nimare.meta.ibma.Fishers

class Fishers(*args, **kwargs)

Bases: nimare.base.MetaEstimator

An image-based meta-analytic test using t- or z-statistic images.

Requires z-statistic images, but will be extended to work with t-statistic images as well.

Notes

Requires z images.

Warning

Masking approaches which average across voxels (e.g., NiftiLabelsMaskers) will result in invalid results. It cannot be used with these types of maskers.

All image-based meta-analysis estimators adopt an aggressive masking strategy, in which any voxels with a value of zero in any of the input maps will be removed from the analysis.

References

• Fisher, R. A. (1934). Statistical methods for research workers. Statistical methods for research workers., (5th Ed). https://www.cabdirect.org/cabdirect/abstract/19351601205

See also

pymare.estimators.FisherCombinationTest

The PyMARE estimator called by this class.

fit(dataset, drop_invalid=True)

Fit Estimator to Dataset.

Parameters

• dataset (nimare.dataset.Dataset) – Dataset object to analyze.

• drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is False.

Returns

nimare.results.MetaResult – Results of Estimator fitting.

Variables

inputs_ (dict) – Inputs used in _fit.

Notes

The fit method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Estimators’ individual “fitting” methods are implemented as _fit, although users should call fit.

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 (dict) – Parameter names mapped to their values.

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 (class object) – Loaded 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.

Returns

self

Examples using nimare.meta.ibma.Fishers

Using Neurovault Statistical Maps in NiMARE

Run image-based meta-analyses on 21 pain studies