2a:T449,table(_.map (RELAX_summary, (data,key) => { var r = {"Alignment" : key, "Seqs": data["input"]["number of sequences"], "Sites" : data["input"]["number of sites"], "p-value" : d3v5.format (".2g")(data["test"]["p-value"])}; get_k (data, r); return r; }).sort ((a,b) => {return (+a["p-value"])-(+b["p-value"]);}), {limit: 1024,columns: [ 'Alignment', 'Seqs', 'Sites', { key: 'p-value', name: 'LRT p-value', render: val => {if (val < 0.05) return `${val}`; return `${val}`;} }, { key: 'EF', name: `K_"EF"`, render: val => {if (val > 1) return `${val}`; return `${val}`;} }, { key: 'K', name: `K_"K"`, render: val => {if (val > 1) return `${val}`; return `${val}`;} }, { key: 'H', name: `K_"H"`, render: val => {if (val > 1) return `${val}`; return `${val}`;} } ]})2b:T56b,rate_distribution = (plot_data, plot_title) => { var data_by_group = {}; _.each (plot_data, (rates) => { _.each (rates["relaxation or intensification parameter"], (value, key) => { if (key != "NEF") { if (!(key in data_by_group)) { data_by_group [key] = []; } data_by_group[key].push (value); } }); }); data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'suspectedoutliers', jitter: 0.3, pointpos: 0, type: 'box', boxmean : true, opacity : 0.5, notched : true, marker:{ color: 'rgb(50,50,50)', }, name: "Branch set " + k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { range: [0,5], gridwidth: 0, nticks : 5, tickmode : "auto", title: "Intensification/relaxation parameter", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }2c:T9d6,k_distribution = (relax_data, plot_title, by_paralog) => { var data_by_group, para_regexp; if (!by_paralog) { data_by_group = {'K' : [], 'NEF' : [], 'H' : [], 'EF' : []}; } else { data_by_group = {'K-ortho' : [], 'K-para' : [], 'NEF-ortho' : [], 'NEF-para' : [], 'H-ortho' : [], 'H-para' : [], 'EF-ortho' : [], 'EF-para' : []}; para_regexp = new RegExp('_([0-9]+)_XX'); } //K < NEF < H < EF _.each (relax_data, (dataset, ds_name) => { // standardize to harmonic mean of 1 _.each (dataset["ks"], (value, key) => { var label = dataset["labels"][key]; if (label == 'Unclassified') { return; } if (by_paralog) { var match = para_regexp.exec (key); if (match) { if ((+match[1]) > 0) { label += "-para"; } else { label += "-ortho"; } } else { return; } } if (! (label in data_by_group)) { data_by_group[label] = []; } data_by_group[label].push (value); }); }); /*console.log ("list(c(" + data_by_group['K'].join (',') + "),"+ "c(" + data_by_group['NEF'].join (',') + "),"+ "c(" + data_by_group['H'].join (',') + "),"+ "c(" + data_by_group['EF'].join (',') + "))" );*/ data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'suspectedoutliers', jitter: 0.3, pointpos: 0, type: 'box', boxmean : true, opacity : 0.5, notched : true, marker:{ color: 'rgb(50,50,50)', }, name: k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { range: [0,20], gridwidth: 0, nticks : 5, tickmode : "auto", title: "Branch intensification/relaxation parameter", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }2d:T620,omega_distribution = (omega_data, plot_title) => { var data_by_group = {'K' : [], 'NEF' : [], 'H' : [], 'EF' : []}; var upper_bound = 1.5; _.each (omega_data, (dataset, ds_name) => { _.each (dataset, (value, key) => { var label = key.substring (key.indexOf ('*') + 1, key.length-1); if (label == 'Unclassified') { return; } if (! (label in data_by_group)) { data_by_group[label] = []; } data_by_group[label].push (Math.sqrt(value[0][0])); }); }); data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'all', jitter: 0.5, pointpos: 0, type: 'box', boxmean : false, pointpos: -1.8, opacity : 0.5, notched : false, marker:{ color: 'rgb(50,50,50)', }, name: k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { range: [0,upper_bound], gridwidth: 0, nticks : 5, tickmode : "auto", title: "ω^1/2", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }2e:T80b,omega_distribution_relax = (omega_data, plot_title) => { var data_by_group = {'K' : [], 'NEF' : [], 'H' : [], 'EF' : []}; var ub = 1.1; //K < NEF < H < EF _.each (omega_data, (dataset, ds_name) => { _.each (dataset, (value, key) => { var label = key; if (label == 'Unclassified') { return; } if (! (label