Statistics#
Functions for statistical analysis.
Parametric statistics (see scipy.stats and statsmodels for more
options):
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Perform one-sample t-test.  | 
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Independent samples t-test without p calculation.  | 
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Perform a 1-way ANOVA.  | 
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Compute M-way repeated measures ANOVA for fully balanced designs.  | 
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Compute F-value thresholds for a two-way ANOVA.  | 
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Fit Ordinary Least Squares (OLS) regression.  | 
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Estimate regression-based evoked potentials/fields by linear modeling.  | 
Mass-univariate multiple comparison correction:
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P-value correction with Bonferroni method.  | 
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P-value correction with False Discovery Rate (FDR).  | 
Non-parametric (clustering) resampling methods:
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Create a sparse binary adjacency/neighbors matrix.  | 
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Cluster-level statistical permutation test.  | 
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Non-parametric cluster-level paired t-test.  | 
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One sample/paired sample permutation test based on a t-statistic.  | 
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Non-parametric cluster-level test for spatio-temporal data.  | 
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Non-parametric cluster-level paired t-test for spatio-temporal data.  | 
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Assemble summary SourceEstimate from spatiotemporal cluster results.  | 
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Get confidence intervals from non-parametric bootstrap.  | 
Compute adjacency matrices for cluster-level statistics:
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Find the adjacency matrix for the given channels.  | 
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Read a channel adjacency ("neighbors") file that ships with MNE.  | 
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Compute adjacency from distances in a source space.  | 
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Compute adjacency for a source space activation.  | 
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Compute adjacency from triangles.  | 
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Get vertices on each hemisphere that are close to the other hemisphere.  | 
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Compute adjacency for a source space activation over time.  | 
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Compute adjacency from triangles and time instants.  | 
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Compute adjacency from distances in a source space and time instants.  |