nispace.stats.coloc.lasso

nispace.stats.coloc.lasso(x, y, cv=None, seed=None, kwargs={})[source]

Lasso-regularized regression of x on y via sklearn.linear_model.LassoCV.

Parameters:

  • x (np.ndarray, shape (n_obs, n_predictors)) – Does not handle NaN – sklearn errors on NaN input; pre-mask.

  • y (np.ndarray, shape (n_obs,)) –

  • cv (int, cross-validation generator, or None) – Passed to LassoCV for selecting alpha.

  • seed (int, optional) – Passed as LassoCV’s random_state.

  • kwargs (dict, default {}) – Forwarded to LassoCV.

Returns:

out – "alpha" (selected regularization strength), "r2", "beta" (shape (n_predictors,)).

Return type:

dict

Notes

Used by core/colocalize.py’s regularized-regression colocalization case, with NaN excluded list-wise (see elasticnet’s Notes) before calling this function.