nimare.meta.ibma
.Fishers
- class Fishers(aggressive_mask=True, memory=Memory(location=None), memory_level=0, *, mask=None, **kwargs)[source]
Bases:
IBMAEstimator
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.
This method is described in Fisher and others[1].
Changed in version 0.2.1:
New parameter:
aggressive_mask
, to control whether to use an aggressive mask.
- Parameters:
aggressive_mask (
bool
, optional) – Voxels with a value of zero of NaN in any of the input maps will be removed from the analysis. If False, all voxels are included by running a separate analysis on bags of voxels that belong that have a valid value across the same studies. Default is True.
Notes
Requires
z
images.fit()
produces aMetaResult
object with the following maps:“z”
Z-statistic map from one-sample test.
“p”
P-value map from one-sample test.
“dof”
Degrees of freedom 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.
By default, 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. Setting
aggressive_mask=False
will instead run tha analysis in bags of voxels that have a valid value across the same studies.References
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:
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