nimare.meta.cbma.ale.SCALE

class SCALE(voxel_thresh=0.001, n_iters=10000, n_cores=-1, xyz=None, kernel_transformer=<class 'nimare.meta.kernel.ALEKernel'>, memory_limit=None, **kwargs)[source]

Bases: nimare.meta.cbma.base.CBMAEstimator

Specific coactivation likelihood estimation.

Changed in version 0.0.10: Replace ijk with xyz. This should be easier for users to collect.

Parameters
  • voxel_thresh (float, optional) – Uncorrected voxel-level threshold. Default: 0.001

  • n_iters (int, optional) – Number of iterations for correction. Default: 10000

  • n_cores (int, optional) – Number of processes to use for meta-analysis. If -1, use all available cores. Default: -1

  • xyz (str or (N x 3) array_like) – Tab-delimited file of coordinates from database or numpy array with XYZ coordinates. Voxels are rows and x, y, z (meaning coordinates) values are the three columnns.

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

  • memory_limit (str or None, optional) – Memory limit to apply to data. If None, no memory management will be applied. Otherwise, the memory limit will be used to (1) assign memory-mapped files and (2) restrict memory during array creation to the limit. Default is None.

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

References

compute_summarystat(data)[source]

Compute summary statistics from data.

The actual summary statistic varies across Estimators. For ALE and SCALE, the values are known as ALE values. For (M)KDA, they are “OF” scores.

Parameters

data (array, pandas.DataFrame, or list of img_like) – Data from which to estimate summary statistics. The data can be: (1) a 1d contrast-len or 2d contrast-by-voxel array of MA values, (2) a DataFrame containing coordinates to produce MA values, or (3) a list of imgs containing MA values.

Returns

stat_values (1d array) – Summary statistic values. One value per voxel.

correct_fwe_montecarlo(result, voxel_thresh=0.001, n_iters=10000, n_cores=- 1, vfwe_only=False)[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 p-value threshold. Default is 0.001.

  • n_iters (int, optional) – Number of iterations to build the vFWE and cFWE null distributions. 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: ‘vthresh’, ‘logp_level-cluster’, and ‘logp_level-voxel’.

See also

nimare.correct.FWECorrector

The Corrector from which to call this method.

Examples

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

Fit Estimator to Dataset.

Parameters
  • dataset (nimare.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

nimare.results.MetaResult – Results of Estimator fitting.

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

Returns

params (dict) – 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