nimare.meta.ibma
.PermutedOLS
- class PermutedOLS(two_sided=True, **kwargs)[source]
Bases:
IBMAEstimator
An analysis with permuted ordinary least squares (OLS), using nilearn.
Changed in version 0.0.12:
Use beta maps instead of z maps.
Changed in version 0.0.8:
[FIX] Remove single-dimensional entries of each array of returns (
dict
).
New in version 0.0.4.
This approach is described in Freedman and Lane[1].
- 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
beta
images.fit()
produces aMetaResult
object with the following maps:“t”
T-statistic map from one-sample test.
“z”
Z-statistic map from one-sample test.
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
See also
nilearn.mass_univariate.permuted_ols
The function used for this IBMA.
Methods
correct_fwe_montecarlo
(result[, n_iters, ...])Perform FWE correction using the max-value permutation method.
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.
- 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 – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘p_level-voxel’, ‘z_level-voxel’, ‘logp_level-voxel’.
- Return type:
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:
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