python Housman检验代码

Hosmer-Lemeshow检验是一种统计方法，用于评估二元逻辑回归模型的拟合优度，尤其是预测结果是否均匀分布在各个类别上。Python中可以使用`statsmodels`库来进行这个检验。以下是简单的Hosmer-Lemeshow检验的代码示例：

from statsmodels.discrete.discrete_model import Logit
import pandas as pd
import statsmodels.api as sm

# 假设你有一个DataFrame df，其中包含 'response_variable' 和 'predictor_variables'
df = pd.read_csv('your_data.csv')  # 替换为实际数据文件路径
response = df['response_variable']
predictors = df[['predictor_1', 'predictor_2', ...]]  # 根据实际情况选择特征列名

# 将分类变量转换为哑编码
dummies = pd.get_dummies(predictors)
X = dummies.drop('response_variable', axis=1)  # 去除目标变量
y = response

# 创建Logistic回归模型
model = Logit(y, X)

# 拟合模型并获取残差
results = model.fit()
residuals = results.resid_response

# 进行Hosmer-Lemeshow检验
hl_test = sm HosmerLemeshow(residuals, y, groups=X.index)
p_value = hl_test.pvalue
test_statistic = hl_test.statistic

print("Hosmer-Lemeshow Test Results:")
print(f"Test Statistic: {test_statistic}")
print(f"P-value: {p_value}")

if p_value > 0.05:
    print("The model fits the data well.")
else:
    print("The model does not fit the data well. Consider revising your model.")