nimare.meta.cbma.model
.SBLFR¶
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class
SBLFR
[source]¶ Spatial Bayesian latent factor regression model [R270830ed9ee2-1].
Warning
This method is not yet implemented.
References
[R270830ed9ee2-1] Montagna, Silvia, et al. “Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data.” Biometrics 74.1 (2018): 342-353. https://doi.org/10.1111/biom.12713 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
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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
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classmethod
load
(filename, compressed=True)[source]¶ Load a pickled class instance from file.
Parameters: Returns: obj – Loaded class object.
Return type: class object
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save
(self, filename, compress=True)[source]¶ Pickle the class instance to the provided file.
Parameters:
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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
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