nimare.meta.ibma
.Fishers
- class Fishers(*args, **kwargs)[source]
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.
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
- Returns
Results of Estimator fitting.
- Return type
- Variables
~Estimator.fit.inputs_ (
dict
) – Inputs used in _fit.
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters
- Returns
obj – Loaded class object.
- Return type
class object
- 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