hidimstat.ensemble_clustered_inference_pvalue#
- hidimstat.ensemble_clustered_inference_pvalue(n_samples, group, list_ward, list_beta_hat, list_theta_hat, list_precision_diag, fdr=0.1, fdr_control='bhq', reshaping_function=None, adaptive_aggregation=False, gamma=0.5, n_jobs=None, verbose=0, **kwargs)[source]#
Compute and aggregate p-values across multiple bootstrap iterations using an aggregation method.
This function performs statistical inference on each bootstrap sample and combines the results using a specified aggregation method to obtain robust estimates. The implementation follows the methodology in [1].
- Parameters:
- n_samplesint
Number of samples in the dataset
- groupbool
If True, uses group lasso p-values for multivariate outcomes
- list_wardlist of AgglomerativeClustering
List of fitted clustering objects from bootstraps
- list_beta_hatlist of ndarray
List of estimated coefficients at cluster level from each bootstrap
- list_theta_hatlist of ndarray
List of estimated precision matrices from each bootstrap
- list_precision_diaglist of ndarray
List of diagonal elements of covariance matrices from each bootstrap
- fdrfloat, default=0.1
False discovery rate threshold for multiple testing correction
- fdr_controlstr, default=”bhq”
Method for FDR control (‘bhq’ for Benjamini-Hochberg) Available methods are: * ‘bhq’: Standard Benjamini-Hochberg [2][3] * ‘bhy’: Benjamini-Hochberg-Yekutieli [3] * ‘ebh’: e-Benjamini-Hochberg [4]
- reshaping_functioncallable, optional (default=None)
Function to reshape data before FDR control
- adaptive_aggregationbool, default=False
Whether to use adaptive quantile aggregation
- gammafloat, default=0.5
Quantile level for aggregation
- n_jobsint or None, optional (default=None)
Number of parallel jobs. None means using all processors.
- verboseint, default=0
Verbosity level for computation progress
- **kwargsdict
Additional arguments passed to p-value computation functions
- Returns:
- beta_hatndarray, shape (n_features,) or (n_features, n_times)
Averaged coefficients across bootstraps
- selectedndarray, shape (n_features,)
Selected features: 1 for positive effects, -1 for negative effects, 0 for non-selected features
References
