nimare.meta.ibma
.SampleSizeBasedLikelihood
- class SampleSizeBasedLikelihood(method='ml', *args, **kwargs)[source]
Bases:
nimare.base.MetaEstimator
Method estimates with known sample sizes but unknown sampling variances.
Changed in version 0.0.8:
[FIX] Remove single-dimensional entries of each array of returns (
dict
).
New in version 0.0.4.
Iteratively estimates the between-subject variance tau^2 and fixed effect betas 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
images and sample size from metadata.Homogeneity of sigma^2 across studies is assumed. 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.
See also
pymare.estimators.SampleSizeBasedLikelihoodEstimator
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
.