hidimstat.hd_inference¶
- hidimstat.hd_inference(X, y, method, n_jobs=1, memory=None, verbose=0, **kwargs)¶
Wrap-up high-dimensional inference procedures
- Parameters:
- Xndarray, shape (n_samples, n_features)
Data.
- yndarray, shape (n_samples,) or (n_samples, n_times)
Target.
- methodstr, optional (default=’desparsified-lasso’)
Method used for making the inference. Currently the two methods available are ‘desparsified-lasso’ and ‘group-desparsified-lasso’. Use ‘desparsified-lasso’ for non-temporal data and ‘group-desparsified-lasso’ for temporal data.
- n_jobsint or None, optional (default=1)
Number of CPUs to use during parallel steps such as inference.
- memorystr or joblib.Memory object, optional (default=None)
Used to cache the output of the computation of the clustering and the inference. By default, no caching is done. If a string is given, it is the path to the caching directory.
- verbose: int, optional (default=1)
The verbosity level. If verbose > 0, we print a message before runing the clustered inference.
- **kwargs:
Arguments passed to the statistical inference function.
- Returns:
- beta_hatndarray, shape (n_features,) or (n_features, n_times)
Estimated parameter vector or matrix.
- pvalndarray, shape (n_features,)
p-value, with numerically accurate values for positive effects (ie., for p-value close to zero).
- pval_corrndarray, shape (n_features,)
p-value corrected for multiple testing.
- one_minus_pvalndarray, shape (n_features,)
One minus the p-value, with numerically accurate values for negative effects (ie., for p-value close to one).
- one_minus_pval_corrndarray, shape (n_features,)
One minus the p-value corrected for multiple testing.