fig = moabb_plt.paired_plot(results, "CSP+LDA", "RG+LDA") plt.show() ############################################################################### # Statistical testing and further plots # ---------------------------------------- # # If the statistical significance of results is of interest, the method # compute_dataset_statistics allows one to show a meta-analysis style plot as # well. For an overview of how all algorithms perform in comparison with each # other, the method find_significant_differences and the summary_plot are # possible. stats = compute_dataset_statistics(results) P, T = find_significant_differences(stats) ############################################################################### # The meta-analysis style plot shows the standardized mean difference within # each tested dataset for the two algorithms in question, in addition to a # meta-effect and significances both per-dataset and overall. fig = moabb_plt.meta_analysis_plot(stats, "CSP+LDA", "RG+LDA") plt.show() ############################################################################### # The summary plot shows the effect and significance related to the hypothesis # that the algorithm on the y-axis significantly out-performed the algorithm on # the x-axis over all datasets moabb_plt.summary_plot(P, T) plt.show()
fig = moabb_plt.paired_plot(results, 'CSP + LDA', 'RG + LDA') plt.show() ############################################################################### # Statistical testing and further plots # ---------------------------------------- # # If the statistical significance of results is of interest, the method # compute_dataset_statistics allows one to show a meta-analysis style plot as # well. For an overview of how all algorithms perform in comparison with each # other, the method find_significant_differences and the summary_plot are # possible. stats = compute_dataset_statistics(results) P, T = find_significant_differences(stats) ############################################################################### # The meta-analysis style plot shows the standardized mean difference within # each tested dataset for the two algorithms in question, in addition to a # meta-effect and significances both per-dataset and overall. fig = moabb_plt.meta_analysis_plot(stats, 'CSP + LDA', 'RG + LDA') plt.show() ############################################################################### # The summary plot shows the effect and significance related to the hypothesis # that the algorithm on the y-axis significantly out-performed the algorithm on # the x-axis over all datasets moabb_plt.summary_plot(P, T) plt.show()