nimare.meta.ibma
.Stouffers
- class Stouffers(use_sample_size=False, *args, **kwargs)[source]
Bases:
nimare.base.MetaEstimator
A t-test on z-statistic images.
Requires z-statistic images.
- Parameters
use_sample_size (
bool
, optional) – Whether to use sample sizes for weights (i.e., “weighted Stouffer’s”) or not. Default is False.
Notes
Requires
z
images and optionally the sample size metadata field.Warning
Masking approaches which average across voxels (e.g., NiftiLabelsMaskers) will result in invalid results. It cannot be used with these types of maskers.
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
Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A., & Williams Jr, R. M. (1949). The American Soldier: Adjustment during army life. Studies in social psychology in World War II, vol. 1. https://psycnet.apa.org/record/1950-00790-000
Zaykin, D. V. (2011). Optimally weighted Z‐test is a powerful method for combining probabilities in meta‐analysis. Journal of evolutionary biology, 24(8), 1836-1841. https://doi.org/10.1111/j.1420-9101.2011.02297.x
See also
pymare.estimators.StoufferCombinationTest
The PyMARE estimator called by this class.
- fit(dataset, drop_invalid=True)[source]
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 callfit
.