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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Tidying methods for multinomial logistic regression models</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for tidy.multinom {broom}"><tr><td>tidy.multinom {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Tidying methods for multinomial logistic regression models</h2> <h3>Description</h3> <p>These methods tidy the coefficients of multinomial logistic regression models generated by <code>multinom</code> of the <code>nnet</code> package. </p> <h3>Usage</h3> <pre> ## S3 method for class 'multinom' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>multinom</code> object returned from <code><a href="../../nnet/html/multinom.html">nnet::multinom()</a></code>.</p> </td></tr> <tr valign="top"><td><code>conf.int</code></td> <td> <p>Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to <code>FALSE</code>.</p> </td></tr> <tr valign="top"><td><code>conf.level</code></td> <td> <p>The confidence level to use for the confidence interval if <code>conf.int = TRUE</code>. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.</p> </td></tr> <tr valign="top"><td><code>exponentiate</code></td> <td> <p>Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to <code>FALSE</code>.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Additional arguments. Not used. Needed to match generic signature only. <strong>Cautionary note:</strong> Misspelled arguments will be absorbed in <code>...</code>, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass <code>conf.lvel = 0.9</code>, all computation will proceed using <code>conf.level = 0.95</code>. Additionally, if you pass <code>newdata = my_tibble</code> to an <code><a href="reexports.html">augment()</a></code> method that does not accept a <code>newdata</code> argument, it will use the default value for the <code>data</code> argument.</p> </td></tr> </table> <h3>Value</h3> <p>A <code><a href="../../tibble/html/tibble.html">tibble::tibble()</a></code> with columns: </p> <table summary="R valueblock"> <tr valign="top"><td><code>conf.high</code></td> <td> <p>Upper bound on the confidence interval for the estimate.</p> </td></tr> <tr valign="top"><td><code>conf.low</code></td> <td> <p>Lower bound on the confidence interval for the estimate.</p> </td></tr> <tr valign="top"><td><code>estimate</code></td> <td> <p>The estimated value of the regression term.</p> </td></tr> <tr valign="top"><td><code>p.value</code></td> <td> <p>The two-sided p-value associated with the observed statistic.</p> </td></tr> <tr valign="top"><td><code>statistic</code></td> <td> <p>The value of a T-statistic to use in a hypothesis that the regression term is non-zero.</p> </td></tr> <tr valign="top"><td><code>std.error</code></td> <td> <p>The standard error of the regression term.</p> </td></tr> <tr valign="top"><td><code>term</code></td> <td> <p>The name of the regression term.</p> </td></tr> <tr valign="top"><td><code>y.value</code></td> <td> <p>The response level.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">tidy()</a></code>, <code><a href="../../nnet/html/multinom.html">nnet::multinom()</a></code> </p> <p>Other multinom tidiers: <code><a href="glance.multinom.html">glance.multinom</a>()</code> </p> <h3>Examples</h3> <pre> library(nnet) library(MASS) example(birthwt) bwt.mu <- multinom(low ~ ., bwt) tidy(bwt.mu) glance(bwt.mu) #* This model is a truly terrible model #* but it should show you what the output looks #* like in a multinomial logistic regression fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars) tidy(fit.gear) glance(fit.gear) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>