<!DOCTYPE html> <html> <head><title>R: Feed-Forward Neural Networks and Multinomial Log-Linear Models</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body><div class="container"> <h1> Feed-Forward Neural Networks and Multinomial Log-Linear Models <img class="toplogo" src="../../../doc/html/Rlogo.svg" alt="[R logo]" /> </h1> <hr/> <div style="text-align: center;"> <a href="../../../doc/html/packages.html"><img class="arrow" src="../../../doc/html/left.jpg" alt="[Up]" /></a> <a href="../../../doc/html/index.html"><img class="arrow" src="../../../doc/html/up.jpg" alt="[Top]" /></a> </div><h2>Documentation for package ‘nnet’ version 7.3-19</h2> <ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li> <li><a href="../NEWS">Package NEWS</a>.</li> </ul> <h2>Help Pages</h2> <table style="width: 100%;"> <tr><td style="width: 25%;"><a href="nnet.html">add.net</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">add1.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">anova.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="class.ind.html">class.ind</a></td> <td>Generates Class Indicator Matrix from a Factor</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">coef.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">coef.nnet</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">drop1.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">eval.nn</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">extractAIC.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">logLik.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">model.frame.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">nnet</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">nnet.default</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">nnet.formula</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="nnet.Hess.html">nnetHess</a></td> <td>Evaluates Hessian for a Neural Network</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">norm.net</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">predict.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="predict.nnet.html">predict.nnet</a></td> <td>Predict New Examples by a Trained Neural Net</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">print.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">print.nnet</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">print.summary.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">print.summary.nnet</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">summary.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="nnet.html">summary.nnet</a></td> <td>Fit Neural Networks</td></tr> <tr><td style="width: 25%;"><a href="multinom.html">vcov.multinom</a></td> <td>Fit Multinomial Log-linear Models</td></tr> <tr><td style="width: 25%;"><a href="which.is.max.html">which.is.max</a></td> <td>Find Maximum Position in Vector</td></tr> </table> </div></body></html>