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. The available options are
”ml” (default)
Maximum likelihood
”reml”
Restricted maximum likelihood
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.
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