pull master changes into SWE branch

+nla/+net/+mcc/Base.m

@@ -1,6 +1,6 @@
 classdef Base
     methods (Abstract)
-        p_max = correct(obj, net_atlas, input_struct, prob)
+        [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
         correction_label = createLabel(obj, net_atlas, input_struct, prob)
     end
 end

+nla/+net/+mcc/BenjaminiHochberg.m

@@ -4,21 +4,11 @@
     end
 
     methods
-        function p_max = correct(obj, net_atlas, input_struct, prob)
-            [~, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'pdep');
+        function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
+            [is_sig_vector, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'pdep');
         end
         function correction_label = createLabel(obj, net_atlas, input_struct, prob)
-            p_max = obj.correct(net_atlas, input_struct, prob);
-            if p_max == 0
-                correction_label = sprintf('FDR_{BH} produced no significant nets');
-            else
-                format_specs = "%g/%d tests";
-                if isequal(input_struct.behavior_count, 1)
-                    format_specs = "%g/%d test";
-                end
-                correction_label = sprintf(strcat("FDR_{BH}(", format_specs, ")"), input_struct.prob_max * input_struct.behavior_count,...
-                    input_struct.behavior_count);
-            end
+            correction_label = sprintf("Benjamini-Hochberg (alpha = %g)",input_struct.prob_max);
         end
     end
 end

+nla/+net/+mcc/BenjaminiYekutieli.m

@@ -4,21 +4,11 @@
     end
 
     methods
-        function p_max = correct(obj, net_atlas, input_struct, prob)
-            [~, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'dep');
+        function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
+            [is_sig_vector, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'dep');
         end
         function correction_label = createLabel(obj, net_atlas, input_struct, prob)
-            p_max = obj.correct(net_atlas, input_struct, prob);
-            if p_max == 0
-                correction_label = sprintf('FDR_{BY} produced no significant nets');
-            else
-                format_specs = "%g/%d tests";
-                if isequal(input_struct.behavior_count, 1)
-                    format_specs = "%g/%d test";
-                end
-                correction_label = sprintf(strcat("FDR_{BY}(", format_specs, ")"), input_struct.prob_max * input_struct.behavior_count,...
-                    input_struct.behavior_count);
-            end
+            correction_label = sprintf("Benjamini-Yekutieli (alpha = %g)",input_struct.prob_max);
         end
     end
 end

+nla/+net/+mcc/Bonferroni.m

@@ -4,15 +4,18 @@
     end
 
     methods
-        function p_max = correct(obj, net_atlas, input_struct, prob)
+        function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
+
             p_max = input_struct.prob_max / net_atlas.numNetPairs();
+            is_sig_vector = prob.v < p_max;
         end
         function correction_label = createLabel(obj, net_atlas, input_struct, prob)
-            format_specs_tests = "%d tests";
+            format_specs_tests = "%d tests)";
             if isequal(input_struct.behavior_count, 1)
-                format_specs_tests = "%d test";
+                format_specs_tests = "%d test)";
             end
-            correction_label = sprintf(strcat("%g/%d net-pairs/", format_specs_tests), input_struct.prob_max * input_struct.behavior_count,...
+            p_max = input_struct.prob_max / net_atlas.numNetPairs();
+            correction_label = sprintf(strcat("P < %.2g (%.2g/%d net-pairs/", format_specs_tests), p_max, input_struct.prob_max * input_struct.behavior_count,...
                 net_atlas.numNetPairs(), input_struct.behavior_count);
         end
     end

