nimare.meta.ibma.DerSimonianLaird

class DerSimonianLaird(*args, **kwargs)

Bases: nimare.base.MetaEstimator

DerSimonian-Laird meta-regression estimator.

Changed in version 0.0.8:

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

New in version 0.0.4.

Estimates the between-subject variance tau^2 using the DerSimonian-Laird (1986) method-of-moments approach.

Notes

Requires beta and varcope images.

Warning

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

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.

References

• DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled clinical trials, 7(3), 177-188.

• Kosmidis, I., Guolo, A., & Varin, C. (2017). Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Biometrika, 104(2), 489–496. https://doi.org/10.1093/biomet/asx001

See also

pymare.estimators.DerSimonianLaird

The PyMARE estimator called by this class.

fit(dataset, drop_invalid=True)

Fit Estimator to Dataset.

Parameters

• dataset (nimare.dataset.Dataset) – Dataset object to analyze.

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

Returns

nimare.results.MetaResult – Results of Estimator fitting.

Variables

inputs_ (dict) – Inputs used in _fit.

Notes

The fit method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Estimators’ individual “fitting” methods are implemented as _fit, although users should call fit.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params (dict) – Parameter names mapped to their values.

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 (class object) – Loaded 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.

Returns

self

Examples using nimare.meta.ibma.DerSimonianLaird

Compare image and coordinate based meta-analyses on 21 pain studies

Run image-based meta-analyses on 21 pain studies