nimare.meta.cbma.mkda.MKDAChi2

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

Multilevel kernel density analysis- Chi-square analysis [Rb50f9c63f995-1].

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_estimator (nimare.meta.cbma.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is MKDAKernel.
  • **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.

Notes

Available correction methods: MKDAChi2.correct_fwe_permutation, MKDAChi2.correct_fdr_bh

References

[Rb50f9c63f995-1]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

Methods

correct_fdr_bh(self, result[, alpha]) Perform FDR correction using the Benjamini-Hochberg method.
correct_fwe_permutation(self, result[, …]) Perform FWE correction using the max-value permutation method.
fit(self, dataset, dataset2) Fit Estimator to datasets.
get_params(self[, deep]) Get parameters for this estimator.
load(filename[, compressed]) Load a pickled class instance from file.
save(self, filename[, compress]) Pickle the class instance to the provided file.
set_params(self, \*\*params) Set the parameters of this estimator.
correct_fdr_bh(self, result, alpha=0.05)[source]

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

Parameters:
Returns:

images – 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’.
Return type:

dict

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_permutation(self, 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 – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘consistency_p_FWE’, ‘consistency_z_FWE’, ‘specificity_p_FWE’, and ‘specificity_z_FWE’.
Return type:

dict

See also

nimare.correct.FWECorrector()
The Corrector from which to call this method.

Examples

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

Fit Estimator to datasets.

Parameters:dataset/dataset2 (nimare.dataset.Dataset) – Dataset objects to analyze.
Returns:Results of Estimator fitting.
Return type:nimare.results.MetaResult
get_params(self, 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 – Parameter names mapped to their values.
Return type:mapping of string to any
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(self, 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(self, **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:
Return type:self