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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: Tidy a(n) ergm object</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.ergm {broom}"><tr><td>tidy.ergm {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Tidy a(n) ergm object</h2> <h3>Description</h3> <p>Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return. </p> <p>The methods should work with any model that conforms to the <span class="pkg">ergm</span> class, such as those produced from weighted networks by the <span class="pkg">ergm.count</span> package. </p> <h3>Usage</h3> <pre> ## S3 method for class 'ergm' 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>An <code>ergm</code> object returned from a call to <code><a href="../../ergm/html/ergm.html">ergm::ergm()</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 to pass to <code><a href="../../ergm/html/summary.formula.html">ergm::summary()</a></code>. <strong>Cautionary note</strong>: Mispecified arguments may be silently ignored.</p> </td></tr> </table> <h3>Value</h3> <p>A <a href="../../tibble/html/tibble.html">tibble::tibble</a> with one row for each coefficient in the exponential random graph model, with columns: </p> <table summary="R valueblock"> <tr valign="top"><td><code>term</code></td> <td> <p>The term in the model being estimated and tested</p> </td></tr> <tr valign="top"><td><code>estimate</code></td> <td> <p>The estimated coefficient</p> </td></tr> <tr valign="top"><td><code>std.error</code></td> <td> <p>The standard error</p> </td></tr> <tr valign="top"><td><code>mcmc.error</code></td> <td> <p>The MCMC error</p> </td></tr> <tr valign="top"><td><code>p.value</code></td> <td> <p>The two-sided p-value</p> </td></tr> </table> <h3>References</h3> <p>Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). <span class="pkg">ergm</span>: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. <em>Journal of Statistical Software</em>, 24(3). <a href="http://www.jstatsoft.org/v24/i03/">http://www.jstatsoft.org/v24/i03/</a>. </p> <h3>See Also</h3> <p><code><a href="reexports.html">tidy()</a></code>, <code><a href="../../ergm/html/ergm.html">ergm::ergm()</a></code>, <code><a href="../../ergm/html/control.ergm.html">ergm::control.ergm()</a></code>, <code><a href="../../ergm/html/summary.formula.html">ergm::summary()</a></code> </p> <p>Other ergm tidiers: <code><a href="glance.ergm.html">glance.ergm</a>()</code> </p> <h3>Examples</h3> <pre> library(ergm) # Using the same example as the ergm package # Load the Florentine marriage network data data(florentine) # Fit a model where the propensity to form ties between # families depends on the absolute difference in wealth gest <- ergm(flomarriage ~ edges + absdiff("wealth")) # Show terms, coefficient estimates and errors tidy(gest) # Show coefficients as odds ratios with a 99% CI tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99) # Take a look at likelihood measures and other # control parameters used during MCMC estimation glance(gest) glance(gest, deviance = TRUE) glance(gest, mcmc = TRUE) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>