nimare.meta.ibma.WeightedLeastSquares

class WeightedLeastSquares(tau2=0, *args, **kwargs)[source]

Bases: nimare.base.MetaEstimator

Weighted least-squares meta-regression.

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

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)[source]

Fit Estimator to Dataset.

Parameters

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

Returns

nimare.results.MetaResult – Results of Estimator fitting.

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)[source]

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)[source]

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)[source]

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)[source]

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