nimare.meta.cbma.mkda.MKDAChi2

class MKDAChi2(prior=0.5, kernel_transformer=<class 'nimare.meta.cbma.kernel.MKDAKernel'>, **kwargs)[source]

Multilevel kernel density analysis- Chi-square analysis.

Parameters

  • prior (float, optional) – Uniform prior probability of each feature being active in a map in the absence of evidence from the map. Default: 0.5

  • kernel_transformer (nimare.meta.cbma.kernel.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is MKDAKernel.

  • **kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix ‘kernel__’ in the variable name.

Notes

Available correction methods: MKDAChi2.correct_fwe_montecarlo, MKDAChi2.correct_fdr_bh

References

  • Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015

correct_fdr_bh(result, alpha=0.05)[source]

Perform FDR correction using the Benjamini-Hochberg method. Only call this method from within a Corrector.

Parameters
Returns

images (dict) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘consistency_z_FDR’ and ‘specificity_z_FDR’.

See also

nimare.correct.FDRCorrector()

The Corrector from which to call this method.

Examples

>>> meta = MKDAChi2()
>>> result = meta.fit(dset)
>>> corrector = FDRCorrector(method='bh', alpha=0.05)
>>> cresult = corrector.transform(result)
correct_fwe_montecarlo(result, n_iters=5000, n_cores=-1)[source]

Perform FWE correction using the max-value permutation method. Only call this method from within a Corrector.

Parameters

  • result (nimare.results.MetaResult) – Result object from a KDA meta-analysis.

  • n_iters (int, optional) – Number of iterations to build the vFWE null distribution. Default is 5000.

  • n_cores (int, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is -1.

Returns

images (dict) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘p_desc-consistency_level-voxel’, ‘z_desc-consistency_level-voxel’, ‘p_desc-specificity_level-voxel’, and ‘p_desc-specificity_level-voxel’.

See also

nimare.correct.FWECorrector()

The Corrector from which to call this method.

Examples

>>> meta = MKDAChi2()
>>> result = meta.fit(dset)
>>> corrector = FWECorrector(method='montecarlo', n_iters=5, n_cores=1)
>>> cresult = corrector.transform(result)
fit(dataset1, dataset2)[source]

Fit CBMAEstimator to datasets.

Parameters

dataset1/dataset2 (nimare.dataset.Dataset) – Dataset objects to analyze.

Returns

nimare.results.MetaResult – Results of CBMAEstimator fitting, with the following maps: ‘prob_desc-A’, ‘prob_desc-AgF’, ‘prob_desc-FgA’, ‘prob_desc-AgF_given_pF=XX’, ‘prob_desc-FgA_given_pF=XX’, ‘z_desc-consistency’, ‘z_desc-specificity’, ‘chi2_desc-consistency’, ‘chi2_desc-specificity’, ‘p_desc-consistency’, and ‘p_desc-specificity’

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params (mapping of string to any) – Parameter names mapped to their values.

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 (class object) – Loaded 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.

Returns

self