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: LME fit from lmList 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 lme.lmList {nlme}"><tr><td>lme.lmList {nlme}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>LME fit from lmList Object</h2> <h3>Description</h3> <p>If the random effects names defined in <code>random</code> are a subset of the <code>lmList</code> object coefficient names, initial estimates for the covariance matrix of the random effects are obtained (overwriting any values given in <code>random</code>). <code>formula(fixed)</code> and the <code>data</code> argument in the calling sequence used to obtain <code>fixed</code> are passed as the <code>fixed</code> and <code>data</code> arguments to <code>lme.formula</code>, together with any other additional arguments in the function call. See the documentation on <code>lme.formula</code> for a description of that function. </p> <h3>Usage</h3> <pre> ## S3 method for class 'lmList' lme(fixed, data, random, correlation, weights, subset, method, na.action, control, contrasts, keep.data) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>fixed</code></td> <td> <p>an object inheriting from class <code>"<a href="lmList.html">lmList</a>."</code>, representing a list of <code>lm</code> fits with a common model.</p> </td></tr> <tr valign="top"><td><code>data</code></td> <td> <p>this argument is included for consistency with the generic function. It is ignored in this method function.</p> </td></tr> <tr valign="top"><td><code>random</code></td> <td> <p>an optional one-sided linear formula with no conditioning expression, or a <code>pdMat</code> object with a <code>formula</code> attribute. Multiple levels of grouping are not allowed with this method function. Defaults to a formula consisting of the right hand side of <code>formula(fixed)</code>.</p> </td></tr> <tr valign="top"><td><code>correlation</code></td> <td> <p>an optional <code>corStruct</code> object describing the within-group correlation structure. See the documentation of <code>corClasses</code> for a description of the available <code>corStruct</code> classes. Defaults to <code>NULL</code>, corresponding to no within-group correlations.</p> </td></tr> <tr valign="top"><td><code>weights</code></td> <td> <p>an optional <code>varFunc</code> object or one-sided formula describing the within-group heteroscedasticity structure. If given as a formula, it is used as the argument to <code>varFixed</code>, corresponding to fixed variance weights. See the documentation on <code>varClasses</code> for a description of the available <code>varFunc</code> classes. Defaults to <code>NULL</code>, corresponding to homoscedastic within-group errors.</p> </td></tr> <tr valign="top"><td><code>subset</code></td> <td> <p>an optional expression indicating the subset of the rows of <code>data</code> that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.</p> </td></tr> <tr valign="top"><td><code>method</code></td> <td> <p>a character string. If <code>"REML"</code> the model is fit by maximizing the restricted log-likelihood. If <code>"ML"</code> the log-likelihood is maximized. Defaults to <code>"REML"</code>.</p> </td></tr> <tr valign="top"><td><code>na.action</code></td> <td> <p>a function that indicates what should happen when the data contain <code>NA</code>s. The default action (<code>na.fail</code>) causes <code>lme</code> to print an error message and terminate if there are any incomplete observations.</p> </td></tr> <tr valign="top"><td><code>control</code></td> <td> <p>a list of control values for the estimation algorithm to replace the default values returned by the function <code>lmeControl</code>. Defaults to an empty list.</p> </td></tr> <tr valign="top"><td><code>contrasts</code></td> <td> <p>an optional list. See the <code>contrasts.arg</code> of <code>model.matrix.default</code>.</p> </td></tr> <tr valign="top"><td><code>keep.data</code></td> <td> <p>logical: should the <code>data</code> argument (if supplied and a data frame) be saved as part of the model object?</p> </td></tr> </table> <h3>Value</h3> <p>an object of class <code>lme</code> representing the linear mixed-effects model fit. Generic functions such as <code>print</code>, <code>plot</code> and <code>summary</code> have methods to show the results of the fit. See <code>lmeObject</code> for the components of the fit. The functions <code>resid</code>, <code>coef</code>, <code>fitted</code>, <code>fixed.effects</code>, and <code>random.effects</code> can be used to extract some of its components. </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>References</h3> <p>The computational methods follow the general framework of Lindstrom and Bates (1988). The model formulation is described in Laird and Ware (1982). The variance-covariance parametrizations are described in Pinheiro and Bates (1996). The different correlation structures available for the <code>correlation</code> argument are described in Box, Jenkins and Reinse (1994), Littel <em>et al</em> (1996), and Venables and Ripley, (2002). The use of variance functions for linear and nonlinear mixed effects models is presented in detail in Davidian and Giltinan (1995). </p> <p>Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden–Day. </p> <p>Davidian, M. and Giltinan, D.M. (1995) "Nonlinear Mixed Effects Models for Repeated Measurement Data", Chapman and Hall. </p> <p>Laird, N.M. and Ware, J.H. (1982) "Random-Effects Models for Longitudinal Data", Biometrics, 38, 963–974. </p> <p>Lindstrom, M.J. and Bates, D.M. (1988) "Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data", Journal of the American Statistical Association, 83, 1014–1022. </p> <p>Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996) "SAS Systems for Mixed Models", SAS Institute. </p> <p>Pinheiro, J.C. and Bates., D.M. (1996) "Unconstrained Parametrizations for Variance-Covariance Matrices", Statistics and Computing, 6, 289–296. </p> <p>Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, Springer-Verlag. </p> <h3>See Also</h3> <p><code><a href="lme.html">lme</a></code>, <code><a href="lmList.html">lmList</a></code>, <code><a href="lmeObject.html">lmeObject</a></code> </p> <h3>Examples</h3> <pre> fm1 <- lmList(Orthodont) fm2 <- lme(fm1) summary(fm1) summary(fm2) </pre> <hr /><div style="text-align: center;">[Package <em>nlme</em> version 3.1-139 <a href="00Index.html">Index</a>]</div> </body></html>