+nla/+net/+mcc/HolmBonferroni.m

@@ -4,22 +4,16 @@
     end
 
     methods
-        function p_max = correct(obj, net_atlas, input_struct, prob)
-            [is_significant, adjusted_pvals, ~] = nla.lib.bonferroni_holm(prob.v, input_struct.prob_max);
-            p_max = max(is_significant .* adjusted_pvals);
+        function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
+            [is_sig_vector, adjusted_pvals, ~] = nla.lib.bonferroni_holm(prob.v, input_struct.prob_max);
+
+            p_max = max(is_sig_vector .* prob.v);
         end
         function correction_label = createLabel(obj, net_atlas, input_struct, prob)
-            p_max = obj.correct(net_atlas, input_struct, prob);
-            if p_max == 0
-                correction_label = sprintf('Holm-Bonferroni produced no significant networks');
-            else
-                format_specs = "%g/%d tests";
-                if isequal(input_struct.behavior_count, 1)
-                    format_specs = "%g/%d test";
-                end
-                correction_label = sprintf(strcat("Holm-Bonferroni(", format_specs, ")"), input_struct.prob_max * input_struct.behavior_count,...
-                    input_struct.behavior_count);
-            end
+            correction_label = sprintf("Holm-Bonferroni (alpha = %g)",input_struct.prob_max);
+
+            %Since p threshold is variable with this test, exclude it and
+            %just use the initial 'alpha' term used in the algorithm.
         end
     end
 end

+nla/+net/+mcc/None.m

@@ -4,15 +4,16 @@
     end
 
     methods
-        function p_max = correct(obj, net_atlas, input_struct, prob)
+        function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)            
             p_max = input_struct.prob_max;
+            is_sig_vector = prob.v < p_max;
         end
         function correction_label = createLabel(obj, net_atlas, input_struct, prob)
-            format_specs = "%g/%d tests";
+            format_specs = "P < %.2g (%g/%d tests)";
             if isequal(input_struct.behavior_count, 1)
-                format_specs = "%g/%d test";
+                format_specs = "P < %.2g (%g/%d test)";
             end
-            correction_label = sprintf(format_specs, input_struct.prob_max * input_struct.behavior_count,...
+            correction_label = sprintf(format_specs, input_struct.prob_max, input_struct.prob_max * input_struct.behavior_count,...
                 input_struct.behavior_count);
         end
     end

+nla/+net/+result/+plot/NetworkTestPlot.m

@@ -89,8 +89,8 @@ function getPlotTitle(obj)
                 obj.title = sprintf("%s (D > %g)", obj.title, obj.network_test_options.d_max);
             end
             if ~isequal(obj.test_method, "no_permutations")
-                if isequal(obj.current_settings.ranking, nla.RankingMethod.WINKLER) 
-                    obj.title = strcat(obj.title, "\nRanking by Winkler Method");
+                if isequal(obj.current_settings.ranking, nla.RankingMethod.FREEDMAN_LANE) 
+                    obj.title = strcat(obj.title, "\nRanking by Freedman-Lane Method");
                 elseif isequal(obj.current_settings.ranking, nla.RankingMethod.WESTFALL_YOUNG)
                     obj.title = strcat(obj.title, "\nRanking by Westfall-Young Method");
                 else
@@ -228,8 +228,8 @@ function resizeFigure(obj, plot_width, plot_height)
             % All the options (buttons, pulldowns, checkboxes)
             scale_option = PullDown("plot_scale", "Plot Scale", ["Linear", "Log", "Negative Log10"],...
                 [ProbPlotMethod.DEFAULT, ProbPlotMethod.LOG, ProbPlotMethod.NEGATIVE_LOG_10]);
-            ranking_method = PullDown("ranking", "Ranking", ["Uncorrected", "Winkler", "Westfall-Young"],...
-                [RankingMethod.UNCORRECTED, RankingMethod.WINKLER, RankingMethod.WESTFALL_YOUNG]);
+            ranking_method = PullDown("ranking", "Ranking", ["Uncorrected", "Freedman-Lane", "Westfall-Young"],...
+                [RankingMethod.UNCORRECTED, RankingMethod.FREEDMAN_LANE, RankingMethod.WESTFALL_YOUNG]);
             cohens_d = CheckBox("cohens_d", "Cohen's D Threshold", true);
             centroids = CheckBox("centroids", "ROI Centroids in brain plots", false);
             multiple_comparison_correction = PullDown("mcc", "Multiple Comparison Correction",...

