{"appearance":{"background":"white","padding":14,"font":{"family":"Courier New","size":10.0,"bold":false,"italic":false,"underline":false,"strikeout":false,"color":"rgb(0,72,168)"},"border":{"on":true,"size":0.0,"style":"solid","color":"#666"},"text":{"wrap":false,"hAlign":"left","vAlign":"top"}},"outputType":"WIDGET","widgetState":null,"outputs":{"console":"<pre class='debug-source'>>library(flipMultivariates)\n</pre>\n<pre class='debug-source'>>set.seed(23)\n</pre>\n<pre class='debug-source'>>y <- rep(1:3, rep(100,3))\n</pre>\n<pre class='debug-source'>>x0 <- y + rnorm(300, 0, .5)\n</pre>\n<pre class='debug-source'>>x1 <- x0\n</pre>\n<pre class='debug-source'>>x1[y == 2] <- x1[y == 2] - 1\n</pre>\n<pre class='debug-source'>>x2 <- y + rnorm(300,0, 1)\n</pre>\n<pre class='debug-source'>>x3 <- y + rnorm(300,0, 6)\n</pre>\n<pre class='debug-source'>>y <- factor(y, levels = 1:3, labels = c("Group A", "Group B", "Group C"))\n</pre>\n<pre class='debug-source'>>SupportVectorMachine(y ~ x1 + x2 + x3, output = "Prediction-Accuracy Table")\n</pre>\r\n<div class=\"debug-summarystatistics\">\r\n<table>\r\n<tr><th>Total time:</th><td>1.04s</td></tr>\r\n<tr><th>Time on R server:</th><td title=\"rApacheServe 0.98s (pre 0.01s, post 0.00s) httpget_code() setup for eval 0.00s session$eval 0.94s (pre 0.00s, post 0.15s) unexplained 0.04s apparmor forking (pre 0.04s, post 0.00s)\">0.98s</td></tr>\r\n<tr><th>Time evaluating code:</th><td>0.74s</td></tr>\r\n<tr><th>Bytes sent:</th><td>898</td></tr>\r\n<tr><th>Bytes received:</th><td>48,262</td></tr>\r\n</table>\r\n</div>","htmlwidgets":"<div id=\"htmlwidget_container\">\n <div id=\"htmlwidget-76d0df96d1a91852928a\" style=\"width:960px;height:500px;\" class=\"rhtmlHeatmap html-widget\"></div>\n</div>\n<script type=\"application/json\" data-for=\"htmlwidget-76d0df96d1a91852928a\">{\"x\":{\"rows\":null,\"cols\":null,\"matrix\":{\"data\":[\"79\",\"21\",\"0\",\"33\",\"67\",\"0\",\"0\",\"1\",\"99\"],\"dim\":[3,3],\"rows\":[\"Group A\",\"Group B\",\"Group C\"],\"cols\":[\"Group A\",\"Group B\",\"Group C\"],\"cells_to_hide\":[0,0,0,0,0,0,0,0,0],\"cellnote_in_cell\":[\"79\",\"21\",\"0\",\"33\",\"67\",\"0\",\"0\",\"1\",\"99\"]},\"image\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAADCAYAAABWKLW/AAAALUlEQVQImWPYIyr1/++xDf//f/3wn+Hvgsb/D/TUIJz/Xz/8///53f90Bt7/ABlmG6gfOQdRAAAAAElFTkSuQmCC\",\"theme\":null,\"options\":{\"xaxis_height\":80,\"yaxis_width\":120,\"xaxis_font_size\":11.06,\"yaxis_font_size\":11,\"xaxis_location\":\"top\",\"yaxis_location\":\"left\",\"xaxis_title\":\"Predicted\",\"yaxis_title\":\"Observed\",\"xaxis_title_font_size\":14,\"yaxis_title_font_size\":14,\"xaxis_hidden\":false,\"yaxis_hidden\":false,\"xaxis_font_family\":\"sans-serif\",\"yaxis_font_family\":\"sans-serif\",\"title\":\"Prediction-Accuracy Table: y\",\"title_font_size\":24,\"title_font_family\":\"sans-serif\",\"title_font_color\":\"#000000\",\"subtitle\":null,\"subtitle_font_size\":18,\"subtitle_font_family\":\"sans-serif\",\"subtitle_font_color\":\"#000000\",\"footer\":\"Fitted model : n = 300 cases used in estimation; 300 observed/predicted pairs with 81.67% accuracy;\",\"footer_font_size\":11,\"footer_font_family\":\"sans-serif\",\"footer_font_color\":\"#000000\",\"tip_font_size\":11,\"tip_font_family\":\"sans-serif\",\"brush_color\":\"#0000FF\",\"show_grid\":true,\"legend_font_size\":11,\"x_is_factor\":false,\"legend_colors\":[\"#67000D\",\"#71010F\",\"#7B0310\",\"#850612\",\"#8F0813\",\"#990B14\",\"#A40F15\",\"#AA1016\",\"#B01217\",\"#B71319\",\"#BD151A\",\"#C3161B\",\"#C9181D\",\"#CF1D1F\",\"#D52421\",\"#DB2924\",\"#E12F26\",\"#E73429\",\"#ED392B\",\"#F0412F\",\"#F24934\",\"#F45139\",\"#F6593E\",\"#F86043\",\"#FA6748\",\"#FB6E4D\",\"#FC7454\",\"#FC7B5A\",\"#FC8261\",\"#FC8867\",\"#FC8E6E\",\"#FC9575\",\"#FD9B7C\",\"#FDA284\",\"#FDA98C\",\"#FDAF93\",\"#FCB69B\",\"#FCBDA3\",\"#FDC3AB\",\"#FDC9B3\",\"#FECFBB\",\"#FED5C3\",\"#FEDBCB\",\"#FEE0D3\",\"#FEE4D7\",\"#FFE7DC\",\"#FFEBE1\",\"#FFEEE6\",\"#FFF2EB\",\"#FFF5F0\"],\"legend_range\":[0,99],\"legend_width\":60,\"legend_digits\":1,\"shownote_in_cell\":true,\"cell_font_size\":11,\"left_columns\":null,\"left_columns_font_size\":11,\"right_columns\":null,\"right_columns_font_size\":11,\"extra_tooltip_info\":{\"% cases\":[\"26.33% of