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) lm 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.lm {broom}"><tr><td>glance.lm {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) lm 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 'lm' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>An <code>lm</code> object created by <code><a href="../../stats/html/lm.html">stats::lm()</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>adj.r.squared</code></td> <td> <p>Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.</p> </td></tr> <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>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>p.value</code></td> <td> <p>P-value corresponding to the test statistic.</p> </td></tr> <tr valign="top"><td><code>r.squared</code></td> <td> <p>R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.</p> </td></tr> <tr valign="top"><td><code>sigma</code></td> <td> <p>Estimated standard error of the residuals.</p> </td></tr> <tr valign="top"><td><code>statistic</code></td> <td> <p>Test statistic.</p> </td></tr> <tr valign="top"><td><code>df</code></td> <td> <p>The degrees for freedom from the numerator of the overall F-statistic. This is new in broom 0.7.0. Previously, this reported the rank of the design matrix, which is one more than the numerator degrees of freedom of the overall F-statistic.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">glance()</a></code> </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.svyglm.html">glance.svyglm</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(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod) glance(mod) # coefficient plot d <- tidy(mod) %>% mutate( low = estimate - std.error, high = estimate + std.error ) ggplot(d, aes(estimate, term, xmin = low, xmax = high, height = 0)) + geom_point() + geom_vline(xintercept = 0) + geom_errorbarh() augment(mod) augment(mod, mtcars) # predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata) au <- augment(mod, data = mtcars) ggplot(au, aes(.hat, .std.resid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE) plot(mod, which = 6) ggplot(au, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point() # column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>