nimare.meta.cbma.mkda.MKDAChi2¶
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class
MKDAChi2(kernel_transformer=<class 'nimare.meta.kernel.MKDAKernel'>, prior=0.5, **kwargs)[source]¶ Bases:
nimare.meta.cbma.base.PairwiseCBMAEstimatorMultilevel kernel density analysis- Chi-square analysis.
- Parameters
prior (float, optional) – Uniform prior probability of each feature being active in a map in the absence of evidence from the map. Default: 0.5
kernel_transformer (
nimare.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default isnimare.meta.kernel.MKDAKernel.**kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix ‘kernel__’ in the variable name.
Notes
Available correction methods:
MKDAChi2.correct_fwe_montecarlo(),MKDAChi2.correct_fdr_bhReferences
Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015
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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.
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correct_fdr_bh(result, alpha=0.05)[source]¶ Perform FDR correction using the Benjamini-Hochberg method.
Only call this method from within a Corrector.
- Parameters
result (
nimare.results.MetaResult) – Result object from a KDA meta-analysis.alpha (
float, optional) – Alpha. Default is 0.05.
- Returns
images (
dict) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘consistency_z_FDR’ and ‘specificity_z_FDR’.
See also
nimare.correct.FDRCorrectorThe Corrector from which to call this method.
Examples
>>> meta = MKDAChi2() >>> result = meta.fit(dset) >>> corrector = FDRCorrector(method='bh', alpha=0.05) >>> cresult = corrector.transform(result)
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correct_fwe_montecarlo(result, n_iters=5000, 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.n_iters (
int, optional) – Number of iterations to build the vFWE null distribution. Default is 5000.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: ‘p_desc-consistency_level-voxel’, ‘z_desc-consistency_level-voxel’, ‘p_desc-specificity_level-voxel’, and ‘p_desc-specificity_level-voxel’.
See also
nimare.correct.FWECorrectorThe Corrector from which to call this method.
Examples
>>> meta = MKDAChi2() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', n_iters=5, n_cores=1) >>> cresult = corrector.transform(result)
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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
fitmethod 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.