nimare.meta.cbma.mkda

CBMA methods from the multilevel kernel density analysis (MKDA) family

Classes

KDA([kernel_estimator])	Kernel density analysis.
MKDAChi2([prior, kernel_estimator])	Multilevel kernel density analysis- Chi-square analysis [Rb50f9c63f995-1].
MKDADensity([kernel_estimator])	Multilevel kernel density analysis- Density analysis [R8ac5f6c30bba-1].

class KDA(kernel_estimator=<class 'nimare.meta.cbma.kernel.KDAKernel'>, **kwargs)

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)

Fit Estimator to Dataset.

Parameters:dataset (nimare.dataset.Dataset) – Dataset object to analyze. Returns:Results of Estimator fitting. Return type:nimare.base.base.MetaResult

get_params(self, deep=True)

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)

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)

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)

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

class MKDAChi2(prior=0.5, kernel_estimator=<class 'nimare.meta.cbma.kernel.MKDAKernel'>, **kwargs)

Multilevel kernel density analysis- Chi-square analysis [Rb50f9c63f995-1].

Parameters:

• prior (float, optional) – Uniform prior probability of each feature being active in a map in the absence of evidence from the map. Default: 0.5
• kernel_estimator (nimare.meta.cbma.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is MKDAKernel.
• **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.

References

[Rb50f9c63f995-1]

(1, 2) Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015

Methods

fit(self, dataset, dataset2)	Fit Estimator to datasets.
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, dataset2)

Fit Estimator to datasets.

Parameters:dataset2 (dataset,) – Dataset objects to analyze. Returns:Results of Estimator fitting. Return type:nimare.base.base.MetaResult

get_params(self, deep=True)

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)

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)

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)

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

class MKDADensity(kernel_estimator=<class 'nimare.meta.cbma.kernel.MKDAKernel'>, **kwargs)

Multilevel kernel density analysis- Density analysis [R8ac5f6c30bba-1].

Parameters:

• kernel_estimator (nimare.meta.cbma.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is MKDAKernel.
• **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.

References

[R8ac5f6c30bba-1]

(1, 2) Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015

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)

Fit Estimator to Dataset.

Parameters:dataset (nimare.dataset.Dataset) – Dataset object to analyze. Returns:Results of Estimator fitting. Return type:nimare.base.base.MetaResult

get_params(self, deep=True)

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)

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)

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)

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