+nla/+net/+result/+plot/NetworkTestPlotApp_exported.m

@@ -88,8 +88,8 @@ function getPlotTitle(app)
                 app.title = sprintf("%s (D > %g)", app.title, app.CohensDThresholdEditField.Value);
             end
             if ~isequal(app.test_method, "no_permutations")
-                if isequal(app.RankingDropDown.Value, "nla.RankingMethod.WINKLER") % Look at me, I'm MATLAB. I have no idea why enums are beneficial or how to use them
-                    app.title = strcat(app.title, "\nRanking by Winkler Method");
+                if isequal(app.RankingDropDown.Value, "nla.RankingMethod.FREEDMAN_LANE") % Look at me, I'm MATLAB. I have no idea why enums are beneficial or how to use them
+                    app.title = strcat(app.title, "\nRanking by Freedman-Lane Method");
                 elseif isequal(app.RankingDropDown.Value, "nla.RankingMethod.WESTFALL_YOUNG")
                     app.title = strcat(app.title, "\nRanking by Westfall-Young Method");
                 else
@@ -439,8 +439,8 @@ function createComponents(app)
 
             % Create RankingDropDown
             app.RankingDropDown = uidropdown(app.Panel);
-            app.RankingDropDown.Items = {'Uncorrected', 'Winkler', 'Westfall-Young'};
-            app.RankingDropDown.ItemsData = {'nla.RankingMethod.UNCORRECTED', 'nla.RankingMethod.WINKLER', 'nla.RankingMethod.WESTFALL_YOUNG'};
+            app.RankingDropDown.Items = {'Uncorrected', 'Freedman-Lane', 'Westfall-Young'};
+            app.RankingDropDown.ItemsData = {'nla.RankingMethod.UNCORRECTED', 'nla.RankingMethod.FREEDMAN_LANE', 'nla.RankingMethod.WESTFALL_YOUNG'};
             app.RankingDropDown.ValueChangedFcn = createCallbackFcn(app, @PlotScaleValueChanged, true);
             app.RankingDropDown.Position = [291 297 100 22];
             app.RankingDropDown.Value = 'nla.RankingMethod.UNCORRECTED';

+nla/+net/+result/NetworkResultPlotParameter.m

@@ -27,7 +27,7 @@
         end
 
         function result = plotProbabilityParameters(obj, edge_test_options, edge_test_result, test_method, plot_statistic,...
-                plot_title, fdr_correction, significance_filter, ranking_method)
+                plot_title, fdr_correction, effect_size_filter, ranking_method)
             % plot_title - this will be a string
             % plot_statistic - this is the stat that will be plotted
             % significance filter - this will be a boolean or some sort of object (like Cohen's D > D-value)
@@ -36,9 +36,9 @@
 
             import nla.TriMatrix nla.TriMatrixDiag
             % We're going to use a default filter here
-            if isequal(significance_filter, false)
-                significance_filter = TriMatrix(obj.number_of_networks, "logical", TriMatrixDiag.KEEP_DIAGONAL);
-                significance_filter.v = true(numel(significance_filter.v), 1);
+            if isequal(effect_size_filter, false)
+                effect_size_filter = TriMatrix(obj.number_of_networks, "logical", TriMatrixDiag.KEEP_DIAGONAL);
+                effect_size_filter.v = true(numel(effect_size_filter.v), 1);
             end
 
