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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: Glance at a(n) svyglm 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 glance.svyglm {broom}"><tr><td>glance.svyglm {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) svyglm object</h2> <h3>Description</h3> <p>Glance accepts a model object and returns a <code><a href="../../tibble/html/tibble.html">tibble::tibble()</a></code> with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information. </p> <p>Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function. </p> <p>Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as <code>NA</code>. </p> <p>Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an <code>NA</code> of the appropriate type. </p> <h3>Usage</h3> <pre> ## S3 method for class 'svyglm' glance(x, maximal = x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>svyglm</code> object returned from <code><a href="../../survey/html/svyglm.html">survey::svyglm()</a></code>.</p> </td></tr> <tr valign="top"><td><code>maximal</code></td> <td> <p>A <code>svyglm</code> object corresponding to the maximal model against which to compute the BIC. See Lumley and Scott (2015) for details. Defaults to <code>x</code>, which is equivalent to not using a maximal model.</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 exactly one row and columns: </p> <table summary="R valueblock"> <tr valign="top"><td><code>AIC</code></td> <td> <p>Akaike's Information Criterion for the model.</p> </td></tr> <tr valign="top"><td><code>BIC</code></td> <td> <p>Bayesian Information Criterion for the model.</p> </td></tr> <tr valign="top"><td><code>deviance</code></td> <td> <p>Deviance of the model.</p> </td></tr> <tr valign="top"><td><code>df.null</code></td> <td> <p>Degrees of freedom used by the null model.</p> </td></tr> <tr valign="top"><td><code>df.residual</code></td> <td> <p>Residual degrees of freedom.</p> </td></tr> <tr valign="top"><td><code>null.deviance</code></td> <td> <p>Deviance of the null model.</p> </td></tr> </table> <h3>References</h3> <p>Lumley T, Scott A (2015). AIC and BIC for modelling with complex survey data. <em>Journal of Survey Statistics and Methodology</em>, 3(1). <a href="https://doi.org/10.1093/jssam/smu021">https://doi.org/10.1093/jssam/smu021</a>. </p> <h3>See Also</h3> <p><code><a href="../../survey/html/svyglm.html">survey::svyglm()</a></code>, <code><a href="../../stats/html/glm.html">stats::glm()</a></code>, <a href="../../survey/html/anova.svyglm.html">survey::anova.svyglm</a> </p> <p>Other lm tidiers: <code><a href="augment.glm.html">augment.glm</a>()</code>, <code><a href="augment.lm.html">augment.lm</a>()</code>, <code><a href="glance.glm.html">glance.glm</a>()</code>, <code><a href="glance.lm.html">glance.lm</a>()</code>, <code><a href="tidy.glm.html">tidy.glm</a>()</code>, <code><a href="tidy.lm.beta.html">tidy.lm.beta</a>()</code>, <code><a href="tidy.lm.html">tidy.lm</a>()</code>, <code><a href="tidy.mlm.html">tidy.mlm</a>()</code> </p> <h3>Examples</h3> <pre> library(survey) set.seed(123) data(api) # survey design dstrat <- svydesign( id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc ) # model m <- survey::svyglm( formula = sch.wide ~ ell + meals + mobility, design = dstrat, family = quasibinomial() ) glance(m) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>