labs.statistical_mapping¶
Module: labs.statistical_mapping
¶
Inheritance diagram for nipy.labs.statistical_mapping
:
Class¶
LinearModel
¶

class
nipy.labs.statistical_mapping.
LinearModel
(data, design_matrix, mask=None, formula=None, model='spherical', method=None, niter=2)¶ Bases:
object

__init__
(data, design_matrix, mask=None, formula=None, model='spherical', method=None, niter=2)¶

contrast
(vector)¶ Compute images of contrast and contrast variance.

def_model
= 'spherical'¶

def_niter
= 2¶

dump
(filename)¶ Dump GLM fit as npz file.

Functions¶

nipy.labs.statistical_mapping.
bonferroni
(p, n)¶

nipy.labs.statistical_mapping.
cluster_stats
(zimg, mask, height_th, height_control='fpr', cluster_th=0, nulls={})¶ 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.
Parameters: zimg: zscore image
mask: mask image
height_th: cluster forming threshold
height_control: string
false positive control meaning of cluster forming threshold: ‘fpr’’fdr’’bonferroni’’none’
cluster_th: cluster size threshold
null_s : clusterlevel calibration method: None’rft’array
Notes
This works only with three dimensional data

nipy.labs.statistical_mapping.
get_3d_peaks
(image, mask=None, threshold=0.0, nn=18, order_th=0)¶ returns all the peaks of image that are with the mask and above the provided threshold
Parameters: image, (3d) test image
mask=None, (3d) mask image
By default no masking is performed
threshold=0., float, threshold value above which peaks are considered
nn=18, int, number of neighbours of the topological spatial model
order_th=0, int, threshold on topological order to validate the peaks
Returns: peaks, a list of dictionaries, where each dict has the fields:
vals, map value at the peak
order, topological order of the peak
ijk, array of shape (1,3) grid coordinate of the peak
pos, array of shape (n_maxima,3) mm coordinates (mapped by affine)
of the peaks

nipy.labs.statistical_mapping.
linear_model_fit
(data_images, mask_images, design_matrix, vector)¶ Helper function for group data analysis using arbitrary design matrix

nipy.labs.statistical_mapping.
onesample_test
(data_images, vardata_images, mask_images, stat_id, permutations=0, cluster_forming_th=0.01)¶ Helper function for permutationbased mass univariate onesample group analysis.

nipy.labs.statistical_mapping.
prepare_arrays
(data_images, vardata_images, mask_images)¶

nipy.labs.statistical_mapping.
simulated_pvalue
(t, simu_t)¶

nipy.labs.statistical_mapping.
twosample_test
(data_images, vardata_images, mask_images, labels, stat_id, permutations=0, cluster_forming_th=0.01)¶ Helper function for permutationbased mass univariate twosample group analysis. Labels is a binary vector (12). Regions more active for group 1 than group 2 are inferred.