nimare.meta.cbma.mkda.KDA

class KDA(kernel_transformer=<class 'nimare.meta.cbma.kernel.KDAKernel'>, **kwargs)

Kernel density analysis.

Parameters

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

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

Notes

Kernel density analysis was first introduced in 1 and 2.

Available correction methods: KDA.correct_fwe_montecarlo

References

1

Wager, Tor D., et al. “Valence, gender, and lateralization of functional brain anatomy in emotion: a meta-analysis of findings from neuroimaging.” Neuroimage 19.3 (2003): 513-531. https://doi.org/10.1016/S1053-8119(03)00078-8

2

Wager, Tor D., John Jonides, and Susan Reading. “Neuroimaging studies of shifting attention: a meta-analysis.” Neuroimage 22.4 (2004): 1679-1693. https://doi.org/10.1016/j.neuroimage.2004.03.052

correct_fwe_montecarlo(result, n_iters=10000, n_cores=-1)

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 10000.

• 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: ‘logp_level-voxel’.

See also

nimare.correct.FWECorrector()

The Corrector from which to call this method.

Examples

>>> meta = KDA()
>>> result = meta.fit(dset)
>>> corrector = FWECorrector(method='montecarlo', n_iters=5, n_cores=1)
>>> cresult = corrector.transform(result)

fit(dataset)

Fit Estimator to Dataset.

Parameters

dataset (nimare.dataset.Dataset) – Dataset object to analyze.

Returns

nimare.results.MetaResult – Results of Estimator fitting.

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)

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)

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)

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)

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