{"size":{"Width":576,"Height":325},"appearance":{"background":null,"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'>&gt;library(flipMultivariates)\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;model.2 &lt;- MachineLearning(formula = OverallSatisfaction ~ Fees + Interestrates + Phoneservice + Branchservice + Onlineservice + ATMavailability,\n algorithm = formAlgorithm,\n weights = QPopulationWeight, subset = QFilter,\n missing = formMissing, output = formOutput, show.labels = !formNames,\n seed = get0(&quot;formSeed&quot;),\n cost = get0(&quot;formCost&quot;),\n booster = get0(&quot;formBooster&quot;),\n grid.search = get0(&quot;formSearch&quot;),\n sort.by.importance = get0(&quot;formImportance&quot;),\n hidden.nodes = get0(&quot;formHiddenLayers&quot;),\n max.epochs = get0(&quot;formEpochs&quot;),\n normalize = get0(&quot;formNormalize&quot;),\n outcome.color = get0(&quot;formOutColor&quot;),\n predictors.color = get0(&quot;formPredColor&quot;),\n prior = get0(&quot;formPrior&quot;),\n prune = get0(&quot;formPruning&quot;),\n early.stopping = get0(&quot;formStopping&quot;),\n predictor.level.treatment = get0(&quot;formPredictorCategoryLabels&quot;),\n outcome.level.treatment = get0(&quot;formOutcomeCategoryLabels&quot;),\n long.running.calculations = get0(&quot;formLongRunningCalculations&quot;),\n type = get0(&quot;formRegressionType&quot;),\n auxiliary.data = get0(&quot;formAuxiliaryVariables&quot;),\n correction = get0(&quot;formCorrection&quot;),\n robust.se = get0(&quot;formRobustSE&quot;, ifnotfound = FALSE),\n importance.absolute = get0(&quot;formAbsoluteImportance&quot;),\n interaction = get0(&quot;formInteraction&quot;),\n relative.importance = formOutput == &quot;Relative Importance Analysis&quot;)\n</pre>\n<pre class='debug-warning'>Unusual observations detected. The largest hat value is 0.025, which is higher than the threshhold of 0.0179 = 2 * (k + 1) / n.\n</pre>\n<pre class='debug-warning'>The outcome variable appears to contain categories (i.e., the values are non-negative integers). A limited dependent variable regression may be more appropriate (e.g., Ordered Logit for ordered categories, Multinomial Logit for unordered categories, Quasi-Poisson Regression for counts).</pre>\r\n<div class=\"debug-summarystatistics\"><table>\r\n<tr><th>Started:</th><td class=utc-time>2018-09-07T00:00:07.000Z</td></tr>\r\n<tr><th>Finished:</th><td class=utc-time>2018-09-07T00:00:00.000Z</td></tr>\r\n<tr><th>Total time:</th><td>-7.36s</td></tr>\r\n<tr><th>Time executing code:</th><td title=\"2h 2m 29.64s\">-7357.00s</td></tr>\r\n<tr><th>Other overhead on R server:</th><td>0.00s</td></tr>\r\n<tr><th>Time spent transferring data:</th><td>2h 2m 29.64s</td></tr>\r\n<tr><th>Data sent to R server:</th><td>-7.2KB</td></tr>\r\n<tr><th>Data received from R server:</th><td>-7.2KB</td></tr>\r\n</table></div>","warning":"Unusual observations detected. The largest hat value is 0.025, which is higher than the threshhold of 0.0179 = 2 * (k + 1) / n.\n\r\nThe outcome variable appears to contain categories (i.e., the values are non-negative integers). A limited dependent variable regression may be more appropriate (e.g., Ordered Logit for ordered categories, Multinomial Logit for unordered categories, Quasi-Poisson Regression for counts).","htmlwidgets":"<div id=\"htmlwidget_container\">\n <div id=\"htmlwidget-da69b3299e57b0e20ec7\" class=\"formattable_widget html-widget\" style=\"width:100%;height:500px;\" width=\"100%\" height=\"500\"></div>\n</div>\n<script type=\"application/json\" data-for=\"htmlwidget-da69b3299e57b0e20ec7\">{\"x\":{\"html\":\"<table class = \\\"table table-condensed\\\"style = \\\"margin:0; border-bottom: 2px solid; border-top: 2px solid; font-size:90%;\\\">\\n<h3 class=\\\".h3\\\" style=\\\"color:#3E7DCC; text-align:left; margin-top:0px; margin-bottom:0\\\">Linear Regression: Overall Satisfaction<\\/h3>\\n<caption