in data_by_group)) { data_by_group[label] = []; } var sum = 0.; var weight = 0.; _.each (value, v => { if (v.omega <= 1) { sum += d3.min ([v.omega,5]) * v.proportion; weight += v.proportion; } //sum += d3.min ([v.omega,20]) * v.proportion; }); sum /= weight; data_by_group[label].push (Math.sqrt (d3.min ([sum,sum]))); }); }); console.log ("list(c(" + data_by_group["K"].join (",")+"),c("+data_by_group["NEF"].join (",")+"),c("+data_by_group["H"].join (",")+"),c("+data_by_group["EF"].join (",")+"))"); data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'all', jitter: 0.5, pointpos: 0, type: 'box', boxmean : false, pointpos: -1.8, opacity : 0.5, notched : false, marker:{ color: 'rgb(100,100,100)', }, name: k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { range: [0,ub], gridwidth: 0, nticks : 5, tickmode : "auto", title: "Mean ω for negative selection", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }2f:T9c0,hexbins = (points) => { // data => array of [x,y] coordinates var svg_dim = [600,500]; var svg = d3v5.select(DOM.svg(svg_dim[0], svg_dim[1])), margin = {top: 20, right: 20, bottom: 50, left: 80}, width = svg_dim[0] - margin.left - margin.right, height = svg_dim[1] - margin.top - margin.bottom, g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")"); svg.attr ("width", svg_dim[0]).attr ("height", svg_dim[1]); var color = d3v5.scaleSequential(d3.interpolateLab("blue", "red")) .domain([0, 50]); var x_domain = d3v5.extent (points, r => {return r[0];}); var y_domain = d3v5.extent (points, r => {return r[1];}); var bin_radius = 6; var hexbin = d3_hexbin.hexbin() .radius(bin_radius) .size(width, height); var x = d3v5.scaleLinear() .domain([x_domain[0], x_domain[1] * 1.01]) .range([0, width]).nice(); var y = d3v5.scaleLinear() .domain(y_domain) .range([height, 0]).nice(); g.append("clipPath") .attr("id", "clip-rectangle") .append("rect") .attr("width", width) .attr("height", height); g.append("g") .attr("class", "hexagon") .style("clip-path", "url(#clip-rectangle)") .style("-webkit-clip-path", "url(#clip-rectangle)") .selectAll("path") .data(hexbin(points.map (d => {return [x(d[0]), y(d[1])];}))) .enter().append("path") .attr("d", hexbin.hexagon()) //.attr("d", function (d) {return hexbin.hexagon(radius(d.length));}) .attr("transform", function(d) { return "translate(" + (d.x+bin_radius) + "," + (d.y-bin_radius) + ")"; }) .attr("fill", function(d) { return color(d.length); }); g.append("g") .attr("class", "axis axis--y legend-text") .call(d3v5.axisLeft(y).tickSizeOuter(-width));//.tickFormat (d3.format(".2~p"))); g.append("g") .attr("class", "axis axis--x legend-text") .attr("transform", "translate(0," + height + ")") .call(d3v5.axisBottom(x).tickSizeOuter(-height)); g.append ("text").text ("omega").attr ("class", "legend-text").attr ("x", width).attr ("y", height).attr ("dy", "2.5em").attr ("text-anchor", "end"); g.append ("text").text ("weight").attr ("class", "legend-text").attr ("x", 0).attr ("y", 0).attr ("dy", "-3.5em").attr ("text-anchor", "end").attr ("transform", "rotate (-90)"); /*var corr = new spearman_rho (points.map (r => {return r[0];}), points.map (r => {return r[1];})); corr.calc().then (value => {console.log (value);}); */ return svg.node(); }30:T68c,omega_distribution_absrel = (omega_data, plot_title) => { var data_by_group = {'K' : [], 'NEF' : [], 'H' : [], 'EF' : []}; var ub = 10; //K < NEF < H < EF _.each (omega_data, (dataset, ds_name) => { _.each (dataset[1], (value, key) => { if (dataset[0] in RELAX_summary) { var label = RELAX_summary[dataset[0]]['labels'][key]; if (label == 'Unclassified') { return; } if (! (label in data_by_group)) { data_by_group[label] = []; } var sum = 0.; _.each (value, v => {sum +=v[1] * Math.min (10,v[0])}); data_by_group[label].push (sum); } }); }); data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'suspectedoutliers', jitter: 0.3, pointpos: 0, type: 'box', boxmean : true, opacity : 0.5, notched : true, marker:{ color: 'rgb(50,50,50)', }, name: k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { range: [0,ub], gridwidth: 0, nticks : 5, tickmode : "auto", title: "Average dN/dS", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }31:T8d7,proportion_selected = (fraction_data, plot_title) => { var data_by_group = {}; var annotations = []; var x_tick = 0.5; _.each (fraction_data, (d, k) => { data_by_group [k + ' para'] = _.map (d, (r) => r[0]); data_by_group [k + ' ortho'] = _.map (d, (r) => r[1]); var p_value = "p = " + d3v5.format (".2e")(genstat.wilcoxon (data_by_group [k + ' para'], data_by_group [k + ' ortho']).p); var para_mean = d3v5.format (".3g") (d3.mean (data_by_group [k + ' para'])); var ortho_mean = d3v5.format (".3g") (d3.mean (data_by_group [k + ' ortho'])); annotations.push ({ x: x_tick, y: 1.0, xref: 'x', yref: 'y', text: p_value, showarrow: true, arrowhead: 7, ax: 0, ay: 40, font:{ family: 'Arial', size : 14 } }); annotations.push ({ x: x_tick-0.5, y: 1.1, xref: 'x', yref: 'y', text: para_mean, showarrow: false, font:{ family: 'Arial', size : 14 } }); annotations.push ({ x: x_tick+0.5, y: 1.1, xref: 'x', yref: 'y', text: ortho_mean, showarrow: false, font:{ family: 'Arial', size : 14 } }); x_tick += 2; }); data_by_group = _.map (data_by_group, (x,k) => {return { y : _.clone (x), boxpoints: 'suspectedoutliers', jitter: 0.3, pointpos: 0, type: 'box', boxmean : true, opacity : 0.5, notched : true, marker:{ color: 'rgb(50,50,50)', }, name: k }}); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, annotations: annotations, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Branch Group", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { gridwidth: 0, nticks : 5, tickmode : "auto", title: "Proportions of branches under selection", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, data_by_group, layout); return div; }32:T4ce,absrel_proportions = (group_filter)=>_.filter (_.map (summary_absrel, (r, key) => { var total = 0, selected_p = 0, selected_o = 0, total_p = 0, total_o = 0; var by_group = {}; var para_regexp = new RegExp('_([0-9]+)_XX'); _.each (r['pvalues'], (p, name) => { var m = para_regexp.exec (name); if (group_filter) { if (!(key in RELAX_summary)) { return; } if ( RELAX_summary[key]['labels'][name] != group_filter) { return; } } if (m) { if (p < 0.05) { if (+m[1] > 0) { selected_p++; } else { selected_o++; } } if (+m[1] > 0) { total_p++; } else { total_o++; } total ++; } }); if (total_p && total_o) { return [selected_p/total_p, selected_o/total_o]; } return null; } ), (r)=>r );33:[] 29:{"title":"Multiple group RELAX analysis","cells":[{"id":0,"value":"md`# Multiple group RELAX analysis\n\nThis page summarizes the analysis of **${number_of_trees}** alignments with branches partitioned into multiple groups (M=4 in this case). The standard RELAX analysis has been extended to estimate M-1 intensification / relaxation parameters _K_ for each of the groups, except for one (_EF_), which is held as the reference. A significant test result, with the p-value derived using the LRT with χ² distribution with M-1 degrees of freedom, implies that **some** of the _K_ values are different from each other, but does not determine **which** ones.\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"md`**Table 1** Analysis results sorted by p-value. Relaxation/Intensification parameters for each branch group relative to **NEF** are shown.` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"$2a","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"md`**Figure 2.** Distribution of inferred relaxation/intensifcation parameters by branch group (all relative to the **NEF** group) and censored at K = 5 to improve readability`","pinned":false,"hidden":false,"mode":"ojs"},{"id":255,"value":"rate_distribution (_.map (RELAX_summary, v => {return v[\"test\"]}),\"\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":328,"value":"md`**Figure 3.** The distributions of branch-specfific relaxation/intensification parameters (k) grouped by branch class`","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"k_distribution (RELAX_summary,\"\", false)","pinned":false,"hidden":false,"mode":"ojs"},{"id":466,"value":"md`**Figure 3.1** The distributions of branch-specfific relaxation/intensification parameters (k) grouped by branch class and ortholog/paralog. Only terminal branches are shown.