frites.workflow.WfStats#

class frites.workflow.WfStats(verbose=None)[source]#

Workflow of non-parametric statistics for electropĥysiological data.

The goal of this workflow is to provide an interface for assessing non- parametric statistics on electrophysiological data based on anatomical informations (ROI = Region Of Interest). In particular, it allows to make inferences either about a specific population (FFX = fixed effect) or build a model of a random population (RFX = Random effect). In addition, significant effect can either be detected at the spatio-temporal level (i.e at each time point and for each ROI) either at the cluster-level (i.e temporal clusters).

Attributes
cluster_th

Cluster forming threshold.

Methods

fit(effect, perms[, inference, mcp, tail, ...])

Fit the workflow on true data.

clean
property cluster_th#

Cluster forming threshold.

fit(effect, perms, inference='rfx', mcp='cluster', tail=1, cluster_th=None, cluster_alpha=0.05, ttested=False, rfx_sigma=0.001, rfx_center=False)[source]#

Fit the workflow on true data.

Parameters
effectpython:list

True effect list of length (n_roi,) composed of arrays each one of shape (n_subjects, …, n_times). Number of subjects per ROI could be different

permspython:list

Permutation list of length (n_roi,) composed of arrays each one of shape (n_perm, n_subjects, …, n_times). Number of subjects per ROI could be different

inference{‘ffx’, ‘rfx’}

Perform either Fixed-effect (‘ffx’) or Random-effect (‘rfx’) inferences. By default, random-effect is used

mcp{‘cluster’, ‘maxstat’, ‘fdr’, ‘bonferroni’, ‘nostat’, python:None}

Method to use for correcting p-values for the multiple comparison problem. Use either :

  • ‘cluster’ : cluster-based statistics [default]

  • ‘maxstat’ : test-wise maximum statistics correction

  • ‘fdr’ : test-wise FDR correction

  • ‘bonferroni’ : test-wise Bonferroni correction

  • ‘nostat’ : permutations are computed but no statistics are performed

  • ‘noperm’ / None : no permutations are computed

tail{-1, 0, 1}

Type of comparison. Use -1 for the lower part of the distribution, 1 for the higher part and 0 for both. By default, upper tail of the distribution is used

cluster_thpython:str, python:float | python:None

The threshold to use for forming clusters. Use either :

  • a float that is going to act as a threshold

  • None and the threshold is automatically going to be inferred using the distribution of permutations

  • ‘tfce’ : for Threshold Free Cluster Enhancement

cluster_alphapython:float | 0.05

Control the percentile to use for forming the clusters. By default the 95th percentile of the permutations is used.

ttestedbool | python:False

Specify if the inputs have already been t-tested

rfx_sigmapython:float | 0.001

Hat adjustment method, a value of 1e-3 may be a reasonable choice

rfx_center{python:False, ‘mean’, ‘median’, ‘trimmed’, ‘zscore’}

Re-center the time-series of effect arround 0 before computing the t-test. This parameters can be useful in case of a different number of data per brain region.

Returns
pvaluesnumpy:array_like

Array of p-values of shape (…, n_times, n_roi)

tvaluesnumpy:array_like

Array of t-values of shape (…, n_times, n_roi). This ouput is only computed for group-level analysis

References

Smith and Nichols, 2009 [18]