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: Tidy a(n) 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 tidy.glmnet {broom}"><tr><td>tidy.glmnet {broom}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Tidy a(n) glmnet 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 'glmnet' tidy(x, return_zeros = FALSE, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>x</code></td> <td> <p>A <code>glmnet</code> object returned from <code><a href="../../glmnet/html/glmnet.html">glmnet::glmnet()</a></code>.</p> </td></tr> <tr valign="top"><td><code>return_zeros</code></td> <td> <p>Logical indicating whether coefficients with value zero zero should be included in the results. Defaults to <code>FALSE</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>Details</h3> <p>Note that while this representation of GLMs is much easier to plot and combine than the default structure, it is also much more memory-intensive. Do not use for large, sparse matrices. </p> <p>No <code>augment</code> method is yet provided even though the model produces predictions, because the input data is not tidy (it is a matrix that may be very wide) and therefore combining predictions with it is not logical. Furthermore, predictions make sense only with a specific choice of lambda. </p> <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>dev.ratio</code></td> <td> <p>Fraction of null deviance explained at each value of lambda.</p> </td></tr> <tr valign="top"><td><code>estimate</code></td> <td> <p>The estimated value of the regression term.</p> </td></tr> <tr valign="top"><td><code>lambda</code></td> <td> <p>Value of penalty parameter lambda.</p> </td></tr> <tr valign="top"><td><code>step</code></td> <td> <p>Which step of lambda choices was used.</p> </td></tr> <tr valign="top"><td><code>term</code></td> <td> <p>The name of the regression term.</p> </td></tr> </table> <h3>See Also</h3> <p><code><a href="reexports.html">tidy()</a></code>, <code><a href="../../glmnet/html/glmnet.html">glmnet::glmnet()</a></code> </p> <p>Other glmnet tidiers: <code><a href="glance.cv.glmnet.html">glance.cv.glmnet</a>()</code>, <code><a href="glance.glmnet.html">glance.glmnet</a>()</code>, <code><a href="tidy.cv.glmnet.html">tidy.cv.glmnet</a>()</code> </p> <h3>Examples</h3> <pre> library(glmnet) set.seed(2014) x <- matrix(rnorm(100 * 20), 100, 20) y <- rnorm(100) fit1 <- glmnet(x, y) tidy(fit1) glance(fit1) library(dplyr) library(ggplot2) tidied <- tidy(fit1) %>% filter(term != "(Intercept)") ggplot(tidied, aes(step, estimate, group = term)) + geom_line() ggplot(tidied, aes(lambda, estimate, group = term)) + geom_line() + scale_x_log10() ggplot(tidied, aes(lambda, dev.ratio)) + geom_line() # works for other types of regressions as well, such as logistic g2 <- sample(1:2, 100, replace = TRUE) fit2 <- glmnet(x, g2, family = "binomial") tidy(fit2) </pre> <hr /><div style="text-align: center;">[Package <em>broom</em> version 0.7.0 <a href="00Index.html">Index</a>]</div> </body></html>