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.