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) factanal 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.factanal {broom}"><tr><td>glance.factanal {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) factanal 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 'factanal' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>factanal</code> object created by <code><a href="../../stats/html/factanal.html">stats::factanal()</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>converged</code></td> <td> <p>Logical indicating if the model fitting procedure was succesful and converged.</p> </td></tr> <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>method</code></td> <td> <p>Which method was used.</p> </td></tr> <tr valign="top"><td><code>n</code></td> <td> <p>The total number of observations.</p> </td></tr> <tr valign="top"><td><code>n.factors</code></td> <td> <p>The number of fitted factors.</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> <tr valign="top"><td><code>total.variance</code></td> <td> <p>Total cumulative proportion of variance accounted for by all factors.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">glance()</a></code>, <code><a href="../../stats/html/factanal.html">stats::factanal()</a></code> </p> <p>Other factanal tidiers: <code><a href="augment.factanal.html">augment.factanal</a>()</code>, <code><a href="tidy.factanal.html">tidy.factanal</a>()</code> </p> <h3>Examples</h3> <pre> set.seed(123) # data m1 <- dplyr::tibble( v1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 5, 6), v2 = c(1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 3, 4, 3, 3, 3, 4, 6, 5), v3 = c(3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 4, 6), v4 = c(3, 3, 4, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 5, 6, 4), v5 = c(1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 6, 4, 5), v6 = c(1, 1, 1, 2, 1, 3, 3, 3, 4, 3, 1, 1, 1, 2, 1, 6, 5, 4) ) # new data m2 <- purrr::map_dfr(m1, rev) # factor analysis objects fit1 <- stats::factanal(m1, factors = 3, scores = "Bartlett") fit2 <- stats::factanal(m1, factors = 3, scores = "regression") # tidying the object tidy(fit1) tidy(fit2) # augmented dataframe augment(fit1) augment(fit2) # augmented dataframe (with new data) augment(fit1, data = m2) augment(fit2, data = m2) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>