python - Python中的惩罚cox回归-选择惩罚强度α后如何获得系数和置信区间？

Question

我正在尝试在 python 中进行惩罚 cox 回归并获得系数，我正在努力获得置信区间并感谢您的帮助。运行此代码，我可以获得每个变量的系数。但是，我正在努力获得 P 值或置信区间

我想知道获得 P 值和置信区间的下一步是什么代码是：

import warnings

from sklearn.exceptions import ConvergenceWarning

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import StandardScaler

coxnet_pipe = make_pipeline(
    StandardScaler(),
    CoxnetSurvivalAnalysis(l1_ratio=0.9, alpha_min_ratio=0.01, max_iter=100)
)

warnings.simplefilter("ignore", ConvergenceWarning)

coxnet_pipe.fit(Xt, y)

estimated_alphas = coxnet_pipe.named_steps["coxnetsurvivalanalysis"].alphas_
cv = KFold(n_splits=5, shuffle=True, random_state=0)

gcv = GridSearchCV(
    make_pipeline(StandardScaler(), CoxnetSurvivalAnalysis(l1_ratio=0.9)),
    param_grid={"coxnetsurvivalanalysis__alphas": [[v] for v in estimated_alphas]},
    cv=cv,
    error_score=0.5,
    n_jobs=4).fit(Xt, y)

cv_results = pd.DataFrame(gcv.cv_results_)

best_model = gcv.best_estimator_.named_steps["coxnetsurvivalanalysis"]

best_coefs = pd.DataFrame(
    best_model.coef_,
    index=Xt.columns,
    columns=["coefficient"]
)