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 and varcope 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