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"k_distribution (RELAX_summary,\"\", true)","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"md`**Figure 4.** The distributions of group-specific mean dN/dS grouped by branch class, inferred under the simple (no rate variation) MG94 model`","pinned":false,"hidden":false,"mode":"ojs"},{"id":301,"value":"omega_distribution (_.map (RELAX_summary, (r)=>r['mg']['Rate Distributions']),\"\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":417,"value":"md`**Figure 5.** The distributions of group-specific mean dN/dS grouped by branch class, inferred under the partitioned RELAX model`","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"omega_distribution_relax (_.map (RELAX_summary, (r)=>r['descriptive']['Rate Distributions']),\"\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"md`**Figure 5.B** The distributions of branch-specific mean dN/dS grouped by branch class, inferred under the aBSREL model`","pinned":false,"hidden":false,"mode":"ojs"},{"id":506,"value":"omega_distribution_absrel (_.map (summary_absrel, (r,k)=>[k,r['distributions']]),\"\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":554,"value":"md`**Figure 6.** The proportion of branches (terminal only) inferred to be under selection by aBSREL. Wilcoxon rank-sum p-value is **${d3v5.format (\".2e\")(genstat.wilcoxon (_.map (absrel_proportions(''), (r) => r[0]), _.map (absrel_proportions(''), (r) => r[1])).p)}**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":699,"value":"proportion_selected ({'K' : absrel_proportions('K'), 'NEF' : absrel_proportions('NEF'), 'H' : absrel_proportions('H'), 'EF' : absrel_proportions('EF')}, \"\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"md`**Figure 7.** The distribution of positively selected rate class (ω > 1) for the K branch group. Blue = least weight; red = most weight`","pinned":false,"hidden":false,"mode":"ojs"},{"id":336,"value":"hexbins (extract_class (\"K\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":420,"value":"md`**Figure 8.** The distribution of positively selected rate class (ω > 1) for the NEF branch group`","pinned":false,"hidden":false,"mode":"ojs"},{"id":422,"value":"hexbins (extract_class (\"NEF\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"md`**Figure 9.** The distribution of positively selected rate class (ω > 1) for the H branch group`","pinned":false,"hidden":false,"mode":"ojs"},{"id":430,"value":"hexbins (extract_class (\"H\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":426,"value":"md`**Figure 10.** The distribution of positively selected rate class (ω > 1) for the EF branch group`","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"hexbins (extract_class (\"EF\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"","pinned":false,"hidden":false,"mode":"ojs"},{"id":75,"value":"viewof fileview = DOM.select(_.map(RELAX_summary, (v,k) => {return k}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"viewof height = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"viewof width = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"md`**Figure 11.** Tree for \\`${fileview}\\` colored by group and scaled by input branch lengths. EF/NEF, H, K, Unclassified`","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"render_tree (tree(d3.layout.newick_parser(RELAX_summary[fileview][\"input\"][\"trees\"][\"0\"])), RELAX_summary[fileview][\"labels\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"md`#### Dependancies`","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"_ = window._ = await require(\"lodash@4\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"d3v5 = require(\"https://d3js.org/d3.v5.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"RELAX_summary=await d3v5.json(\"https://dl.dropbox.com/s/bnt3r2cg3jmby92/results.