nimare.meta.ibma
.Hedges
- class Hedges(*, mask=None, resample=False, memory_limit=None, **kwargs)[source]
Bases:
IBMAEstimator
Hedges meta-regression estimator.
Changed in version 0.0.12:
Add “se” to outputs.
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 and Olkin1 approach.
Notes
Requires beta and varcope images.
fit()
produces aMetaResult
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.
“tau2”
Estimated between-study variance.
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
- 1
Larry V Hedges and Ingram Olkin. Statistical methods for meta-analysis. Academic press, 2014.
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
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