EVOLUTION-MANAGER
Edit File: glance.fixest.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: Glance at a(n) fixest 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.fixest {broom}"><tr><td>glance.fixest {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) fixest 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 'fixest' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>fixest</code> object returned from any of the <code>fixest</code> estimators</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Additional arguments passed to <code>summary</code> and <code>confint</code>. Important arguments are <code>se</code> and <code>cluster</code>. Other arguments are <code>dof</code>, <code>exact_dof</code>, <code>forceCovariance</code>, and <code>keepBounded</code>. See <code><a href="../../fixest/html/summary.fixest.html">summary.fixest</a></code>.</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>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>pseudo.r.squared</code></td> <td> <p>Like the R squared statistic, but for situations when the R squared statistic isn't defined.</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>within.r.squared</code></td> <td> <p>R squared within fixed-effect groups.</p> </td></tr> </table> <h3>Note</h3> <p>The columns of the result depend on the type of model estimated. </p> <h3>Examples</h3> <pre> library(fixest) gravity <- feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product + Year, trade) tidy(gravity) glance(gravity) augment(gravity, trade) ## To get robust or clustered SEs, users can either: tidy(gravity, conf.int = TRUE, cluster = c("Product", "Year")) tidy(gravity, conf.int = TRUE, se = "threeway") # 2) Feed tidy() a summary.fixest object that has already accepted these arguments gravity_summ <- summary(gravity, cluster = c("Product", "Year")) tidy(gravity_summ, conf.int = TRUE) # Approach (1) is preferred. ## The other fixest methods all work similarly. For example: gravity_pois <- feglm(Euros ~ log(dist_km) | Origin + Destination + Product + Year, trade) tidy(gravity_pois) glance(gravity_pois) augment(gravity_pois, trade) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>