frites.stats.cluster_correction_mcp#
- frites.stats.cluster_correction_mcp(x, x_p, th, tail=1, **kwargs)[source]#
Cluster-based correction for MCP using non-parametric statistics.
This function can be used to compute the p-values, corrected for multiple comparisons using cluster-based. Note that this function detect clusters for each ROI but can be performed across multiple other dimensions (e.g frequencies, times etc.).
- Parameters
- xnumpy:array_like
Array of true effect size of shape (n_roi, …)
- x_pnumpy:array_like
Array of permutations of shape (n_perm, n_roi, …)
- th
python:float The threshold to use
- 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
- kwargs
python:dict| {} Additional arguments are send to the
mne.stats.cluster_level._find_clusters()
- Returns
- pvaluesnumpy:array_like
Array of p-values of shape (n_roi, n_times)