EVOLUTION-MANAGER
Edit File: augment.decomposed.ts.html
<!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) decomposed.ts 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.decomposed.ts {broom}"><tr><td>augment.decomposed.ts {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Augment data with information from a(n) decomposed.ts 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 'decomposed.ts' augment(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>decomposed.ts</code> object returned from <code><a href="../../stats/html/decompose.html">stats::decompose()</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 <a href="../../tibble/html/tibble.html">tibble::tibble</a> with one row for each observation in the original times series: </p> <table summary="R valueblock"> <tr valign="top"><td><code><code>.seasonal</code></code></td> <td> <p>The seasonal component of the decomposition.</p> </td></tr> <tr valign="top"><td><code><code>.trend</code></code></td> <td> <p>The trend component of the decomposition.</p> </td></tr> <tr valign="top"><td><code><code>.remainder</code></code></td> <td> <p>The remainder, or "random" component of the decomposition.</p> </td></tr> <tr valign="top"><td><code><code>.weight</code></code></td> <td> <p>The final robust weights (<code>stl</code> only).</p> </td></tr> <tr valign="top"><td><code><code>.seasadj</code></code></td> <td> <p>The seasonally adjusted (or "deseasonalised") series.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">augment()</a></code>, <code><a href="../../stats/html/decompose.html">stats::decompose()</a></code> </p> <p>Other decompose tidiers: <code><a href="augment.stl.html">augment.stl</a>()</code> </p> <h3>Examples</h3> <pre> # Time series of temperatures in Nottingham, 1920-1939: nottem # Perform seasonal decomposition on the data with both decompose # and stl: d1 <- stats::decompose(nottem) d2 <- stats::stl(nottem, s.window = "periodic", robust = TRUE) # Compare the original series to its decompositions. cbind( broom::tidy(nottem), broom::augment(d1), broom::augment(d2) ) # Visually compare seasonal decompositions in tidy data frames. library(tibble) library(dplyr) library(tidyr) library(ggplot2) decomps <- tibble( # Turn the ts objects into data frames. series = list(as.data.frame(nottem), as.data.frame(nottem)), # Add the models in, one for each row. decomp = c("decompose", "stl"), model = list(d1, d2) ) %>% rowwise() %>% # Pull out the fitted data using broom::augment. mutate(augment = list(broom::augment(model))) %>% ungroup() %>% # Unnest the data frames into a tidy arrangement of # the series next to its seasonal decomposition, grouped # by the method (stl or decompose). group_by(decomp) %>% unnest(c(series, augment)) %>% mutate(index = 1:n()) %>% ungroup() %>% select(decomp, index, x, adjusted = .seasadj) ggplot(decomps) + geom_line(aes(x = index, y = x), colour = "black") + geom_line(aes( x = index, y = adjusted, colour = decomp, group = decomp )) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>