EVOLUTION-MANAGER
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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) nls 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.nls {broom}"><tr><td>glance.nls {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) nls 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 'nls' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>An <code>nls</code> object returned from <code><a href="../../stats/html/nls.html">stats::nls()</a></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 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.residual</code></td> <td> <p>Residual degrees of freedom.</p> </td></tr> <tr valign="top"><td><code>finTol</code></td> <td> <p>The achieved convergence tolerance.</p> </td></tr> <tr valign="top"><td><code>isConv</code></td> <td> <p>Whether the fit successfully converged.</p> </td></tr> <tr valign="top"><td><code>logLik</code></td> <td> <p>The log-likelihood of the model. [stats::logLik()] may be a useful reference.</p> </td></tr> <tr valign="top"><td><code>nobs</code></td> <td> <p>Number of observations used.</p> </td></tr> <tr valign="top"><td><code>sigma</code></td> <td> <p>Estimated standard error of the residuals.</p> </td></tr> </table> <h3>See Also</h3> <p><a href="reexports.html">tidy</a>, <code><a href="../../stats/html/nls.html">stats::nls()</a></code> </p> <p>Other nls tidiers: <code><a href="augment.nls.html">augment.nls</a>()</code>, <code><a href="tidy.nls.html">tidy.nls</a>()</code> </p> <h3>Examples</h3> <pre> n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) tidy(n) augment(n) glance(n) library(ggplot2) ggplot(augment(n), aes(wt, mpg)) + geom_point() + geom_line(aes(y = .fitted)) newdata <- head(mtcars) newdata$wt <- newdata$wt + 1 augment(n, newdata = newdata) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>