{"size":{"Width":576,"Height":255},"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(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(Q5_5_1 ~ d3 + d1 + Q5_31_1, weights = QPopulationWeight, subset = QFilter, missing = formMissing, output = formOutput, robust.se = formRobustSE, type = formType, show.labels = !formNames, auxiliary = formAuxiliaryVariables)\n</pre>\n<pre class='debug-message'>Coefficient covariances computed by hccm()\n</pre>\n<pre class='debug-warning'>Unusual observations detected. The largest studentized residual is 4.39, which is significant, with a Bonferroni-corrected p-value of 0.01.\n\nThe largest hat value is 0.512, which is higher than the threshhold of 0.0734 = 2 * (k + 1) / n.\n</pre>\n<pre class='debug-warning'>The Variance Inflation Factor of the coefficients are: Gender: Female: 1; Age: 25 to 29: 1; Age: 30 to 34: 1; Age: 35 to 39: 1; Age: 40 to 44: 8; Age: 45 to 49: 1; Age: 50 to 54: 1; Age: 55 to 64: 1; Age: 65 or more: 1; Q5: weight-conscious - Coke: 1. A value of 4 or more indicates the confidence interval for the coefficient is\n twice as wide as they would be for uncorrelated predictors. A value of 10 or more\n indicates high multicollinearity.</pre>\n<pre class='debug-warning'>A Breusch Pagan Test for non-constant variance has been failed (p = &lt; 0.000000000001). A plot of the residuals versus the fitted values of the\n outcome variable may be useful (Regression &gt; Diagnostic &gt; Plot &gt; Residuals vs Fitted).\n A transformation of the outcome or predictor variables may solve this problem. Or, consider using Robust Standard Errors.\n</pre>\n<pre class='debug-warning'>The outcome variable contains only two unique values. A Binary Logit may be\n more appropriate.</pre>\r\n<div class=\"debug-summarystatistics\">\r\n<table>\r\n<tr><th>Total time:</th><td>3.20s</td></tr>\r\n<tr><th>Time on R server:</th><td title=\"rApacheServe 3.14s (pre 0.00s, post 0.00s) httpget_code() setup for eval 0.00s session$eval 3.04s (pre 0.00s, post 0.48s) unexplained 0.10s apparmor forking (pre 0.00s, post 0.00s)\">3.14s</td></tr>\r\n<tr><th>Time evaluating code:</th><td>2.48s</td></tr>\r\n<tr><th>Bytes sent:</th><td>4,720</td></tr>\r\n<tr><th>Bytes received:</th><td>111,747</td></tr>\r\n</table>\r\n</div>","message":"Coefficient covariances computed by hccm()\n","warning":"Unusual observations detected. The largest studentized residual is 4.39, which is significant, with a Bonferroni-corrected p-value of 0.01.\n\nThe largest hat value is 0.512, which is higher than the threshhold of 0.0734 = 2 * (k + 1) / n.\n\r\nThe Variance Inflation Factor of the coefficients are: Gender: Female: 1; Age: 25 to 29: 1; Age: 30 to 34: 1; Age: 35 to 39: 1; Age: 40 to 44: 8; Age: 45 to 49: 1; Age: 50 to 54: 1; Age: 55 to 64: 1; Age: 65 or more: 1; Q5: weight-conscious - Coke: 1. A value of 4 or more indicates the confidence interval for the coefficient is\n twice as wide as they would be for uncorrelated predictors. A value of 10 or more\n indicates high multicollinearity.\r\nA Breusch Pagan Test for non-constant variance has been failed (p = < 0.000000000001). A plot of the residuals versus the fitted values of the\n outcome variable may be useful (Regression > Diagnostic > Plot > Residuals vs Fitted).\n A transformation of the outcome or predictor variables may solve this problem. Or, consider using Robust Standard Errors.\n\r\nThe outcome variable contains only two unique values. A Binary Logit may be\n more appropriate.","htmlwidgets":"<div id=\"htmlwidget_container\">\n <div id=\"htmlwidget-7a523a021b8c2180d428\" 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-7a523a021b8c2180d428\">{\"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\\\">Analysis of Variance: Q5: feminine - Coke<\\/h3>\\n<h5 class=\\\".h5\\\" style=\\\"color:#888888; text-align:left; margin-top:5px; margin-bottom:0\\\">Linear Regression: Anova Table (Type II tests)<\\/h5>\\n<caption style=\\\"caption-side:bottom;font-style:italic; font-size:90%;\\\">n = 327 cases used in estimation; R-squared: 0.06934; Correct predictions: 93.88%; AIC: 9.0246; multiple comparisons 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