EVOLUTION-MANAGER
Edit File: glance.mjoint.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) mjoint 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.mjoint {broom}"><tr><td>glance.mjoint {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) mjoint 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 'mjoint' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>An <code>mjoint</code> object returned from <code><a href="../../joineRML/html/mjoint.html">joineRML::mjoint()</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>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>sigma2_j</code></td> <td> <p>The square root of the estimated residual variance for the j-th longitudinal process</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">glance()</a></code>, <code><a href="../../joineRML/html/mjoint.html">joineRML::mjoint()</a></code> </p> <p>Other mjoint tidiers: <code><a href="tidy.mjoint.html">tidy.mjoint</a>()</code> </p> <h3>Examples</h3> <pre> ## Not run: # Fit a joint model with bivariate longitudinal outcomes library(joineRML) data(heart.valve) hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi) & heart.valve$num <= 50, ] fit <- mjoint( formLongFixed = list( "grad" = log.grad ~ time + sex + hs, "lvmi" = log.lvmi ~ time + sex ), formLongRandom = list( "grad" = ~ 1 | num, "lvmi" = ~ time | num ), formSurv = Surv(fuyrs, status) ~ age, data = hvd, inits = list("gamma" = c(0.11, 1.51, 0.80)), timeVar = "time" ) # Extract the survival fixed effects tidy(fit) # Extract the longitudinal fixed effects tidy(fit, component = "longitudinal") # Extract the survival fixed effects with confidence intervals tidy(fit, ci = TRUE) # Extract the survival fixed effects with confidence intervals based # on bootstrapped standard errors bSE <- bootSE(fit, nboot = 5, safe.boot = TRUE) tidy(fit, boot_se = bSE, ci = TRUE) # Augment original data with fitted longitudinal values and residuals hvd2 <- augment(fit) # Extract model statistics glance(fit) ## End(Not run) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>