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: Tidy a(n) loess 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 augment.loess {broom}"><tr><td>augment.loess {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Tidy a(n) loess 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> <h3>Usage</h3> <pre> ## S3 method for class 'loess' augment(x, data = model.frame(x), newdata = NULL, se_fit = FALSE, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>loess</code> objects returned by <code><a href="../../stats/html/loess.html">stats::loess()</a></code>.</p> </td></tr> <tr valign="top"><td><code>data</code></td> <td> <p>A <a href="../../base/html/data.frame.html">base::data.frame</a> or <code><a href="../../tibble/html/tibble.html">tibble::tibble()</a></code> containing the original data that was used to produce the object <code>x</code>. Defaults to <code>stats::model.frame(x)</code> so that <code>augment(my_fit)</code> returns the augmented original data. <strong>Do not</strong> pass new data to the <code>data</code> argument. Augment will report information such as influence and cooks distance for data passed to the <code>data</code> argument. These measures are only defined for the original training data.</p> </td></tr> <tr valign="top"><td><code>newdata</code></td> <td> <p>A <code><a href="../../base/html/data.frame.html">base::data.frame()</a></code> or <code><a href="../../tibble/html/tibble.html">tibble::tibble()</a></code> containing all the original predictors used to create <code>x</code>. Defaults to <code>NULL</code>, indicating that nothing has been passed to <code>newdata</code>. If <code>newdata</code> is specified, the <code>data</code> argument will be ignored.</p> </td></tr> <tr valign="top"><td><code>se_fit</code></td> <td> <p>Logical indicating whether or not a <code>.se.fit</code> column should be added to the augmented output. For some models, this calculation can be somwhat time-consuming. 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>Details</h3> <p>When the modeling was performed with <code>na.action = "na.omit"</code> (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with <code>na.action = "na.exclude"</code>, one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to <code><a href="reexports.html">augment()</a></code> and <code>na.action = "na.exclude"</code>, a warning is raised and the incomplete rows are dropped. </p> <p>Note that <code>loess</code> objects by default will not predict on data outside of a bounding hypercube defined by the training data unless the original <code>loess</code> object was fit with <code style="white-space: pre;">control = loess.control(surface = \"direct\"))</code>. See <code><a href="../../stats/html/predict.loess.html">stats::predict.loess()</a></code> for details. </p> <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>.fitted</code></td> <td> <p>Fitted or predicted value.</p> </td></tr> <tr valign="top"><td><code>.resid</code></td> <td> <p>The difference between observed and fitted values.</p> </td></tr> <tr valign="top"><td><code>.se.fit</code></td> <td> <p>Standard errors of fitted values.</p> </td></tr> </table> <h3>See Also</h3> <p><a href="../../stats/html/na.action.html">stats::na.action</a> </p> <p><code><a href="reexports.html">augment()</a></code>, <code><a href="../../stats/html/loess.html">stats::loess()</a></code>, <code><a href="../../stats/html/predict.loess.html">stats::predict.loess()</a></code> </p> <h3>Examples</h3> <pre> lo <- loess( mpg ~ hp + wt, mtcars, control = loess.control(surface = "direct") ) augment(lo) # with all columns of original data augment(lo, mtcars) # with a new dataset augment(lo, newdata = head(mtcars)) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>