python逐步回归调包

在Python中，可以使用statsmodels.api模块中的OLS函数来进行逐步回归的调包。以下是一个示例代码：

import statsmodels.api as sm

def stepwise(X, y, alpha_in=0.1, alpha_out=0.15):
    included = []
    
    while True:
        changed = False
        excluded = list(set(X.columns) - set(included))
        p_val = pd.Series(index=excluded)
        
        for new_column in excluded:
            model = sm.OLS(y, sm.add_constant(X[included + [new_column]])).fit()
            p_val[new_column = model.pvalues[new_column]
            
        min_pval = p_val.min()
        
        # forward step
        if min_pval < alpha_in:
            changed = True
            add_feature = p_val.idxmin()
            included.append(add_feature)
            print("Add {:20} with p_value {:.6}".format(add_feature, min_pval))
            
        # backward step
        model = sm.OLS(y, sm.add_constant(X[included])).fit()
        p_val = model.pvalues.iloc