nimare.meta.cbma.model

Model-based coordinate-based meta-analysis estimators

Classes

BHICP()	Bayesian hierarchical cluster process model [Rbb0a73000b2f-1].
HPGRF()	Hierarchical Poisson/Gamma random field model [Rb6084ac49a63-1].
SBLFR()	Spatial Bayesian latent factor regression model [R270830ed9ee2-1].
SBR()	Spatial binary regression model [Rbf3e8ffed16f-1].

class BHICP

Bayesian hierarchical cluster process model [Rbb0a73000b2f-1].

Warning

This method is not yet implemented.

References

[Rbb0a73000b2f-1]

(1, 2) Kang, Jian, et al. “Meta analysis of functional neuroimaging data via Bayesian spatial point processes.” Journal of the American Statistical Association 106.493 (2011): 124-134. https://doi.org/10.1198/jasa.2011.ap09735

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.results.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 HPGRF

Hierarchical Poisson/Gamma random field model [Rb6084ac49a63-1].

Warning

This method is not yet implemented.

References

[Rb6084ac49a63-1]

(1, 2) Kang, Jian, et al. “A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis.” The annals of applied statistics 8.3 (2014): 1800.

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.results.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 SBLFR

Spatial Bayesian latent factor regression model [R270830ed9ee2-1].

Warning

This method is not yet implemented.

References

[R270830ed9ee2-1]

(1, 2) 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)

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)

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 SBR

Spatial binary regression model [Rbf3e8ffed16f-1].

Warning

This method is not yet implemented.

References

[Rbf3e8ffed16f-1]

(1, 2) 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)

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)

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