             % Adding on to the plot title if it's a -log10 plot
@@ -53,20 +53,26 @@
             if isstring(fdr_correction) || ischar(fdr_correction)
                 fdr_correction = nla.net.mcc.(erase(fdr_correction, "-"))();
             end
-            p_value_max = fdr_correction.correct(obj.network_atlas, obj.updated_test_options, statistic_input);
+
+            [is_sig_vector, p_value_max] = fdr_correction.correct(obj.network_atlas, obj.updated_test_options, statistic_input);
+
             p_value_breakdown_label = fdr_correction.createLabel(obj.network_atlas, obj.updated_test_options,...
                 statistic_input);
-
-            name_label = sprintf("%s %s\nP < %.2g (%s)", obj.network_test_results.test_display_name, plot_title,...
-                p_value_max, p_value_breakdown_label);
-            if p_value_max == 0
-                name_label = sprintf("%s %s\nP = %.2g (%s)", obj.network_test_results.test_display_name, plot_title,...
-                    p_value_max, p_value_breakdown_label);
-            end
+            name_label = sprintf("%s %s\n%s", obj.network_test_results.test_display_name, plot_title,...
+                p_value_breakdown_label);
+%             else
+% 
+%                 name_label = sprintf("%s %s\nP < %.2g (%s)", obj.network_test_results.test_display_name, plot_title,...
+%                     p_value_max, p_value_breakdown_label);
+%                 if p_value_max == 0
+%                     name_label = sprintf("%s %s\nP = %.2g (%s)", obj.network_test_results.test_display_name, plot_title,...
+%                         p_value_max, p_value_breakdown_label);
+%                 end
+%             end
 
             % Filtering if there's a filter provided 
             significance_plot = TriMatrix(obj.number_of_networks, "logical", TriMatrixDiag.KEEP_DIAGONAL);
-            significance_plot.v = (statistic_input.v < p_value_max) & significance_filter.v;
+            significance_plot.v = is_sig_vector & effect_size_filter.v;
 
             % scale values very slightly for display so numbers just below
             % the threshold don't show up white but marked significant
@@ -195,8 +201,8 @@ function brainFigureButtonCallback(network1, network2)
             end
 
             switch ranking_method
-                case "nla.RankingMethod.WINKLER"
-                    ranking = "winkler_";
+                case "nla.RankingMethod.FREEDMAN_LANE"
+                    ranking = "freedman_lane_";
                 case "nla.RankingMethod.WESTFALL_YOUNG"
                     ranking = "westfall_young_";
                 otherwise

+nla/+net/+result/NetworkTestResult.m

@@ -242,7 +242,7 @@ function createPValueTriMatrices(obj, number_of_networks, test_method)
 
             non_correlation_test = any(strcmp(obj.test_name, obj.noncorrelation_input_tests));
             uncorrected_names = ["uncorrected_", "legacy_"];
-            corrected_names = ["winkler_", "westfall_young_"];
+            corrected_names = ["freedman_lane_", "westfall_young_"];
 
             switch test_method
                 case "no_permutations"
@@ -285,15 +285,15 @@ function createPValueTriMatrices(obj, number_of_networks, test_method)
         % I don't really know what these do and haven't really thought about it. Hence the bad naming.
         function [sig, name] = singleSigMat(obj, network_atlas, edge_test_options, p_value, mcc_method, title_prefix)
             mcc_method = nla.net.mcc.(mcc_method)();
-            p_value_max = mcc_method.correct(network_atlas, edge_test_options, p_value);
+            [is_sig_vector, p_value_max] = mcc_method.correct(network_atlas, edge_test_options, p_value);
             p_breakdown_labels = mcc_method.createLabel(network_atlas, edge_test_options, p_value);
 
             sig = nla.TriMatrix(network_atlas.numNets(), 'double', nla.TriMatrixDiag.KEEP_DIAGONAL);
-            sig.v = (p_value.v < p_value_max);
-            name = sprintf("%s %s P < %.2g (%s)", title_prefix, obj.test_display_name, p_value_max, p_breakdown_labels);
-            if p_value_max == 0
-                name = sprintf("%s %s P = 0 (%s)", title_prefix, obj.test_display_name, p_breakdown_labels);
-            end
+            sig.v = is_sig_vector;
+            name = sprintf("%s %s %s", title_prefix, obj.test_display_name, p_value_max, p_breakdown_labels);
+%             if p_value_max == 0
+%                 name = sprintf("%s %s P = 0 (%s)", title_prefix, obj.test_display_name, p_breakdown_labels);
+%             end
         end
 
