nimare.meta.ibma
.PermutedOLS
- class PermutedOLS(two_sided=True, *args, **kwargs)[source]
Bases:
nimare.base.MetaEstimator
An analysis with permuted ordinary least squares (OLS), using nilearn.
Changed in version 0.0.8:
[FIX] Remove single-dimensional entries of each array of returns (
dict
).
New in version 0.0.4.
- Parameters
two_sided (
bool
, optional) – If True, performs an unsigned t-test. Both positive and negative effects are considered; the null hypothesis is that the effect is zero. If False, only positive effects are considered as relevant. The null hypothesis is that the effect is zero or negative. Default is True.
Notes
Requires
z
images.Available correction methods:
PermutedOLS.correct_fwe_montecarlo()
Warning
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
Freedman, D., & Lane, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business & Economic Statistics, 1(4), 292-298.
See also
nilearn.mass_univariate.permuted_ols
The function used for this IBMA.
- correct_fwe_montecarlo(result, n_iters=10000, n_cores=1)[source]
Perform FWE correction using the max-value permutation method.
Changed in version 0.0.8:
[FIX] Remove single-dimensional entries of each array of returns (
dict
).
New in version 0.0.4.
Only call this method from within a Corrector.
- Parameters
result (
MetaResult
) – Result object from an ALE meta-analysis.n_iters (
int
, optional) – The number of iterations to run in estimating the null distribution. Default is 10000.n_cores (
int
, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.
- Returns
images (
dict
) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘z_vthresh’, ‘p_level-voxel’, ‘z_level-voxel’, and ‘logp_level-cluster’.
See also
nimare.correct.FWECorrector
The Corrector from which to call this method.
nilearn.mass_univariate.permuted_ols
The function used for this IBMA.
Examples
>>> meta = PermutedOLS() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', n_iters=5, n_cores=1) >>> cresult = corrector.transform(result)
- fit(dataset, drop_invalid=True)[source]
Fit Estimator to Dataset.
- Parameters
- Returns
MetaResult
– Results of Estimator fitting.- Variables
~Estimator.fit.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
.