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"number_of_trees = _.keys (RELAX_summary).length","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"import { table } from \"@gampleman/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":66,"value":"get_k = (d,o) => {\n _.each ([\"K\",\"H\",\"EF\"], g => {\n if (g in d[\"test\"][\"relaxation or intensification parameter\"]) {\n o [g] = d3v5.format (\".4f\") (d[\"test\"][\"relaxation or intensification parameter\"][g]);\n } else {\n o [g] = \"None\";\n }\n \n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":86,"value":"d3 = {\n const d3 = window.d3 = await require(\"d3@3\");\n await require(\"phylotree@v0.2.0-alpha.1\").catch(() => {});\n return d3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":122,"value":"plotly=require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"tree = {\n return d3.layout.phylotree()\n .options({\n 'left-right-spacing': 'fit-to-size', // fit to given size top-to-bottom\n 'top-bottom-spacing': 'fit-to-size', // fit to given size left-to-right\n 'selectable': false, // make nodes and branches not selectable\n 'collapsible': false, // turn off the menu on internal nodes\n 'transitions' : false // turn off animations\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"render_tree = (parsed_tree, labels) => {\n //var parsed_tree = tree(d3.layout.newick_parser(RELAX_summary[fileview][\"input\"][\"trees\"][\"0\"])).size([height, width]);\n const svg = d3.select(DOM.svg(width, height));\n parsed_tree\n .svg(svg)\n .size([height, width])\n .style_edges (function (element, data) {\n if (data.target.name in labels) {\n element.style (\"stroke\", group_colors(labels[data.target.name]));\n element.style (\"stroke-width\", \"5px\");\n } else {\n element.style (\"stroke\", null);\n element.style (\"stroke-width\", null);\n }\n })\n .layout();\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":138,"value":"group_colors = d3v5.scaleOrdinal(d3v5.schemeCategory10).domain (['NEF','K','H','EF'])","pinned":false,"hidden":false,"mode":"ojs"},{"id":251,"value":"$2b","pinned":false,"hidden":false,"mode":"ojs"},{"id":303,"value":"","pinned":false,"hidden":false,"mode":"ojs"},{"id":288,"value":"$2c","pinned":false,"hidden":false,"mode":"ojs"},{"id":304,"value":"$2d","pinned":false,"hidden":false,"mode":"ojs"},{"id":438,"value":"$2e","pinned":false,"hidden":false,"mode":"ojs"},{"id":323,"value":"d3_hexbin=require(\"d3-hexbin\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":326,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":343,"value":"extract_class = (group) => { \n return _.map (_.map (_.filter (RELAX_summary, f => f[\"descriptive\"][\"Rate Distributions\"][group]), f=>f[\"descriptive\"][\"Rate Distributions\"][group][_.size (f[\"descriptive\"][\"Rate Distributions\"][group])-1]), g => [d3.min ([g.omega,10]), g.proportion]);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":500,"value":"summary_absrel = await d3v5.json ('https://dl.dropbox.com/s/bgx15j3bhm2mz8p/results-absrel.json')","pinned":false,"hidden":false,"mode":"ojs"},{"id":503,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":539,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":563,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":556,"value":"genstat = require('https://bundle.run/genstats@0.0.1')","pinned":false,"hidden":false,"mode":"ojs"},{"id":558,"value":"genstat.wilcoxon (_.map (absrel_proportions(null), (r) => r[0]), _.map (absrel_proportions, (r) => r[1]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":653,"value":"{\n var significant_results = _.filter (RELAX_summary, (r) => r['test']['p-value']<=0.05) ;\n var summary = {'H' : [0,0,[[],[]]], 'EF' : [0,0,[[],[]]], 'K' : [0,0,[[],[]]]};\n _.each (significant_results, (t) => {\n _.each (['H', 'EF', 'K'], (c) => {\n var column = t['test']['relaxation or intensification parameter'][c] > 1 ? 0 : 1;\n summary[c][column] ++;\n summary[c][2][column].push (t['test']['relaxation or intensification parameter'][c]);\n });\n });\n \n var fmt = d3.format (\".3g\");\n \n var result = {};\n _.each (['H', 'EF', 'K'], (k) => {\n result[k] = {};\n result[k]['intensified'] = \"\" + summary[k][0] + \" (\" + fmt(d3.median(summary[k][2][0]))+ \")\";\n result[k]['relaxed'] = \"\" + summary[k][1] + \" (\" + fmt(d3.median(summary[k][2][1]))+ \")\";\n\n });\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":673,"value":"_.size (summary_absrel)","pinned":false,"hidden":false,"mode":"ojs"},{"id":681,"value":"absrel_proportions(null)","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}