nimare.meta.ibma.IBMAEstimator

class IBMAEstimator(aggressive_mask=True, memory=Memory(location=None), memory_level=0, *, mask=None, **kwargs)[source]

Bases: Estimator

Base class for meta-analysis methods in meta.

Changed in version 0.2.1:

  • New parameters: memory and memory_level for memory caching.

Changed in version 0.2.0:

  • Remove resample and memory_limit arguments. Resampling is now performed only if shape/affines are different.

Added in version 0.0.12:

  • IBMA-specific elements of Estimator excised and used to create IBMAEstimator.

  • Generic kwargs and args converted to named kwargs. All remaining kwargs are for resampling.

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, default=True) – 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, default=True) – 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

Examples using nimare.meta.ibma.IBMAEstimator

Use NeuroVault statistical maps in NiMARE

Use NeuroVault statistical maps in NiMARE

Image-based meta-analysis algorithms

Image-based meta-analysis algorithms

The Corrector class

The Corrector class

Compare image and coordinate based meta-analyses

Compare image and coordinate based meta-analyses