nimare.meta.ibma.FixedEffectsHedges

class FixedEffectsHedges(tau2=0, **kwargs)

Bases: IBMAEstimator

Fixed Effects Hedges meta-regression estimator.

Added in version 0.4.0.

Provides the weighted least-squares estimate of the fixed effects using Hedge’s g as the point estimate and the variance of bias-corrected Cohen’s d as the variance estimate, and given known/assumed between-study variance tau^2. When tau^2 = 0 (default), the model is the standard inverse-weighted fixed-effects meta-regression.

This method was described in Bossier et al.[1].

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.

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

Notes

Requires t images and sample size from metadata.

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.
“dof”	Degrees of freedom map from one-sample test.

Warning

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]

Han Bossier, Thomas E. Nichols, and Beatrijs Moerkerke. Standardized effect sizes and image-based meta-analytical approaches for fmri data. bioRxiv, 2019. doi:10.1101/865881.

See also

pymare.estimators.WeightedLeastSquares

The PyMARE estimator called by this class.

Methods

fit(dataset[, drop_invalid])	Fit Estimator to Dataset.
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 Dataset.

Parameters:

• 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:

Results of Estimator fitting.

Return type:

MetaResult

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, 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

Examples using nimare.meta.ibma.FixedEffectsHedges

Image-based meta-analysis algorithms

Image-based meta-analysis algorithms