# Statistics#

Functions for statistical analysis.

Parametric statistics (see `scipy.stats` and `statsmodels` for more options):

 `ttest_1samp_no_p`(X[, sigma, method]) Perform one-sample t-test. `ttest_ind_no_p`(a, b[, equal_var, sigma]) Independent samples t-test without p calculation. `f_oneway`(*args) Perform a 1-way ANOVA. `f_mway_rm`(data, factor_levels[, effects, ...]) Compute M-way repeated measures ANOVA for fully balanced designs. `f_threshold_mway_rm`(n_subjects, factor_levels) Compute F-value thresholds for a two-way ANOVA. `linear_regression`(inst, design_matrix[, names]) Fit Ordinary Least Squares (OLS) regression. `linear_regression_raw`(raw, events[, ...]) Estimate regression-based evoked potentials/fields by linear modeling.

Mass-univariate multiple comparison correction:

 `bonferroni_correction`(pval[, alpha]) P-value correction with Bonferroni method. `fdr_correction`(pvals[, alpha, method]) P-value correction with False Discovery Rate (FDR).

Non-parametric (clustering) resampling methods:

 `combine_adjacency`(*structure) Create a sparse binary adjacency/neighbors matrix. `permutation_cluster_test`(X[, threshold, ...]) Cluster-level statistical permutation test. `permutation_cluster_1samp_test`(X[, ...]) Non-parametric cluster-level paired t-test. `permutation_t_test`(X[, n_permutations, ...]) One sample/paired sample permutation test based on a t-statistic. `spatio_temporal_cluster_test`(X[, threshold, ...]) Non-parametric cluster-level test for spatio-temporal data. Non-parametric cluster-level paired t-test for spatio-temporal data. `summarize_clusters_stc`(clu[, p_thresh, ...]) Assemble summary SourceEstimate from spatiotemporal cluster results. `bootstrap_confidence_interval`(arr[, ci, ...]) Get confidence intervals from non-parametric bootstrap.

Compute `adjacency` matrices for cluster-level statistics:

 `channels.find_ch_adjacency`(info, ch_type) Find the adjacency matrix for the given channels. `channels.read_ch_adjacency`(fname[, picks]) Parse FieldTrip neighbors .mat file. `spatial_dist_adjacency`(src, dist[, verbose]) Compute adjacency from distances in a source space. `spatial_src_adjacency`(src[, dist, verbose]) Compute adjacency for a source space activation. `spatial_tris_adjacency`(tris[, ...]) Compute adjacency from triangles. `spatial_inter_hemi_adjacency`(src, dist[, ...]) Get vertices on each hemisphere that are close to the other hemisphere. `spatio_temporal_src_adjacency`(src, n_times) Compute adjacency for a source space activation over time. `spatio_temporal_tris_adjacency`(tris, n_times) Compute adjacency from triangles and time instants. `spatio_temporal_dist_adjacency`(src, n_times, ...) Compute adjacency from distances in a source space and time instants.