nimare.meta.cbma.mkda
.KDA¶
-
class
KDA
(kernel_estimator=<class 'nimare.meta.cbma.kernel.KDAKernel'>, **kwargs)[source]¶ Kernel density analysis.
Parameters: - kernel_estimator (
nimare.meta.cbma.base.KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is KDAKernel. - **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.
Notes
Kernel density analysis was first introduced in [R258c842da77f-1] and [R258c842da77f-2].
References
[R258c842da77f-1] Wager, Tor D., et al. “Valence, gender, and lateralization of functional brain anatomy in emotion: a meta-analysis of findings from neuroimaging.” Neuroimage 19.3 (2003): 513-531. https://doi.org/10.1016/S1053-8119(03)00078-8 [R258c842da77f-2] Wager, Tor D., John Jonides, and Susan Reading. “Neuroimaging studies of shifting attention: a meta-analysis.” Neuroimage 22.4 (2004): 1679-1693. https://doi.org/10.1016/j.neuroimage.2004.03.052 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.base.base.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
- kernel_estimator (