numpy as np

# 创建一个数据集，每列代表一个变量
data = np.array([
    [1, 2, 3, 4, 5],
    [2, 3, 4, 5, 6],
    [5, 4, 3, 2, 1]
])

# 计算皮尔逊相关系数矩阵
correlation_matrix = np.corrcoef(data)

print("皮尔逊相关系数矩阵:")
print(correlation_matrix)

import pandas as pd

# 创建一个数据帧
data = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [2, 3, 4, 5, 6],
    'C': [5, 4, 3, 2, 1]
})

# 计算皮尔逊相关系数矩阵
correlation_matrix = data.corr()

print("皮尔逊相关系数矩阵:")
print(correlation_matrix)

from scipy import stats

# 创建两个变量的数据集
x = [1, 2, 3, 4, 5]
y = [2, 3, 4, 5, 6]

# 计算皮尔逊相关系数
pearson_correlation, _ = stats.pearsonr(x, y)

print("皮尔逊相关系数:", pearson_correlation)

import pandas as pd

# 创建两个数据帧
data1 = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [2, 3, 4, 5, 6]})
data2 = pd.DataFrame({'C': [5, 4, 3, 2, 1], 'D': [6, 5, 4, 3, 2]})

# 计算两个数据帧之间的皮尔逊相关系数矩阵
correlation_matrix = data1.corrwith(data2)

print("两个数据帧的皮尔逊相关系数矩阵:")
print(correlation_matrix)

from scipy import stats

# 创建两个变量的数据集
x = [1, 2, 3, 4, 5]
y = [2, 3, 4, 5, 6]

# 计算斯皮尔曼相关系数
spearman_correlation, _ = stats.spearmanr(x, y)

print("斯皮尔曼相关系数:", spearman_correlation)

from scipy import stats

# 创建两个变量的数据集
x = [1, 2, 3, 4, 5]
y = [2, 3, 4, 5, 6]

# 计算肯德尔相关系数
kendall_correlation, _ = stats.kendalltau(x, y)

print("肯德尔相关系数:", kendall_correlation)

from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

# 创建训练数据集
X = [[1], [2], [3], [4], [5]]
y = [2, 4, 5, 4, 5]

# 训练线性回归模型
model = LinearRegression()
model.fit(X, y)

# 预测值
predictions = model.predict(X)

# 计算 R^2
r2 = r2_score(y, predictions)

print("R^2 分数:", r2)