Thanks José Rafael, I will try with library(ape) (at the moment I cannot load it).
VarCorr gives the variance estimates for the random effect and the error terms. However, what I am looking for is a measure of the explained proportion of variance, such as it is R2 in regression models, and more precisely, I am looking for a measure of the explained proprotion of variance of each of the variables considered (continuous variables and other with random slope). For example, Snijders and Bosker (2003) pg 102 dedicate a chapter in their book to "how much does the multilevel model explain" (chapter 7) and derive formulaes for R_1 and R_2 (variance in the first and second level respectively). Things seem to get complicated when a slope random effect is included in the model, as in my case. It seems that package HLM provides the necessary estimates. I will have a look at library(ape), thanks for the suggestion. The book I mention is: Snijders, TAB and Bosker RJ (2003). Multilevel Analysis. An introduction to basic and advanced multilevel modeling. SAGE, London. Berta ----- Original Message ----- From: "José Rafael Ferrer Paris" <[EMAIL PROTECTED]> To: "Berta" <[EMAIL PROTECTED]> Cc: <r-help@stat.math.ethz.ch> Sent: Wednesday, March 07, 2007 5:16 PM Subject: Re: [R] anova applied to a lme object > The variances of the random effects and the residual variances are given > by the summary function. Maybe VarCorr or varcomp gives you the answer > you are looking for: > > library(nlme) > library(ape) > ?VarCorr > ?ape > > JR > El mié, 07-03-2007 a las 13:09 +0100, Berta escribió: >> Hi R-users, >> >> when carrying out a multiple regression, say lm(y~x1+x2), we can use an >> anova of the regression with summary.aov(lm(y~x1+x2)), and afterwards >> evaluate the relative contribution of each variable using the global Sum >> of >> Sq of the regression and the Sum of Sq of the simple regression y~x1. >> >> Now I would like to incorporate a random effect in the model, as some >> data >> correspond to the same region and others not: mylme<- lme(y~x1+x2, >> random= >> ~1|as.factor(region)). I would like to know, if possible, which is the >> contribution of each variable to the global variability. Using >> anova(mylme) >> produce an anova table (without the Sum of Sq column), but I am not sure >> how >> can I derive the contribution of each variable from it, or even whether >> it >> is nonsense to try, nor can I derive a measure of how much variability is >> left unexplained. >> >> Sorry for the type of question, but I did not find a simple solution and >> some researchers I work with love to have relative contributions to >> global >> variability. >> >> Thanks a lot in advance, >> >> Berta >> >> >> >> > >> >> ______________________________________________ >> R-help@stat.math.ethz.ch mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > -- > Dipl.-Biol. JR Ferrer Paris > ~~~~~~~~~~~~~~~~~~~~~~~~~~~ > Laboratorio de Biología de Organismos --- Centro de Ecología > Instituto Venezolano de Investigaciones Científicas (IVIC) > Apdo. 21827, Caracas 1020-A > República Bolivariana de Venezuela > > Tel: (+58-212) 504-1452 > Fax: (+58-212) 504-1088 > > email: [EMAIL PROTECTED] > clave-gpg: 2C260A95 > > > > ___________________________________________________________ > Telefonate ohne weitere Kosten vom PC zum PC: http://messenger.yahoo.de > > > > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.