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: Augment data with information from a(n) drc 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.drc {broom}"><tr><td>augment.drc {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Augment data with information from a(n) drc object</h2> <h3>Description</h3> <p>Augment accepts a model object and a dataset and adds information about each observation in the dataset. Most commonly, this includes predicted values in the <code>.fitted</code> column, residuals in the <code>.resid</code> column, and standard errors for the fitted values in a <code>.se.fit</code> column. New columns always begin with a <code>.</code> prefix to avoid overwriting columns in the original dataset. </p> <p>Users may pass data to augment via either the <code>data</code> argument or the <code>newdata</code> argument. If the user passes data to the <code>data</code> argument, it <strong>must</strong> be exactly the data that was used to fit the model object. Pass datasets to <code>newdata</code> to augment data that was not used during model fitting. This still requires that all columns used to fit the model are present. </p> <p>Augment will often behave differently depending on whether <code>data</code> or <code>newdata</code> is given. This is because there is often information associated with training observations (such as influences or related) measures that is not meaningfully defined for new observations. </p> <p>For convenience, many augment methods provide default <code>data</code> arguments, so that <code>augment(fit)</code> will return the augmented training data. In these cases, augment tries to reconstruct the original data based on the model object with varying degrees of success. </p> <p>The augmented dataset is always returned as a <a href="../../tibble/html/tibble.html">tibble::tibble</a> with the <strong>same number of rows</strong> as the passed dataset. This means that the passed data must be coercible to a tibble. At this time, tibbles do not support matrix-columns. This means you should not specify a matrix of covariates in a model formula during the original model fitting process, and that <code><a href="../../splines/html/ns.html">splines::ns()</a></code>, <code><a href="../../stats/html/poly.html">stats::poly()</a></code> and <code><a href="../../survival/html/Surv.html">survival::Surv()</a></code> objects are not supported in input data. If you encounter errors, try explicitly passing a tibble, or fitting the original model on data in a tibble. </p> <p>We are in the process of defining behaviors for models fit with various <code>na.action</code> arguments, but make no guarantees about behavior when data is missing at this time. </p> <h3>Usage</h3> <pre> ## S3 method for class 'drc' augment( x, data = NULL, newdata = NULL, se_fit = FALSE, conf.int = FALSE, conf.level = 0.95, ... ) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>drc</code> object produced by a call to <code><a href="../../drc/html/drm.html">drc::drm()</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>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>...</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 confidence interval for fitted values.</p> </td></tr> <tr valign="top"><td><code>.conf.low</code></td> <td> <p>Lower bound on confidence interval for fitted values.</p> </td></tr> <tr valign="top"><td><code>.cooksd</code></td> <td> <p>Cooks distance.</p> </td></tr> <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><code><a href="reexports.html">augment()</a></code>, <code><a href="../../drc/html/drm.html">drc::drm()</a></code> </p> <p>Other drc tidiers: <code><a href="glance.drc.html">glance.drc</a>()</code>, <code><a href="tidy.drc.html">tidy.drc</a>()</code> </p> <h3>Examples</h3> <pre> library(drc) mod <- drm(dead / total ~ conc, type, weights = total, data = selenium, fct = LL.2(), type = "binomial" ) tidy(mod) tidy(mod, conf.int = TRUE) glance(mod) augment(mod, selenium) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>