nimare.meta.ibma
.VarianceBasedLikelihood
- class VarianceBasedLikelihood(method='ml', **kwargs)[source]
Bases:
IBMAEstimator
A likelihood-based meta-analysis method for estimates with known variances.
Changed in version 0.0.12: Add “se” output.
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 coefficients using the specified likelihood-based estimator (ML or REML) 12.
- 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
andvarcope
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.
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.
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
Rebecca DerSimonian and Nan Laird. Meta-analysis in clinical trials. Controlled clinical trials, 7(3):177–188, 1986.
- 2
Ioannis Kosmidis, Annamaria Guolo, and Cristiano Varin. Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Biometrika, 104(2):489–496, 2017. URL: https://doi.org/10.1093/biomet/asx001, doi:10.1093/biomet/asx001.
See also
pymare.estimators.VarianceBasedLikelihoodEstimator
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