nimare.meta.ibma.Stouffers

class Stouffers(use_sample_size=False, **kwargs)[source]

Bases: IBMAEstimator

A t-test on z-statistic images.

Requires z-statistic images.

This method is described in Stouffer et al.1.

Parameters

use_sample_size (bool, optional) – Whether to use sample sizes for weights (i.e., “weighted Stouffer’s”) or not, as described in Zaykin2. Default is False.

Notes

Requires z images and optionally the sample size metadata field.

fit() produces a MetaResult object with the following maps:

“z”

Z-statistic map from one-sample test.

“p”

P-value map from one-sample test.

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

1

Samuel A Stouffer, Edward A Suchman, Leland C DeVinney, Shirley A Star, and Robin M Williams Jr. The american soldier: adjustment during army life.(studies in social psychology in world war ii), vol. 1. Studies in social psychology in World War II, 1949.

2

Dmitri V Zaykin. Optimally weighted z-test is a powerful method for combining probabilities in meta-analysis. Journal of evolutionary biology, 24(8):1836–1841, 2011. URL: https://doi.org/10.1111/j.1420-9101.2011.02297.x, doi:10.1111/j.1420-9101.2011.02297.x.

See also

pymare.estimators.StoufferCombinationTest

The PyMARE estimator called by this class.

Methods

fit(dataset[, drop_invalid])

Fit Estimator to Dataset.

get_params([deep])

Get parameters for this estimator.

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.
fit(dataset, drop_invalid=True)[source]

Fit Estimator to Dataset.

Parameters

  • 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

Results of Estimator fitting.

Return type

MetaResult

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)[source]

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 load(filename, compressed=True)[source]

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)[source]

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)[source]

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