nimare.meta.cbma.model.SBR

class SBR[source]

Spatial binary regression model [Rbf3e8ffed16f-1].

Warning

This method is not yet implemented.

References

[Rbf3e8ffed16f-1]Yue, Yu Ryan, Martin A. Lindquist, and Ji Meng Loh. “Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression.” The Annals of Applied Statistics 6.2 (2012): 697-718. https://doi.org/10.1214/11-AOAS523

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