EVOLUTION-MANAGER
Edit File: glance.gmm.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) gmm 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.gmm {broom}"><tr><td>glance.gmm {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) gmm 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 'gmm' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>gmm</code> object returned from <code><a href="../../gmm/html/gmm.html">gmm::gmm()</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>df</code></td> <td> <p>Degrees of freedom used by 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>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>statistic</code></td> <td> <p>Test statistic.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">glance()</a></code>, <code><a href="../../gmm/html/gmm.html">gmm::gmm()</a></code> </p> <p>Other gmm tidiers: <code><a href="tidy.gmm.html">tidy.gmm</a>()</code> </p> <h3>Examples</h3> <pre> library(gmm) # examples come from the "gmm" package ## CAPM test with GMM data(Finance) r <- Finance[1:300, 1:10] rm <- Finance[1:300, "rm"] rf <- Finance[1:300, "rf"] z <- as.matrix(r - rf) t <- nrow(z) zm <- rm - rf h <- matrix(zm, t, 1) res <- gmm(z ~ zm, x = h) # tidy result tidy(res) tidy(res, conf.int = TRUE) tidy(res, conf.int = TRUE, conf.level = .99) # coefficient plot library(ggplot2) library(dplyr) tidy(res, conf.int = TRUE) %>% mutate(variable = reorder(term, estimate)) %>% ggplot(aes(estimate, variable)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_vline(xintercept = 0, color = "red", lty = 2) # from a function instead of a matrix g <- function(theta, x) { e <- x[, 2:11] - theta[1] - (x[, 1] - theta[1]) %*% matrix(theta[2:11], 1, 10) gmat <- cbind(e, e * c(x[, 1])) return(gmat) } x <- as.matrix(cbind(rm, r)) res_black <- gmm(g, x = x, t0 = rep(0, 11)) tidy(res_black) tidy(res_black, conf.int = TRUE) ## APT test with Fama-French factors and GMM f1 <- zm f2 <- Finance[1:300, "hml"] - rf f3 <- Finance[1:300, "smb"] - rf h <- cbind(f1, f2, f3) res2 <- gmm(z ~ f1 + f2 + f3, x = h) td2 <- tidy(res2, conf.int = TRUE) td2 # coefficient plot td2 %>% mutate(variable = reorder(term, estimate)) %>% ggplot(aes(estimate, variable)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_vline(xintercept = 0, color = "red", lty = 2) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>