nimare.meta.cbma.base
.PairwiseCBMAEstimator¶
-
class
PairwiseCBMAEstimator
(kernel_transformer, *args, **kwargs)[source]¶ Bases:
nimare.meta.cbma.base.CBMAEstimator
Base class for pairwise coordinate-based meta-analysis methods.
- Parameters
kernel_transformer (
nimare.base.KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.*args – Optional arguments to the
nimare.base.MetaEstimator
__init__ (called automatically).**kwargs – Optional keyword arguments to the
nimare.base.MetaEstimator
__init__ (called automatically).
-
compute_summarystat
(data)[source]¶ Compute OF scores from data.
- Parameters
data (array, pandas.DataFrame, or list of img_like) – Data from which to estimate ALE scores. The data can be: (1) a 1d contrast-len or 2d contrast-by-voxel array of MA values, (2) a DataFrame containing coordinates to produce MA values, or (3) a list of imgs containing MA values.
- Returns
stat_values (1d array) – OF values. One value per voxel.
-
correct_fwe_montecarlo
(result, voxel_thresh=0.001, n_iters=10000, n_cores=- 1)[source]¶ Perform FWE correction using the max-value permutation method.
Only call this method from within a Corrector.
- Parameters
result (
nimare.results.MetaResult
) – Result object from a KDA meta-analysis.voxel_thresh (
float
, optional) – Cluster-defining p-value threshold. Default is 0.001.n_iters (
int
, optional) – Number of iterations to build the vFWE and cFWE null distributions. 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: ‘vthresh’, ‘logp_level-cluster’, and ‘logp_level-voxel’.
See also
nimare.correct.FWECorrector
The Corrector from which to call this method.
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)[source]¶ Fit Estimator to two Datasets.
- Parameters
dataset1/dataset2 (
nimare.dataset.Dataset
) – Dataset objects to analyze.- Returns
nimare.results.MetaResult
– Results of Estimator fitting.
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
.