nimare.meta.cbma.ale.SCALE

class SCALE(xyz, n_iters=5000, n_cores=1, kernel_transformer=<class 'nimare.meta.kernel.ALEKernel'>, memory=Memory(location=None), memory_level=0, **kwargs)

Bases: CBMAEstimator

Specific coactivation likelihood estimation.

This method was originally introduced in Langner et al.[1].

Changed in version 0.2.1:

• New parameters: memory and memory_level for memory caching.

Changed in version 0.0.12:

• Remove unused parameters voxel_thresh and memory_limit.

• Use memmapped array for null distribution.

• Use a 4D sparse array for modeled activation maps.

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

Parameters:

• xyz ((N x 3) numpy.ndarray) –

  Numpy array with XYZ coordinates. Voxels are rows and x, y, z (meaning coordinates) values are the three columnns.

  Changed in version 0.0.12: This parameter was previously incorrectly labeled as “optional” and indicated that it supports tab-delimited files, which it does not (yet).

• n_iters (int, default=5000) – Number of iterations for statistical inference. Default: 5000

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

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

• memory (instance of joblib.Memory, str, or pathlib.Path) – Used to cache the output of a function. By default, no caching is done. If a str is given, it is the path to the caching directory.

• memory_level (int, default=0) – Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.

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

Variables:

• masker (NiftiMasker or similar) – Masker object.

• inputs (dict) – Inputs to the Estimator. For CBMA estimators, there is only one key: coordinates. This is an edited version of the dataset’s coordinates DataFrame.

• null_distributions (dict of numpy.ndarray) –

  Null distribution information. Entries are added to this attribute if and when the corresponding method is applied.

  Important

  The voxel-wise null distributions used by this Estimator are very large, so they are not retained as Estimator attributes.

  If fit() is applied:

  • histogram_bins: Array of bin centers for the null distribution histogram, ranging from zero to the maximum possible summary statistic value for the Dataset.

References

[1]

Robert Langner, Claudia Rottschy, Angela R Laird, Peter T Fox, and Simon B Eickhoff. Meta-analytic connectivity modeling revisited: controlling for activation base rates. Neuroimage, 99:559–570, 2014. URL: https://doi.org/10.1016/j.neuroimage.2014.06.007, doi:10.1016/j.neuroimage.2014.06.007.

Methods

correct_fwe_montecarlo()	Perform Monte Carlo-based FWE correction.
fit(dataset[, drop_invalid])	Fit Estimator to Dataset.
get_params([deep])	Get parameters for this estimator.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.

correct_fwe_montecarlo()

Perform Monte Carlo-based FWE correction.

Warning

This method is not implemented for this class.

fit(dataset, drop_invalid=True)

Fit Estimator to Dataset.

Parameters:

• 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:

Results of Estimator fitting.

Return type:

MetaResult

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)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – 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:

dict

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.

• compressed (bool, default=True) – 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(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.

Return type:

self