nimare.meta.cbma.ale.SCALE

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

Specific coactivation likelihood estimation [Re194cb71ed63-1].

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
  • ijk (str or (N x 3) array_like) – Tab-delimited file of coordinates from database or numpy array with ijk coordinates. Voxels are rows and i, j, k (meaning matrix-space) values are the three columnns.
  • kernel_estimator (nimare.meta.cbma.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.
  • **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.

References

[Re194cb71ed63-1]Langner, Robert, et al. “Meta-analytic connectivity modeling revisited: controlling for activation base rates.” NeuroImage 99 (2014): 559-570. https://doi.org/10.1016/j.neuroimage.2014.06.007

Methods

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.
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