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: Method for Profiling nls Objects</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 profile.nls {stats}"><tr><td>profile.nls {stats}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Method for Profiling nls Objects</h2> <h3>Description</h3> <p>Investigates the profile log-likelihood function for a fitted model of class <code>"nls"</code>. </p> <h3>Usage</h3> <pre> ## S3 method for class 'nls' profile(fitted, which = 1:npar, maxpts = 100, alphamax = 0.01, delta.t = cutoff/5, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>fitted</code></td> <td> <p>the original fitted model object.</p> </td></tr> <tr valign="top"><td><code>which</code></td> <td> <p>the original model parameters which should be profiled. This can be a numeric or character vector. By default, all non-linear parameters are profiled.</p> </td></tr> <tr valign="top"><td><code>maxpts</code></td> <td> <p>maximum number of points to be used for profiling each parameter.</p> </td></tr> <tr valign="top"><td><code>alphamax</code></td> <td> <p>highest significance level allowed for the profile t-statistics.</p> </td></tr> <tr valign="top"><td><code>delta.t</code></td> <td> <p>suggested change on the scale of the profile t-statistics. Default value chosen to allow profiling at about 10 parameter values.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>further arguments passed to or from other methods.</p> </td></tr> </table> <h3>Details</h3> <p>The profile t-statistics is defined as the square root of change in sum-of-squares divided by residual standard error with an appropriate sign. </p> <h3>Value</h3> <p>A list with an element for each parameter being profiled. The elements are data-frames with two variables </p> <table summary="R valueblock"> <tr valign="top"><td><code>par.vals</code></td> <td> <p>a matrix of parameter values for each fitted model.</p> </td></tr> <tr valign="top"><td><code>tau</code></td> <td> <p>the profile t-statistics.</p> </td></tr> </table> <h3>Author(s)</h3> <p>Of the original version, Douglas M. Bates and Saikat DebRoy </p> <h3>References</h3> <p>Bates, D. M. and Watts, D. G. (1988), <em>Nonlinear Regression Analysis and Its Applications</em>, Wiley (chapter 6). </p> <h3>See Also</h3> <p><code><a href="nls.html">nls</a></code>, <code><a href="profile.html">profile</a></code>, <code><a href="plot.profile.nls.html">plot.profile.nls</a></code> </p> <h3>Examples</h3> <pre> # obtain the fitted object fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) # get the profile for the fitted model: default level is too extreme pr1 <- profile(fm1, alpha = 0.05) # profiled values for the two parameters pr1$A pr1$lrc # see also example(plot.profile.nls) </pre> <hr /><div style="text-align: center;">[Package <em>stats</em> version 3.6.0 <a href="00Index.html">Index</a>]</div> </body></html>