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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: Tidy a(n) poLCA 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 tidy.poLCA {broom}"><tr><td>tidy.poLCA {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Tidy a(n) poLCA object</h2> <h3>Description</h3> <p>Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return. </p> <h3>Usage</h3> <pre> ## S3 method for class 'poLCA' tidy(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>poLCA</code> object returned from <code><a href="../../poLCA/html/poLCA.html">poLCA::poLCA()</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 columns: </p> <table summary="R valueblock"> <tr valign="top"><td><code>class</code></td> <td> <p>The class under consideration.</p> </td></tr> <tr valign="top"><td><code>outcome</code></td> <td> <p>Outcome of manifest variable.</p> </td></tr> <tr valign="top"><td><code>std.error</code></td> <td> <p>The standard error of the regression term.</p> </td></tr> <tr valign="top"><td><code>variable</code></td> <td> <p>Manifest variable</p> </td></tr> <tr valign="top"><td><code>estimate</code></td> <td> <p>Estimated class-conditional response probability</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">tidy()</a></code>, <code><a href="../../poLCA/html/poLCA.html">poLCA::poLCA()</a></code> </p> <p>Other poLCA tidiers: <code><a href="augment.poLCA.html">augment.poLCA</a>()</code>, <code><a href="glance.poLCA.html">glance.poLCA</a>()</code> </p> <h3>Examples</h3> <pre> library(poLCA) library(dplyr) data(values) f <- cbind(A, B, C, D) ~ 1 M1 <- poLCA(f, values, nclass = 2, verbose = FALSE) M1 tidy(M1) augment(M1) glance(M1) library(ggplot2) ggplot(tidy(M1), aes(factor(class), estimate, fill = factor(outcome))) + geom_bar(stat = "identity", width = 1) + facet_wrap(~variable) ## Three-class model with a single covariate. data(election) f2a <- cbind( MORALG, CARESG, KNOWG, LEADG, DISHONG, INTELG, MORALB, CARESB, KNOWB, LEADB, DISHONB, INTELB ) ~ PARTY nes2a <- poLCA(f2a, election, nclass = 3, nrep = 5, verbose = FALSE) td <- tidy(nes2a) td # show ggplot(td, aes(outcome, estimate, color = factor(class), group = class)) + geom_line() + facet_wrap(~variable, nrow = 2) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) au <- augment(nes2a) au count(au, .class) # if the original data is provided, it leads to NAs in new columns # for rows that weren't predicted au2 <- augment(nes2a, data = election) au2 dim(au2) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>