style=\\\"caption-side:bottom;font-style:italic; font-size:90%;\\\">n = 896 cases used in estimation; R-squared: 0.5222; Correct predictions: 53.68%; AIC: 1,877.7; multiple comparisons correction: None<\\/caption>\\n <thead>\\n <tr>\\n <th style=\\\"text-align:left;\\\"> <\\/th>\\n <th style=\\\"text-align:right;\\\"> Estimate <\\/th>\\n <th style=\\\"text-align:right;\\\"> Standard Error <\\/th>\\n <th style=\\\"text-align:right;\\\"> <span style='font-style:italic;'>t<\\/span> <\\/th>\\n <th style=\\\"text-align:right;\\\"> <span style='font-style:italic;'>p<\\/span> <\\/th>\\n <\\/tr>\\n <\\/thead>\\n<tbody>\\n <tr>\\n <td style=\\\"vertical-align:middle; text-align:left;\\\"> (Intercept) <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"color:red\\\">-2.26<\\/span> <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> 0.21 <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"display: block; padding: 0 4px; border-radius: 4px; font-weight: bold; background-color: #fb9080\\\">-10.92<\\/span> <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"font-weight: 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Branch service <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"color:blue\\\">0.38<\\/span> <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> 0.02 <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"display: block; padding: 0 4px; border-radius: 4px; font-weight: bold; background-color: #80b4f4\\\">17.64<\\/span> <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"font-weight: bold\\\">&lt; .001<\\/span> <\\/td>\\n <\\/tr>\\n <tr>\\n <td style=\\\"vertical-align:middle; text-align:left;\\\"> Online service <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"color:blue\\\">0.18<\\/span> <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> 0.02 <\\/td>\\n <td style=\\\"vertical-align:middle; text-align:right;\\\"> <span style=\\\"display: block; padding: 0 4px; border-radius: 4px; font-weight: bold; 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<- MachineLearning(formula = OverallSatisfaction ~ Fees + Interestrates + Phoneservice + Branchservice + Onlineservice + ATMavailability,\n algorithm = formAlgorithm,\n weights = QPopulationWeight, subset = QFilter,\n missing = formMissing, output = formOutput, show.labels = !formNames,\n seed = get0(\"formSeed\"),\n cost = get0(\"formCost\"),\n booster = get0(\"formBooster\"),\n grid.search = get0(\"formSearch\"),\n sort.by.importance = get0(\"formImportance\"),\n hidden.nodes = get0(\"formHiddenLayers\"),\n max.epochs = get0(\"formEpochs\"),\n normalize = get0(\"formNormalize\"),\n outcome.color = get0(\"formOutColor\"),\n predictors.color = get0(\"formPredColor\"),\n prior = get0(\"formPrior\"),\n prune = get0(\"formPruning\"),\n early.stopping = get0(\"formStopping\"),\n predictor.level.treatment = get0(\"formPredictorCategoryLabels\"),\n outcome.level.treatment = get0(\"formOutcomeCategoryLabels\"),\n long.running.calculations = get0(\"formLongRunningCalculations\"),\n type = get0(\"formRegressionType\"),\n auxiliary.data = get0(\"formAuxiliaryVariables\"),\n correction = get0(\"formCorrection\"),\n robust.se = get0(\"formRobustSE\", ifnotfound = FALSE),\n importance.absolute = get0(\"formAbsoluteImportance\"),\n interaction = get0(\"formInteraction\"),\n relative.importance = formOutput == \"Relative Importance Analysis\")","lastSavedCode":"library(flipMultivariates)\n\nmodel.2 <- MachineLearning(formula = OverallSatisfaction ~ Fees + Interestrates + Phoneservice + Branchservice + Onlineservice + ATMavailability,\n algorithm = formAlgorithm,\n weights = QPopulationWeight, subset = QFilter,\n missing = formMissing, output = formOutput, show.labels = !formNames,\n seed = get0(\"formSeed\"),\n cost = get0(\"formCost\"),\n booster = get0(\"formBooster\"),\n grid.search = get0(\"formSearch\"),\n sort.by.importance = get0(\"formImportance\"),\n hidden.nodes = get0(\"formHiddenLayers\"),\n max.epochs = get0(\"formEpochs\"),\n normalize = get0(\"formNormalize\"),\n outcome.color = get0(\"formOutColor\"),\n predictors.color = get0(\"formPredColor\"),\n prior = get0(\"formPrior\"),\n prune = get0(\"formPruning\"),\n early.stopping = get0(\"formStopping\"),\n predictor.level.treatment = get0(\"formPredictorCategoryLabels\"),\n outcome.level.treatment = get0(\"formOutcomeCategoryLabels\"),\n long.running.calculations = get0(\"formLongRunningCalculations\"),\n type = get0(\"formRegressionType\"),\n auxiliary.data = get0(\"formAuxiliaryVariables\"),\n correction = get0(\"formCorrection\"),\n robust.se = get0(\"formRobustSE\", ifnotfound = FALSE),\n importance.absolute = get0(\"formAbsoluteImportance\"),\n interaction = get0(\"formInteraction\"),\n relative.importance = formOutput == \"Relative Importance Analysis\")","highlightedCodeSpans":[{"start":65,"length":19,"index":0},{"start":87,"length":4,"index":1},{"start":94,"length":13,"index":0},{"start":110,"length":12,"index":0},{"start":125,"length":13,"index":0},{"start":141,"length":13,"index":0},{"start":157,"length":15,"index":0},{"start":222,"length":13,"index":4},{"start":283,"length":17,"index":6},{"start":311,"length":7,"index":6},{"start":366,"length":11,"index":4},{"start":388,"length":10,"index":4},{"start":415,"length":9,"index":4},{"start":2179,"length":10,"index":4}],"tableTransformations":"<TabularTransformer>\r\n <TabularTransform type=\"Truncation\" truncationHeaderType=\"Column\" />\r\n <TabularTransform />\r\n</TabularTransformer>","tabularFilteringOptions":null,"hasGuiControls":true,"guiControls":{"Code":"form.dropBox({label: \"Outcome\", \n types:[\"Variable: Numeric, Date, Money, Categorical, OrderedCategorical\"], \n name: \"formOutcomeVariable\",\n prompt: \"Independent target variable to be 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output used to show the results\",\n alternatives: output_options, name: \"formOutput\", default_value: output_options[0]}).getValue();\nvar missing = form.comboBox({label: \"Missing data\", \n alternatives: missing_data_options, name: \"formMissing\", default_value: \"Exclude cases with missing data\",\n prompt: \"Options for handling cases with missing data\"}).getValue();\nform.checkBox({label: \"Variable names\", name: \"formNames\", default_value: false, prompt: \"Display names instead of labels\"});\n\n// CONTROLS FOR SPECIFIC ALGORITHMS\n\nif (algorithm == \"Support Vector Machine\")\n form.textBox({label: \"Cost\", name: \"formCost\", default_value: 1, type: \"number\",\n prompt: \"High cost produces a complex model with risk of overfitting, low cost produces a simpler mode with risk of underfitting\"});\n\nif (algorithm == \"Gradient Boosting\") {\n form.comboBox({label: \"Booster\", \n alternatives: [\"gbtree\", \"gblinear\"], name: \"formBooster\", default_value: 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form.colorPicker({label: \"Outcome color\", name: \"formOutColor\", default_value:\"#5B9BD5\"});\n form.colorPicker({label: \"Predictors color\", name: \"formPredColor\", default_value:\"#ED7D31\"});\n }\n form.comboBox({label: \"Prior\", alternatives: [\"Equal\", \"Observed\",], name: \"formPrior\", default_value: \"Observed\",\n prompt: \"Probabilities of group membership\"})\n}\n\nif (algorithm == \"CART\") {\n form.comboBox({label: \"Pruning\", alternatives: [\"Minimum error\", \"Smallest tree\", \"None\"], \n name: \"formPruning\", default_value: \"Minimum error\",\n prompt: \"Remove nodes after tree has been built\"})\n form.checkBox({label: \"Early stopping\", name: \"formStopping\", default_value: false,\n prompt: \"Stop building tree when fit does not improve\"});\n form.comboBox({label: \"Predictor category labels\", alternatives: [\"Full labels\", \"Abbreviated labels\", \"Letters\"],\n name: \"formPredictorCategoryLabels\", default_value: \"Abbreviated labels\",\n prompt: 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