{"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'>&gt;library(flipRegression)\n</pre>\n<pre class='debug-source'>&gt;if(!(formType == &quot;Linear&quot;) || formMissing == &quot;Use partial data (pairwise correlations)&quot; || formMissing == &quot;Multiple imputation&quot;)\n formRobustSE = FALSE\n</pre>\n<pre class='debug-source'>&gt;if(formMissing != &quot;Multiple imputation&quot;)\n formAuxiliaryVariables = NULL\n</pre>\n<pre class='debug-source'>&gt;glm &lt;- Regression(sales ~ advertising, weights = QPopulationWeight, subset = QFilter, missing = formMissing, output = formOutput, robust.se = formRobustSE, type = formType, show.labels = !formNames, auxiliary = formAuxiliaryVariables)\n</pre>\r\n<div class=\"debug-summarystatistics\">\r\n<table>\r\n<tr><th>Total time:</th><td>1.10s</td></tr>\r\n<tr><th>Time on R server:</th><td title=\"rApacheServe 0.92s (pre 0.00s, post 0.00s) httpget_code() setup for eval 0.00s session$eval 0.90s (pre 0.00s, post 0.09s) unexplained 0.02s apparmor forking (pre 0.03s, post 0.00s)\">0.92s</td></tr>\r\n<tr><th>Time evaluating code:</th><td>0.77s</td></tr>\r\n<tr><th>Bytes sent:</th><td>2,598</td></tr>\r\n<tr><th>Bytes received:</th><td>15,810</td></tr>\r\n</table>\r\n</div>","htmlwidgets":"<div id=\"htmlwidget_container\">\n <div id=\"htmlwidget-ec29d9b70b75de54d568\" 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-ec29d9b70b75de54d568\">{\"x\":{\"html\":\"<table class = \\\"table table-condensed\\\"style = \\\"margin:0; border-bottom: 2px solid; border-top: 2px solid; font-size:90%;\\\">\\n<caption><h3 class=\\\".h3\\\" style=\\\"color:#3E7DCC; text-align:left; margin-top:0px; margin-bottom:0\\\">Linear Regression: sales\\u003c/h3>\\n<caption style=\\\"caption-side:bottom;font-style:italic; font-size:90%;\\\">n = 9 cases used in estimation; R-squared: 0.9786; Correct predictions: 88.89%; AIC: 111.47; \\u003c/caption>\\u003c/caption>\\n <thead>\\n <tr>\\n <th style=\\\"text-align:left;\\\"> \\u003c/th>\\n <th style=\\\"text-align:right;\\\"> Estimate \\u003c/th>\\n <th style=\\\"text-align:right;\\\"> Standard Error \\u003c/th>\\n <th style=\\\"text-align:right;\\\"> <span style='font-style:italic;'>t\\u003c/span> \\u003c/th>\\n <th style=\\\"text-align:right;\\\"> <span style='font-style:italic;'>p\\u003c/span> \\u003c/th>\\n \\u003c/tr>\\n \\u003c/thead>\\n<tbody>\\n <tr>\\n <td style=\\\"text-align:left;\\\"> (Intercept) \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"color:blue\\\">313.39\\u003c/span> \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> 109.91 \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"display: block; padding: 0 4px; border-radius: 4px; font-weight: bold; background-color: #b6d4f8\\\">2.85\\u003c/span> \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"font-weight: bold\\\">.025\\u003c/span> \\u003c/td>\\n \\u003c/tr>\\n <tr>\\n <td style=\\\"text-align:left;\\\"> advertising \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"color:blue\\\">23.64\\u003c/span> \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> 1.32 \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"display: block; padding: 0 4px; border-radius: 4px; font-weight: bold; background-color: #80b4f4\\\">17.89\\u003c/span> \\u003c/td>\\n <td style=\\\"text-align:right;\\\"> <span style=\\\"font-weight: bold\\\">&lt; .001\\u003c/span> \\u003c/td>\\n \\u003c/tr>\\n\\u003c/tbody>\\n\\u003c/table>\"},\"evals\":[],\"jsHooks\":[]}</script>\n<script 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== \"Linear\") || formMissing == \"Use partial data (pairwise correlations)\" || formMissing == \"Multiple imputation\")\n formRobustSE = FALSE\nif(formMissing != \"Multiple imputation\")\n formAuxiliaryVariables = NULL\nglm <- Regression(sales ~ advertising, weights = QPopulationWeight, subset = QFilter, missing = formMissing, output = formOutput, robust.se = formRobustSE, type = formType, show.labels = !formNames, auxiliary = formAuxiliaryVariables)","lastSavedCode":"library(flipRegression)\nif(!(formType == \"Linear\") || formMissing == \"Use partial data (pairwise correlations)\" || formMissing == \"Multiple imputation\")\n formRobustSE = FALSE\nif(formMissing != \"Multiple imputation\")\n formAuxiliaryVariables = NULL\nglm <- Regression(sales ~ advertising, weights = QPopulationWeight, subset = QFilter, missing = formMissing, output = formOutput, robust.se = formRobustSE, type = formType, show.labels = !formNames, auxiliary = formAuxiliaryVariables)","highlightedCodeSpans":[{"start":29,"length":8,"index":4},{"start":54,"length":11,"index":4},{"start":115,"length":11,"index":4},{"start":181,"length":11,"index":4},{"start":271,"length":5,"index":1},{"start":279,"length":11,"index":1},{"start":302,"length":17,"index":6},{"start":330,"length":7,"index":6},{"start":349,"length":11,"index":4},{"start":371,"length":10,"index":4},{"start":416,"length":8,"index":4},{"start":441,"length":9,"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\"})\nform.dropBox({label: \"Predictors\",\n types:[\"Variable: Numeric, Date, Money, Categorical, OrderedCategorical\"], \n name: 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>library(flipRegression)

>if(!(formType == "Linear") || formMissing == "Use partial data (pairwise correlations)" || formMissing == "Multiple imputation")
    formRobustSE = FALSE

>if(formMissing != "Multiple imputation")
    formAuxiliaryVariables = NULL

>glm <- Regression(sales ~ advertising, weights = QPopulationWeight, subset = QFilter, missing = formMissing, output = formOutput, robust.se = formRobustSE, type = formType, show.labels = !formNames, auxiliary = formAuxiliaryVariables)