nimare.meta.ibma
.Hedges
- class Hedges(*args, **kwargs)[source]
Bases:
nimare.base.MetaEstimator
Hedges 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 Hedges & Olkin (1985) approach.
Notes
Requires
beta
andvarcope
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
Hedges LV, Olkin I. 1985. Statistical Methods for Meta-Analysis.
See also
pymare.estimators.Hedges
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)[source]
Fit Estimator to Dataset.
- Parameters
- Returns
Results of Estimator fitting.
- Return type
- Variables
~Estimator.fit.inputs_ (
dict
) – Inputs used in _fit.
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters
- Returns
obj – Loaded class object.
- Return type
class object
- 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.- Return type
self