EVOLUTION-MANAGER
Edit File: predict.nlme.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: Predictions from an nlme 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 predict.nlme {nlme}"><tr><td>predict.nlme {nlme}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Predictions from an nlme Object</h2> <h3>Description</h3> <p>The predictions at level <i>i</i> are obtained by adding together the contributions from the estimated fixed effects and the estimated random effects at levels less or equal to <i>i</i> and evaluating the model function at the resulting estimated parameters. If group values not included in the original grouping factors are present in <code>newdata</code>, the corresponding predictions will be set to <code>NA</code> for levels greater or equal to the level at which the unknown groups occur. </p> <h3>Usage</h3> <pre> ## S3 method for class 'nlme' predict(object, newdata, level = Q, asList = FALSE, na.action = na.fail, naPattern = NULL, ...) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>object</code></td> <td> <p>an object inheriting from class <code>"<a href="nlme.html">nlme</a>"</code>, representing a fitted nonlinear mixed-effects model.</p> </td></tr> <tr valign="top"><td><code>newdata</code></td> <td> <p>an optional data frame to be used for obtaining the predictions. All variables used in the nonlinear model, the fixed and the random effects models, as well as the grouping factors, must be present in the data frame. If missing, the fitted values are returned.</p> </td></tr> <tr valign="top"><td><code>level</code></td> <td> <p>an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping (and is <code>object$dims$Q</code>).</p> </td></tr> <tr valign="top"><td><code>asList</code></td> <td> <p>an optional logical value. If <code>TRUE</code> and a single value is given in <code>level</code>, the returned object is a list with the predictions split by groups; else the returned value is either a vector or a data frame, according to the length of <code>level</code>.</p> </td></tr> <tr valign="top"><td><code>na.action</code></td> <td> <p>a function that indicates what should happen when <code>newdata</code> contains <code>NA</code>s. The default action (<code>na.fail</code>) causes the function to print an error message and terminate if there are any incomplete observations.</p> </td></tr> <tr valign="top"><td><code>naPattern</code></td> <td> <p>an expression or formula object, specifying which returned values are to be regarded as missing.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>some methods for this generic require additional arguments. None are used in this method.</p> </td></tr> </table> <h3>Value</h3> <p>if a single level of grouping is specified in <code>level</code>, the returned value is either a list with the predictions split by groups (<code>asList = TRUE</code>) or a vector with the predictions (<code>asList = FALSE</code>); else, when multiple grouping levels are specified in <code>level</code>, the returned object is a data frame with columns given by the predictions at different levels and the grouping factors. </p> <h3>Author(s)</h3> <p>José Pinheiro and Douglas Bates <a href="mailto:bates@stat.wisc.edu">bates@stat.wisc.edu</a></p> <h3>See Also</h3> <p><code><a href="nlme.html">nlme</a></code>, <code><a href="fitted.lme.html">fitted.lme</a></code></p> <h3>Examples</h3> <pre> head(Loblolly) # groupedData w/ 'Seed' is grouping variable : ## Grouped Data: height ~ age | Seed ## height age Seed ## 1 4.51 3 301 ## 15 10.89 5 301 ## .. ..... . ... fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly, fixed = Asym + R0 + lrc ~ 1, random = Asym ~ 1, ## <---grouping---> Asym ~ 1 | Seed start = c(Asym = 103, R0 = -8.5, lrc = -3.3)) fm1 age. <- seq(from = 2, to = 30, by = 2) newLL.301 <- data.frame(age = age., Seed = 301) newLL.329 <- data.frame(age = age., Seed = 329) (p301 <- predict(fm1, newLL.301, level = 0:1)) (p329 <- predict(fm1, newLL.329, level = 0:1)) ## Prediction are the same at level 0 : all.equal(p301[,"predict.fixed"], p329[,"predict.fixed"]) ## and differ by the 'Seed' effect at level 1 : p301[,"predict.Seed"] - p329[,"predict.Seed"] </pre> <hr /><div style="text-align: center;">[Package <em>nlme</em> version 3.1-139 <a href="00Index.html">Index</a>]</div> </body></html>