nimare.correct.FWECorrector

class FWECorrector(method='bonferroni', **kwargs)

Bases: Corrector

Perform family-wise error rate correction on a meta-analysis.

Parameters:

• method (str) – The FWE correction to use. Available internal methods are ‘bonferroni’. Additional methods may be implemented within the provided Estimator.

• **kwargs – Keyword arguments to be used by the FWE correction implementation.

Notes

This corrector supports a small number of internal FWE correction methods, but can also use special methods implemented within individual Estimators. To determine what methods are available for the Estimator you’re using, use inspect(). Estimators have special methods following the naming convention correct_[correction-type]_[method] (e.g., correct_fwe_montecarlo()).

Methods

correct_fwe_bonferroni(p)	Perform Bonferroni FWE correction.
get_params([deep])	Get parameters for this estimator.
inspect(result)	Identify valid 'method' values for a MetaResult object.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.
transform(result)	Apply the multiple comparisons correction method to a MetaResult object.

correct_fwe_bonferroni(p)

Perform Bonferroni FWE correction.

This correction is based on the one described in Bonferroni[1] and Shaffer[2].

Warning

Do not call this method directly. Call transform() with method='bonferroni' instead.

New in version 0.0.12.

Parameters:

p (numpy.ndarray) – A 1D array of p values.

Returns:

• p_corr (numpy.ndarray) – A 1D array of adjusted p values.

• tables (dict) – A dictionary of DataFrames with summary information from the correction. This correction method does not produce any tables, so it will be an empty dict.

• description_ (str) – A description of the correction procedure.

References

[1]

Carlo Bonferroni. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze, 8:3–62, 1936.

[2]

Juliet Popper Shaffer. Multiple hypothesis testing. Annual review of psychology, 46(1):561–584, 1995.

See also

nimare.stats.bonferroni

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

classmethod inspect(result)

Identify valid ‘method’ values for a MetaResult object.

In addition to returning a list of valid values, this method will also print out those values, divided by the value type (Estimator or generic).

Parameters:

result (MetaResult) – Object for which valid correction methods (i.e., ‘method’ values) will be identified.

Returns:

List of valid ‘method’ values for the Corrector+Estimator combination, including both non-specific methods and Estimator-specific ones.

Return type:

list

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.

• compressed (bool, optional) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns:

obj – Loaded class object.

Return type:

class object

save(filename, compress=True)

Pickle the class instance to the provided file.

Parameters:

• filename (str) – File to which object will be saved.

• compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Return type:

self

transform(result)

Apply the multiple comparisons correction method to a MetaResult object.

Parameters:

result (MetaResult) – MetaResult generated by an Estimator to be corrected for multiple comparisons.

Returns:

result – MetaResult with new corrected maps, tables, and description added.

Return type:

MetaResult

Examples using nimare.correct.FWECorrector

Coordinate-based meta-analysis algorithms

Image-based meta-analysis algorithms

The Corrector class

Meta-analytic coactivation modeling analysis

Two-sample ALE meta-analysis