Inheritance diagram for nipy.neurospin.group.permutation_test:
One and two sample permutation tests.
This generic permutation test class contains the calibration method which is common to the derived classes permutation_test_onesample and permutation_test_twosample
Calibrate cluster and region summary statistics using permutation test
Parameters: | nperms : int, optional
clusters : list [(thresh1,diam1),(thresh2,diam2),...], optional
cluster_stats : list [stat1,...], optional
regions : list [Labels1,Labels2,...]
region_stats : list [stat1,...], optional
verbose : boolean, optional
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Returns: | voxel_results : dict
cluster_results : list [results1,results2,...]
region_results :list [results1,results2,...] :
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Bases: nipy.neurospin.group.permutation_test.permutation_test
Class derived from the generic permutation_test class. Inherits the calibrate method
Initialize permutation_test_onesample instance, compute statistic values in each voxel and under permutation In: data data array
- XYZ voxels coordinates
- axis <int> Subject axis in data
- vardata variance (same shape as data)
- optional (if None, mfx statistics cannot be used)
- stat_id <char> choice of test statistic
- (see onesample.stats for a list of possible stats)
base <float> mean signal under H0 niter <int> number of iterations of EM algorithm ndraws <int> Number of generated random t values
self.Tvalues voxelwise test statistic values self.random_Tvalues sorted statistic values in random voxels and under random
sign permutation
Bases: nipy.neurospin.group.permutation_test.permutation_test
Class derived from the generic permutation_test class. Inherits the calibrate method
Initialize permutation_test_onesample instance, compute statistic values in each voxel and under permutation In: data data array
G weighted graph (each vertex corresponds to a voxel) axis <int> Subject axis in data vardata variance (same shape as data)
optional (if None, mfx statistics cannot be used)
- stat_id <char> choice of test statistic
- (see onesample.stats for a list of possible stats)
base <float> mean signal under H0 niter <int> number of iterations of EM algorithm ndraws <int> Number of generated random t values
self.Tvalues voxelwise test statistic values self.random_Tvalues sorted statistic values in random voxels and under random
sign permutation
Bases: nipy.neurospin.group.permutation_test.permutation_test
Class derived from the generic permutation_test class. Inherits the calibrate method
Initialize permutation_test_twosample instance, compute statistic values in each voxel and under permutation In: data1, data2 data arrays
- XYZ voxels coordinates
- axis <int> Subject axis in data
- vardata1, vardata2 variance (same shape as data)
- optional (if None, mfx statistics cannot be used)
- stat_id <char> choice of test statistic
- (see onesample.stats for a list of possible stats)
niter <int> number of iterations of EM algorithm ndraws <int> Number of generated random t values
self.Tvalues voxelwise test statistic values self.random_Tvalues sorted statistic values in random voxels and under random
sign permutation
size_values, Fisher_values = compute_cluster_stats(Tvalues, labels, random_Tvalues, cluster_stats=[“size”,”Fisher”]) Compute summary statistics in each cluster In: see permutation_test_onesample class docstring Out: size_values Array of size nclust, or None if “size” not in cluster_stats
Fisher_values Array of size nclust, or None if “Fisher” not in cluster_stats
Extract clusters from a statistical map under diameter constraint and above given threshold In: T (p) statistical map
XYZ (3,p) voxels coordinates th <float> minimum threshold diam <int> maximal diameter (in voxels) k <int> the number of neighbours considered. (6,18 or 26)
Out: labels (p) cluster labels
Extract clusters from statistical map above specified threshold In: T (p) statistical map
XYZ (3,p) voxels coordinates th <float> threshold k <int> the number of neighbours considered. (6,18 or 26)
Out: labels (p) cluster labels
Maximum distance between two set of points In: XYZ (3,p) voxels coordinates
I (q) index of points J (r) index of points
Out: d <float>