         function [number_of_tests, sig_count_mat, names] = appendSignificanceMatrix(...
@@ -337,11 +337,11 @@ function createPValueTriMatrices(obj, number_of_networks, test_method)
             % :return: The full name of the p-value. (example: "single_sammple_p_value")
 
             import nla.NetworkLevelMethod
-            noncorrelation_input_tests = ["chi_squared", "hypergeometric"];
-            non_correlation_test = any(strcmp(test_name, noncorrelation_input_tests));
+            reference_distribution_tests = ["chi_squared", "hypergeometric"];
+            reference_distribution_test = any(strcmp(test_name, reference_distribution_tests));
 
             probability = "two_sample_p_value";
-            if isequal(non_correlation_test, false)
+            if isequal(reference_distribution_test, false)
                 if isequal(test_method, "no_permutations") || isequal(test_method, "within_network_pair")
                     probability = "single_sample_p_value";
                 end

+nla/+net/ResultRank.m

@@ -42,8 +42,8 @@
                     ranking_result = obj.uncorrectedRank(test_method, permutation_results, no_permutation_results, ranking_statistic,...
                         probability, ranking_result);
 
-                    % Winkler Method ranking
-                    ranking_result = obj.winklerMethodRank(test_method, permutation_results, no_permutation_results, ranking_statistic,...
+                    % Freedman-Lane Method ranking
+                    ranking_result = obj.freedmanLaneMethodRank(test_method, permutation_results, no_permutation_results, ranking_statistic,...
                         probability, ranking_result);
 
                     % Westfall Young ranking
@@ -96,7 +96,7 @@
         end
 
 
-        function ranking = winklerMethodRank(obj, test_method, permutation_results, no_permutation_results, ranking_statistic,...
+        function ranking = freedmanLaneMethodRank(obj, test_method, permutation_results, no_permutation_results, ranking_statistic,...
             probability, ranking)
             % Ranks the observed result using method described by Winkler to correct for FWER
             %
@@ -108,7 +108,7 @@
             % :param ranking: The NetworkTestResult object to place the results
             % :return: The same NetworkTestResult object with ranking results
 
-            winkler_probability = strcat("winkler_", probability);
+            freedman_lane_probability = strcat("freedman_lane_", probability);
             % NET-278: The "max statistic" for hypergeometric is the most significant p-value. Which is the smallest p-value.
             max_statistic_array = max(abs(permutation_results.(strcat(ranking_statistic, "_permutations")).v));
             if isequal(ranking.test_name, "hypergeometric")
@@ -117,16 +117,16 @@
 
             for index = 1:numel(no_permutation_results.(strcat("uncorrected_", probability)).v)
                 if isequal(ranking.test_name, "hypergeometric")
-                    ranking.(test_method).(winkler_probability).v(index) = sum(...
+                    ranking.(test_method).(freedman_lane_probability).v(index) = sum(...
                         squeeze(max_statistic_array) <= abs(no_permutation_results.(ranking_statistic).v(index))...
                     );
                 else
-                    ranking.(test_method).(winkler_probability).v(index) = sum(...
+                    ranking.(test_method).(freedman_lane_probability).v(index) = sum(...
                         squeeze(max_statistic_array) >= abs(no_permutation_results.(ranking_statistic).v(index))...
                     );
                 end
             end
-            ranking.(test_method).(winkler_probability).v = ranking.(test_method).(winkler_probability).v ./ obj.permutations;
+            ranking.(test_method).(freedman_lane_probability).v = ranking.(test_method).(freedman_lane_probability).v ./ obj.permutations;
         end
 
         function ranking = westfallYoungMethodRank(obj, test_method, permutation_results, no_permutation_results, ranking_statistic,...