nimare.meta.cbma.base
.PairwiseCBMAEstimator
- class PairwiseCBMAEstimator(kernel_transformer, memory=Memory(location=None), memory_level=0, *, mask=None, **kwargs)[source]
Bases:
CBMAEstimator
Base class for pairwise coordinate-based meta-analysis methods.
Changed in version 0.0.12:
Use a 4D sparse array for modeled activation maps.
Changed in version 0.0.8:
[REF] Use saved MA maps, when available.
Added in version 0.0.3.
- Parameters:
kernel_transformer (
KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.memory (instance of
joblib.Memory
,str
, orpathlib.Path
) – Used to cache the output of a function. By default, no caching is done. If astr
is given, it is the path to the caching directory.memory_level (
int
, default=0) – Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.*args – Optional arguments to the
Estimator
__init__ (called automatically).**kwargs – Optional keyword arguments to the
Estimator
__init__ (called automatically).
Methods
correct_fwe_montecarlo
(result[, ...])Perform FWE correction using the max-value permutation method.
fit
(dataset1, dataset2[, drop_invalid])Fit Estimator to two Datasets.
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, voxel_thresh=0.001, n_iters=5000, n_cores=1, vfwe_only=False)[source]
Perform FWE correction using the max-value permutation method.
Only call this method from within a Corrector.
Changed in version 0.0.13: Change cluster neighborhood from faces+edges to faces, to match Nilearn.
Changed in version 0.0.12:
Fix the
vfwe_only
option.
Changed in version 0.0.11:
Rename
*_level-cluster
maps to*_desc-size_level-cluster
.Add new
*_desc-mass_level-cluster
maps that use cluster mass-based inference.
- Parameters:
result (
MetaResult
) – Result object from a CBMA meta-analysis.voxel_thresh (
float
, default=0.001) – Cluster-defining p-value threshold. Default is 0.001.n_iters (
int
, default=5000) – Number of iterations to build the voxel-level, cluster-size, and cluster-mass FWE null distributions. Default is 5000.n_cores (
int
, default=1) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.vfwe_only (
bool
, default=False) – If True, only calculate the voxel-level FWE-corrected maps. Voxel-level correction can be performed very quickly if the Estimator’snull_method
was “montecarlo”. Default is False.
- Returns:
images (
dict
) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method:logp_desc-size_level-cluster
: Cluster-level FWE-corrected-log10(p)
map based on cluster size. This was previously simply called “logp_level-cluster”. This array is not generated ifvfwe_only
isTrue
.logp_desc-mass_level-cluster
: Cluster-level FWE-corrected-log10(p)
map based on cluster mass. According to Bullmore et al.[1] and Zhang et al.[2], cluster mass-based inference is more powerful than cluster size. This array is not generated ifvfwe_only
isTrue
.logp_level-voxel
: Voxel-level FWE-corrected-log10(p)
map. Voxel-level correction is generally more conservative than cluster-level correction, so it is only recommended for very large meta-analyses (i.e., hundreds of studies), per Eickhoff et al.[3].
description_ (
str
) – A text description of the correction procedure.
Notes
If
vfwe_only
isFalse
, this method adds three new keys to thenull_distributions_
attribute:values_level-voxel_corr-fwe_method-montecarlo
: The maximum summary statistic value from each Monte Carlo iteration. An array of shape (n_iters,).values_desc-size_level-cluster_corr-fwe_method-montecarlo
: The maximum cluster size from each Monte Carlo iteration. An array of shape (n_iters,).values_desc-mass_level-cluster_corr-fwe_method-montecarlo
: The maximum cluster mass from each Monte Carlo iteration. An array of shape (n_iters,).
See also
nimare.correct.FWECorrector
The Corrector from which to call this method.
References
Examples
>>> meta = MKDADensity() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', voxel_thresh=0.01, n_iters=5, n_cores=1) >>> cresult = corrector.transform(result)
- fit(dataset1, dataset2, drop_invalid=True)[source]
Fit Estimator to two Datasets.
- Parameters:
dataset1/dataset2 (
Dataset
) – Dataset objects to analyze.- Returns:
Results of Estimator fitting.
- Return type:
- 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
Examples using nimare.meta.cbma.base.PairwiseCBMAEstimator
Coordinate-based meta-analysis algorithms