all cases\",\" 7.00% of all cases\",\" 0.00% of all cases\",\"11.00% of all cases\",\"22.33% of all cases\",\" 0.00% of all cases\",\" 0.00% of all cases\",\" 0.33% of all cases\",\"33.00% of all cases\"],\"% Predicted\":[\" 70.54% of Predicted class\",\" 23.60% of Predicted class\",\"-\",\" 29.46% of Predicted class\",\" 75.28% of Predicted class\",\"-\",\"-\",\" 1.12% of Predicted class\",\"100.00% of Predicted class\"],\"% Observed\":[\"79.00% of Observed class\",\"21.00% of Observed class\",\"-\",\"33.00% of Observed class\",\"67.00% of Observed class\",\"-\",\"-\",\" 1.00% of Observed class\",\"99.00% of Observed class\"]},\"anim_duration\":500,\"yclust_width\":0,\"xclust_height\":0}},\"evals\":[],\"jsHooks\":[]}</script>\n<script type=\"application/htmlwidget-sizing\" data-for=\"htmlwidget-76d0df96d1a91852928a\">{\"viewer\":{\"width\":450,\"height\":350,\"padding\":5,\"fill\":true},\"browser\":{\"width\":960,\"height\":500,\"padding\":5,\"fill\":true}}</script>","htmlwidget-head":"{\"stylesheets\":[\"https://rserverhtmlwidgetasset.azureedge.net/heatmapcore-0b0c3c977a3de18d1ff639e311c1249c.css\"],\"javascript\":[\"https://rserverhtmlwidgetasset.azureedge.net/htmlwidgets-d2ab507a7e7d3e3d7c2178bda9d4c762.js\",\"https://rserverhtmlwidgetasset.azureedge.net/d3.min-fe2151217025e25f119e69ca126390f4.js\",\"https://rserverhtmlwidgetasset.azureedge.net/heatmapcore-e2f210a7754f3fcbeddce04d2f73b348.js\",\"https://rserverhtmlwidgetasset.azureedge.net/index-1868b419773570135e12157d84680fcc.js\",\"https://rserverhtmlwidgetasset.azureedge.net/rhtmlHeatmap-fce4de827a4fdb1bd7d1317d03394668.js\"],\"attachments\":[]}","message":"","warning":"","visible":"yes"},"secondsTaken":1.0361538000000001,"updated":"2017-05-19T04:27:16.4701722Z","lastUpdatedMessage":null,"executedCode":"library(flipMultivariates)\nset.seed(23)\ny <- rep(1:3, rep(100,3))\nx0 <- y + rnorm(300, 0, .5)\nx1 <- x0\nx1[y == 2] <- x1[y == 2] - 1\nx2 <- y + rnorm(300,0, 1)\nx3 <- y + rnorm(300,0, 6)\ny <- factor(y, levels = 1:3, labels = c(\"Group A\", \"Group B\", \"Group C\"))\nSupportVectorMachine(y ~ x1 + x2 + x3, output = \"Prediction-Accuracy Table\")\n","lastSavedCode":"library(flipMultivariates)\nset.seed(23)\ny <- rep(1:3, rep(100,3))\nx0 <- y + rnorm(300, 0, .5)\nx1 <- x0\nx1[y == 2] <- x1[y == 2] - 1\nx2 <- y + rnorm(300,0, 1)\nx3 <- y + rnorm(300,0, 6)\ny <- factor(y, levels = 1:3, labels = c(\"Group A\", \"Group B\", \"Group C\"))\nSupportVectorMachine(y ~ x1 + x2 + x3, output = \"Prediction-Accuracy Table\")\n","highlightedCodeSpans":[],"tableTransformations":"<TabularTransformer>\r\n <TabularTransform type=\"Truncation\" truncationHeaderType=\"Column\" />\r\n <TabularTransform />\r\n</TabularTransformer>","tabularFilteringOptions":null,"hasGuiControls":false,"guiControls":{"Code":"","JSError":null,"JSErrorDetails":null,"ControlDefinitionErrors":null,"InputValidationErrors":null,"Controls":[{"ItemGuid":"00000000-0000-0000-0000-000000000000","ControlName":null,"Type":null,"Label":null,"Value":null,"Allowed":null,"EmptyListMessage":null,"Multi":false,"Prompt":null,"ErrorMessage":null,"Invalid":null,"Required":false,"AllowedTypes":null,"MinInputs":0,"MaxInputs":0,"Height":0,"Duplicates":false,"Values":null,"CheckAlign":null,"Text":null,"Increment":0.0,"Min":0.0,"Max":0.0,"Vertical":true}]},"calculating":"Idle","showDebug":false,"layout":"OutputOnly","size":{"Width":576,"Height":393},"vSplit":0.25,"hSplit":0.45,"updateWarnings":true,"updateMode":"Automatic","warnSlow":true,"outputSize":{"Width":576,"Height":392},"Options":{"debugconsole":false,"codeposition":"OutputOnly","size":{"width":576,"height":393},"splitH":0.45,"splitV":0.25,"update warnings":true,"updating":"Automatic","warn slow":true}}