gemmr.sample_analysis.analyzers.analyze_resampled¶
-
gemmr.sample_analysis.analyzers.
analyze_resampled
(estr, X, Y, Xorig=None, Yorig=None, x_align_ref=None, y_align_ref=None, addons=(), n_bs=0, perm=None, loo=False, random_state=None, saved_perm_features='all', show_progress=True, n_jobs=1, **kwargs)¶ Analyze a given dataset and resampled versions of it with a given estimator.
In addition to the dataset itself resampled versions of it are analyzed in the same way. Provided resampling strategies include:
- bootstrap
- leave-one-out
- permutation
Parameters: - estr (sklearn-style estimator) – for performing CCA or PLS. Must have method
fit
and (after fitting) attributesassocs_
,x_rotations_
,y_rotations_
,x_scores_
,y_scores_
- X (np.ndarray (n_samples, n_features)) – dataset X
- Y (np.ndarray (n_samples, n_features)) – dataset Y
- Xorig (
None
or np.ndarray (n_samples, n_orig_features)) – ifNone
set toX
. Allows to provide an alternative set of X features for calculating loadings. I.e. an implicit assumption is that the rows inX
andXorig
correspond to the same samples (subjects). - Yorig (
None
or np.ndarray (n_samples, n_orig_features)) – ifNone
set toY
. Allows to provide an alternative set of Y features for calculating loadings. I.e. an implicit assumption is that the rows inY
andYorig
correspond to the same samples (subjects). - x_align_ref ((n_features,)) – after fitting, the sign of X weights is chosen such that the
cosine-distance between fitted X weights and
x_align_ref
is positive - y_align_ref ((n_features,)) – after fitting, the sign of Y weights is chosen such that the
cosine-distance between fitted Y weights and
y_align_ref
is positive - addons (list-like of add-on functions) –
After fitting the estimator and saving association strengths, weights and loadings in
results
additional analyses can be performed with these functions. They are called in the given order, and must have the signatureaddana_fun(estr, X, Y, Xorig, Yorig, x_align_ref, y_align_ref, results, **kwargs)
and are expected to save their respective outcome features
results
. Various such functions are provided in modulesample_analysis_addons
- n_bs (int) – number of bootstrap iterations to perform on the data
- perm (None, int, or iterable) – if
None
no permutations are performed, ifint
gives the number of permutations to perform on the data, if iterable each element is an array giving the permuted indices to use - loo (bool) – if
True
leave-one-out analysis is performed on the data - random_state (
None
, int or random number generator instance) – used to generate random numbers - saved_perm_features ('all' or list-like of 'str') – if ‘all’ all outcome features resulting from the permutations are returned, otherwise only data variables indicated by this list are
- show_progress (bool) – whether to show progress bar
- n_jobs (int or None) – number of parallel jobs (see
joblib.Parallel
) - kwargs (dict) – forwarded to additional analysis functions
Returns: results – containing data variables for outcome features generated by analyses. Data variables obtained from bootstrapping are suffixed ‘_bs’, from permutations ‘_perm’ and from leave-one-out ‘_loo’
Return type: xr.Dataset