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: Classify Multivariate Observations by Linear Discrimination</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 predict.lda {MASS}"><tr><td>predict.lda {MASS}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2> Classify Multivariate Observations by Linear Discrimination </h2> <h3>Description</h3> <p>Classify multivariate observations in conjunction with <code>lda</code>, and also project data onto the linear discriminants. </p> <h3>Usage</h3> <pre> ## S3 method for class 'lda' predict(object, newdata, prior = object$prior, dimen, method = c("plug-in", "predictive", "debiased"), ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>object</code></td> <td> <p>object of class <code>"lda"</code> </p> </td></tr> <tr valign="top"><td><code>newdata</code></td> <td> <p>data frame of cases to be classified or, if <code>object</code> has a formula, a data frame with columns of the same names as the variables used. A vector will be interpreted as a row vector. If newdata is missing, an attempt will be made to retrieve the data used to fit the <code>lda</code> object. </p> </td></tr> <tr valign="top"><td><code>prior</code></td> <td> <p>The prior probabilities of the classes, by default the proportions in the training set or what was set in the call to <code>lda</code>. </p> </td></tr> <tr valign="top"><td><code>dimen</code></td> <td> <p>the dimension of the space to be used. If this is less than <code>min(p, ng-1)</code>, only the first <code>dimen</code> discriminant components are used (except for <code>method="predictive"</code>), and only those dimensions are returned in <code>x</code>. </p> </td></tr> <tr valign="top"><td><code>method</code></td> <td> <p>This determines how the parameter estimation is handled. With <code>"plug-in"</code> (the default) the usual unbiased parameter estimates are used and assumed to be correct. With <code>"debiased"</code> an unbiased estimator of the log posterior probabilities is used, and with <code>"predictive"</code> the parameter estimates are integrated out using a vague prior. </p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>arguments based from or to other methods </p> </td></tr></table> <h3>Details</h3> <p>This function is a method for the generic function <code>predict()</code> for class <code>"lda"</code>. It can be invoked by calling <code>predict(x)</code> for an object <code>x</code> of the appropriate class, or directly by calling <code>predict.lda(x)</code> regardless of the class of the object. </p> <p>Missing values in <code>newdata</code> are handled by returning <code>NA</code> if the linear discriminants cannot be evaluated. If <code>newdata</code> is omitted and the <code>na.action</code> of the fit omitted cases, these will be omitted on the prediction. </p> <p>This version centres the linear discriminants so that the weighted mean (weighted by <code>prior</code>) of the group centroids is at the origin. </p> <h3>Value</h3> <p>a list with components </p> <table summary="R valueblock"> <tr valign="top"><td><code>class</code></td> <td> <p>The MAP classification (a factor) </p> </td></tr> <tr valign="top"><td><code>posterior</code></td> <td> <p>posterior probabilities for the classes </p> </td></tr> <tr valign="top"><td><code>x</code></td> <td> <p>the scores of test cases on up to <code>dimen</code> discriminant variables </p> </td></tr></table> <h3>References</h3> <p>Venables, W. N. and Ripley, B. D. (2002) <em>Modern Applied Statistics with S.</em> Fourth edition. Springer. </p> <p>Ripley, B. D. (1996) <em>Pattern Recognition and Neural Networks</em>. Cambridge University Press. </p> <h3>See Also</h3> <p><code><a href="lda.html">lda</a></code>, <code><a href="qda.html">qda</a></code>, <code><a href="predict.qda.html">predict.qda</a></code> </p> <h3>Examples</h3> <pre> tr <- sample(1:50, 25) train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) z <- lda(train, cl) predict(z, test)$class </pre> <hr /><div style="text-align: center;">[Package <em>MASS</em> version 7.3-51.4 <a href="00Index.html">Index</a>]</div> </body></html>