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) cv.glmnet 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.cv.glmnet {broom}"><tr><td>glance.cv.glmnet {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Glance at a(n) cv.glmnet 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 'cv.glmnet' glance(x, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>cv.glmnet</code> object returned from <code><a href="../../glmnet/html/cv.glmnet.html">glmnet::cv.glmnet()</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>lambda.1se</code></td> <td> <p>The value of the penalization parameter lambda that results in the sparsest model while remaining within one standard error of the minimum loss.</p> </td></tr> <tr valign="top"><td><code>lambda.min</code></td> <td> <p>The value of the penalization parameter lambda that achieved minimum loss as estimated by cross validation.</p> </td></tr> <tr valign="top"><td><code>nobs</code></td> <td> <p>Number of observations used.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">glance()</a></code>, <code><a href="../../glmnet/html/cv.glmnet.html">glmnet::cv.glmnet()</a></code> </p> <p>Other glmnet tidiers: <code><a href="glance.glmnet.html">glance.glmnet</a>()</code>, <code><a href="tidy.cv.glmnet.html">tidy.cv.glmnet</a>()</code>, <code><a href="tidy.glmnet.html">tidy.glmnet</a>()</code> </p> <h3>Examples</h3> <pre> library(glmnet) set.seed(27) nobs <- 100 nvar <- 50 real <- 5 x <- matrix(rnorm(nobs * nvar), nobs, nvar) beta <- c(rnorm(real, 0, 1), rep(0, nvar - real)) y <- c(t(beta) %*% t(x)) + rnorm(nvar, sd = 3) cvfit1 <- cv.glmnet(x, y) tidy(cvfit1) glance(cvfit1) library(ggplot2) tidied_cv <- tidy(cvfit1) glance_cv <- glance(cvfit1) # plot of MSE as a function of lambda g <- ggplot(tidied_cv, aes(lambda, estimate)) + geom_line() + scale_x_log10() g # plot of MSE as a function of lambda with confidence ribbon g <- g + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25) g # plot of MSE as a function of lambda with confidence ribbon and choices # of minimum lambda marked g <- g + geom_vline(xintercept = glance_cv$lambda.min) + geom_vline(xintercept = glance_cv$lambda.1se, lty = 2) g # plot of number of zeros for each choice of lambda ggplot(tidied_cv, aes(lambda, nzero)) + geom_line() + scale_x_log10() # coefficient plot with min lambda shown tidied <- tidy(cvfit1$glmnet.fit) ggplot(tidied, aes(lambda, estimate, group = term)) + scale_x_log10() + geom_line() + geom_vline(xintercept = glance_cv$lambda.min) + geom_vline(xintercept = glance_cv$lambda.1se, 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>