41:null 42:["$","title",null,{"children":"Loess with approximate GCV bandwidth (function definitions) | Simon Jackman | Observable"}] 48:T765,function addLoessRegression(Plot) { Plot.loess = function ({ x, y, bandwidth, order, ...options }) { console.log("greetings from Plot.loess"); const z = options.z || options.stroke; // maybeZ return Plot.transform(options, function (data, facets) { const Z = Plot.valueof(data,z); const X = Plot.valueof(data,x); const Y = Plot.valueof(data,y); const loess_data = []; console.log("X has " + X.length + " length"); for(let i = 0; i < X.length; i++) { //if(!isNaN(X[i]) && !isNaN(Y[i])){ loess_data.push([X[i],Y[i]]); //Z[i] = NaN; //} } console.log("Plot.loess has constructed data:") console.log(loess_data); const regFacets = []; const points_out = []; var loess_data_subset = []; //console.log(loess_data_subset); for (const facet of facets) { const regFacet = []; for (const I of Z ? d3.group(facet, (i) => Z[i]).values() : [facet]) { //console.log("I:"); console.log(I.length); loess_data_subset = Array(I.length); //console.log("loess_data_subset") //console.log(loess_data_subset); for(let j = 0; j < I.length; j++){ //console.log(j + " " + I[j]); //console.log(loess_data[I[j]]); loess_data_subset[j] = loess_data[I[j]]; }; //console.log(loess_data_subset); const reg = loess_best(loess_data_subset); for( const d of reg){ const j = points_out.push(d) - 1; if (z) d[z] = Z[I[0]]; regFacet.push(j); } } regFacets.push(regFacet); } //console.log("Plot.loess is returning points:") //console.log(points_out); return { data: points_out, facets: regFacets }; }); }; return Plot; }49:T452,function loess_trace(loess_data,bandwidth){ console.log("loess_trace has bandwidth " + bandwidth); //console.log("loess trace has data:"); console.log(loess_data); //const loess_data = data.slice().sort(function(x,y){return d3.ascending(x[0],y[0]);}) const x_vec = loess_data.map(d => d[0]); //console.log("loess_trace x"); //console.log(x_vec); const n = x_vec.length const M = 50; const nindx = n > M ? M : n; var index = new Array(nindx); if(nindx == M){ for(let i = 0; i < nindx; i++){ index[i] = Math.floor(i/nindx * n); } } else { for(let i = 0; i < nindx; i++){ index[i] = i; } } var trace = 0; var tmpData = []; var f; for(let i=0; id[0]) .y(d=>d[1]) .bandwidth(bandwidth) (tmpData); trace += f.map(d => d[1])[i]; tmpData[index[i]][1] = 0.0; } console.log("loess_trace: trace = " + trace); return trace; }4a:T41b,function loess_gcv(data,bw){ console.log("loess_gcv has bandwidth " + bw); const f = d3.regressionLoess() .x(d=>d[0]) .y(d=>d[1]) .bandwidth(bw)(data); const e = loess_resid(data,f); //console.log("loess_gcv has residuals"); //console.log(e); const v = loess_trace(data,bw); console.log("loess_gcv: trace of smoother matrix " + v); const n = e.length; var n_ok = 0; var e_ok = [] //console.log("loess_gcv has s = " + s); for(let i=0; id[0]) .y(d=>d[1]) .bandwidth(bw)(data); const e = loess_resid(data,f); const x_vec = data.map(d => d[0]); //console.log("loess_trace x"); //console.log(x_vec); const M = 200; const n = x_vec.length > M ? M : x_vec.length; var index = new Array(n); if(n > M){ for(let i = 0; i < n; i++){ index[i] = Math.floor(i * x_vec.length / n); } } else { for(let i = 0; i < n; i++){ index[i] = i; } } // loop over M observations for approximation to CVSS var s_ii; var y_tmp = new Array(n).fill(0); var tmpData = []; var f_i; var cvss = 0; for(let i=0; id[0]) .y(d=>d[1]) .bandwidth(bw) (tmpData); s_ii = f_i.map(d => d[1])[i]; // S_{ii} (diagonal of smoother matrix) //console.log("loess_cvss: s_ii for observation " + index[i] + " " + s_ii); cvss += Math.pow(e[index[i]]/(1-s_ii),2); //console.log("loess_cvss: running tally cvss " + cvss); tmpData[i][1] = 0.0 // reset i-th pseudo-y value to 0 } return cvss; }4c:[] 46:{"id":"2f5e9b049fb948db","slug":"loess-autobandwidth","title":"Loess with approximate GCV bandwidth (function definitions)","thumbnail":"20519ab11f67bd8b16cd552234e8f0e4b994c705dbcf03fd8b39b621ed607b97","access_level":"public","create_time":"$D2023-10-28T06:59:50.934Z","update_time":"$D2024-05-25T08:18:21.298Z","view_time":null,"first_public_time":"$D2023-10-28T07:06:38.774Z","has_unpublished_edits":null,"fork":{"id":"538e9ef107239a3f","slug":null,"title":"Loess with optimal selection of span","owner":{"id":"e453ea60d29ec23e","login":"simonjackman","name":"Simon Jackman"}},"stars":1,"starred":false,"owner":{"type":"team","id":"e453ea60d29ec23e","login":"simonjackman","name":"Simon Jackman","avatar_url":"https://avatars-next.observableusercontent.com/c14bb6609a47c8c937d2b9cfa6d34263984b5724ee02d5600987e325903bc377","tier":"starter_2024","bio":"","following":false},"version":158,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"da2057c4cea8c9e4","login":"simonjackman","name":"Simon Jackman","avatar_url":"https://avatars-next.observableusercontent.com/c14bb6609a47c8c937d2b9cfa6d34263984b5724ee02d5600987e325903bc377","tier":null,"bio":"","following":false}],"members":[{"type":"individual","id":"da2057c4cea8c9e4","login":"simonjackman","name":"Simon Jackman","avatar_url":"https://avatars-next.observableusercontent.com/c14bb6609a47c8c937d2b9cfa6d34263984b5724ee02d5600987e325903bc377","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Loess with approximate GCV bandwidth (function definitions)","cells":[{"id":0,"value":"# Loess with approximate GCV bandwidth (function definitions)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"function loess_best(data){\n //console.log(\"loess_best has been passed data:\")\n //console.log(data);\n const localData = data.filter(d => isFinite(d[0]) && isFinite(d[1]))\n //console.log(localData);\n const grid = loess_grid(localData)\n .sort(function(x,y){return d3.ascending(x.gcv,y.gcv);})\n console.log(\"loess_best has grid:\")\n console.log(grid);\n const bw = grid.length > 0 ? grid[0].bw : 0.5;\n console.log(\"bandwidth selected: \" + bw);\n // fit loess with this bandwidth\n const f = d3.regressionLoess()\n .x(d => d[0])\n .y(d => d[1])\n .bandwidth(bw)\n (localData);\n //console.log(f);\n return f;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"function loess_resid(data,f){\n const y_vec = data.map(d=>d[1])\n const yhat = f.map(d=>d[1])\n const n = data.length\n var e = new Array(n)\n for(let i = 0; i < n; i++){\n e[i] = y_vec[i] - yhat[i];\n }\n return e;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"$4a","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"function loess_grid(data){\n const loess_data = data.slice().sort(function(x,y){return d3.ascending(x[0],y[0]);})\n //console.log(\"loess_grid has been passed data:\");\n //console.log(data);\n const bw = d3.range(.2,1,.05);\n //console.log(\"loess_grid: bandwidths to assess\");\n //console.log(bw);\n var out = new Array(bw.length);\n for(let i=0; i !isNaN(d.gcv));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"d3 = require(\"d3@7\", \"d3-regression@1\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 47:"$46:body"