neurospin.neuro.statistical_test
Module: neurospin.neuro.statistical_test
Functions
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nipy.neurospin.neuro.statistical_test.bonferroni(p, n)
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nipy.neurospin.neuro.statistical_test.cluster_stats(zimg, mask, height_th, height_control='fpr', cluster_th=0, null_zmax='bonferroni', null_smax=None, null_s=None)
Return a list of clusters, each cluster being represented by a
dictionary. Clusters are sorted by descending size order. Within
each cluster, local maxima are sorted by descending depth order.
- Input consist of the following:
- zimg – z-score image
mask – mask image
height_th – cluster forming threshold
height_control – false positive control meaning of cluster forming threshold: ‘fpr’|’fdr’|’fwer’
size_th – cluster size threshold
null_zmax – voxel-level familywise error correction method: ‘bonferroni’|’rft’|array
null_smax – cluster-level familywise error correction method: None|‘rft’|array
null_s – cluster-level calibration method: None|‘rft’|array
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nipy.neurospin.neuro.statistical_test.mask_intersection(masks)
- Compute mask intersection
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nipy.neurospin.neuro.statistical_test.onesample_test(data_images, vardata_images, mask_images, stat_id, comparisons=False, cluster_forming_th=0.01, cluster_th=0)
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nipy.neurospin.neuro.statistical_test.prepare_arrays(data_images, vardata_images, mask_images)
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nipy.neurospin.neuro.statistical_test.simulated_pvalue(t, simu_t)
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nipy.neurospin.neuro.statistical_test.z_threshold(height_th, height_control)