nimare.meta.ibma.VarianceBasedLikelihood

class VarianceBasedLikelihood(method='ml', **kwargs)

Bases: IBMAEstimator

A likelihood-based meta-analysis method for estimates with known variances.

Changed in version 0.2.1:

• New parameter: aggressive_mask, to control whether to use an aggressive mask.

Changed in version 0.0.12: Add “se” output.

Changed in version 0.0.8:

• [FIX] Remove single-dimensional entries of each array of returns (dict).

Added in version 0.0.4.

Iteratively estimates the between-subject variance tau^2 and fixed effect coefficients using the specified likelihood-based estimator (ML or REML) [1][2].

Parameters:

• aggressive_mask (bool, optional) – Voxels with a value of zero of NaN in any of the input maps will be removed from the analysis. If False, all voxels are included by running a separate analysis on bags of voxels that belong that have a valid value across the same studies. Default is True.

• method ({'ml', 'reml'}, optional) –

  The estimation method to use. The available options are

  ”ml” (default)	Maximum likelihood
  ”reml”	Restricted maximum likelihood

Notes

Requires beta and varcope images.

fit() produces a MetaResult object with the following maps:

“z”	Z-statistic map from one-sample test.
“p”	P-value map from one-sample test.
“est”	Fixed effects estimate for intercept test.
“se”	Standard error of fixed effects estimate.
“tau2”	Estimated between-study variance.
“dof”	Degrees of freedom map from one-sample test.

The ML and REML solutions are obtained via SciPy’s scalar function minimizer (scipy.optimize.minimize()). Parameters to minimize() can be passed in as keyword arguments.

Warning

Likelihood-based estimators are not parallelized across voxels, so this method should not be used on full brains, unless you can submit your code to a job scheduler.

Masking approaches which average across voxels (e.g., NiftiLabelsMaskers) will likely result in biased results. The extent of this bias is currently unknown.

By default, all image-based meta-analysis estimators adopt an aggressive masking strategy, in which any voxels with a value of zero in any of the input maps will be removed from the analysis. Setting aggressive_mask=False will instead run tha analysis in bags of voxels that have a valid value across the same studies.

References

[1]

Rebecca DerSimonian and Nan Laird. Meta-analysis in clinical trials. Controlled clinical trials, 7(3):177–188, 1986.

[2]

Ioannis Kosmidis, Annamaria Guolo, and Cristiano Varin. Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Biometrika, 104(2):489–496, 2017. URL: https://doi.org/10.1093/biomet/asx001, doi:10.1093/biomet/asx001.

See also

pymare.estimators.VarianceBasedLikelihoodEstimator

The PyMARE estimator called by this class.

Methods

fit(dataset[, drop_invalid])	Fit Estimator to a collection.
get_params([deep])	Get parameters for this estimator.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.

fit(dataset, drop_invalid=True)

Fit Estimator to a collection.

Parameters:

• dataset (Studyset or Dataset) – Collection object to analyze.

• drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is True.

Returns:

Result of Estimator fitting. Subclasses may return a MetaResult subclass.

Return type:

MetaResult

Variables:

• inputs (dict) – Inputs used in _fit.

• warning:: (..) – Support for Dataset inputs is deprecated and will be removed in a future release. Prefer Studyset.

• and (The fit method is a light wrapper that runs input validation)

• individual (preprocessing before fitting the actual model. Estimators')

• should ("fitting" methods are implemented as _fit, although users)

• fit. (call)

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – 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:

dict

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.

• compressed (bool, default=True) – 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(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(**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.

Return type:

self