nimare.meta.ibma
.VarianceBasedLikelihood¶
-
class
VarianceBasedLikelihood
(method='ml', *args, **kwargs)[source]¶ Bases:
nimare.base.MetaEstimator
A likelihood-based meta-analysis method for estimates with known variances.
Iteratively estimates the between-subject variance tau^2 and fixed effect coefficients using the specified likelihood-based estimator (ML or REML).
- Parameters
method ({‘ml’, ‘reml’}, optional) – The estimation method to use. Either ‘ml’ (for maximum-likelihood) or ‘reml’ (restricted maximum-likelihood). Default is ‘ml’.
Notes
Requires
beta
andvarcope
images.The ML and REML solutions are obtained via SciPy’s scalar function minimizer (
scipy.optimize.minimize()
). Parameters tominimize()
can be passed in as keyword arguments.Warning
Likelihood-based estimators are not parallelized across voxels, so this method should not be used on full brains, unless you can submit your code to a job scheduler.
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
DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled clinical trials, 7(3), 177-188.
Kosmidis, I., Guolo, A., & Varin, C. (2017). Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Biometrika, 104(2), 489–496. https://doi.org/10.1093/biomet/asx001
See also
pymare.estimators.VarianceBasedLikelihoodEstimator
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 callfit
.