EVOLUTION-MANAGER
Edit File: nls.control.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: Control the Iterations in nls</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 nls.control {stats}"><tr><td>nls.control {stats}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Control the Iterations in nls</h2> <h3>Description</h3> <p>Allow the user to set some characteristics of the <code>nls</code> nonlinear least squares algorithm. </p> <h3>Usage</h3> <pre> nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = FALSE) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>maxiter</code></td> <td> <p>A positive integer specifying the maximum number of iterations allowed.</p> </td></tr> <tr valign="top"><td><code>tol</code></td> <td> <p>A positive numeric value specifying the tolerance level for the relative offset convergence criterion.</p> </td></tr> <tr valign="top"><td><code>minFactor</code></td> <td> <p>A positive numeric value specifying the minimum step-size factor allowed on any step in the iteration. The increment is calculated with a Gauss-Newton algorithm and successively halved until the residual sum of squares has been decreased or until the step-size factor has been reduced below this limit.</p> </td></tr> <tr valign="top"><td><code>printEval</code></td> <td> <p>a logical specifying whether the number of evaluations (steps in the gradient direction taken each iteration) is printed.</p> </td></tr> <tr valign="top"><td><code>warnOnly</code></td> <td> <p>a logical specifying whether <code><a href="nls.html">nls</a>()</code> should return instead of signalling an error in the case of termination before convergence. Termination before convergence happens upon completion of <code>maxiter</code> iterations, in the case of a singular gradient, and in the case that the step-size factor is reduced below <code>minFactor</code>.</p> </td></tr> </table> <h3>Value</h3> <p>A <code>list</code> with exactly five components: </p> <table summary="R valueblock"> <tr valign="top"><td><code>maxiter</code></td> <td> </td></tr> <tr valign="top"><td><code>tol</code></td> <td> </td></tr> <tr valign="top"><td><code>minFactor</code></td> <td> </td></tr> <tr valign="top"><td><code>printEval</code></td> <td> </td></tr> <tr valign="top"><td><code>warnOnly</code></td> <td> </td></tr> </table> <p>with meanings as explained under ‘Arguments’. </p> <h3>Author(s)</h3> <p>Douglas 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. </p> <h3>See Also</h3> <p><code><a href="nls.html">nls</a></code> </p> <h3>Examples</h3> <pre> nls.control(minFactor = 1/2048) </pre> <hr /><div style="text-align: center;">[Package <em>stats</em> version 3.6.0 <a href="00Index.html">Index</a>]</div> </body></html>