nimare.meta.cbma.mkda.MKDADensity

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

Multilevel kernel density analysis- Density analysis [R8ac5f6c30bba-1].

Parameters:
  • 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: MKDADensity.correct_fwe_permutation

References

[R8ac5f6c30bba-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_fwe_permutation(self, result[, …]) Perform FWE correction using the max-value permutation method.
fit(self, dataset) Fit Estimator to Dataset.
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_fwe_permutation(self, result, voxel_thresh=0.01, n_iters=1000, 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.
  • voxel_thresh (float, optional) – Cluster-defining OF-value threshold. Default is 0.01.
  • n_iters (int, optional) – Number of iterations to build the vFWE and cFWE null distributions. Default is 1000.
  • 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: ‘vthresh’, ‘logp_level-cluster’, and ‘logp_level-voxel’.
Return type:

dict

See also

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

Examples

>>> meta = MKDADensity()
>>> result = meta.fit(dset)
>>> corrector = FWECorrector(method='permutation', voxel_thresh=0.01,
                             n_iters=5, n_cores=1)
>>> cresult = corrector.transform(result)
fit(self, dataset)[source]

Fit Estimator to Dataset.

Parameters:dataset (nimare.dataset.Dataset) – Dataset object 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