nimare.meta.ibma.WeightedLeastSquares

class WeightedLeastSquares(tau2=0, *args, **kwargs)

Bases: nimare.base.MetaEstimator

Weighted least-squares meta-regression.

Changed in version 0.0.8:

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

New in version 0.0.4.

Provides the weighted least-squares estimate of the fixed effects given known/assumed between-study variance tau^2. When tau^2 = 0 (default), the model is the standard inverse-weighted fixed-effects meta-regression.

Parameters

tau2 (float or 1D numpy.ndarray, optional) – Assumed/known value of tau^2. Must be >= 0. Default is 0.

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

• Brockwell, S. E., & Gordon, I. R. (2001). A comparison of statistical methods for meta-analysis. Statistics in Medicine, 20(6), 825–840. https://doi.org/10.1002/sim.650

See also

pymare.estimators.WeightedLeastSquares

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.WeightedLeastSquares

Run image-based meta-analyses on 21 pain studies