anova applied to a lme object
Thanks Jos? 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: "Jos? Rafael Ferrer Paris" <jr_frrr at yahoo.de> To: "Berta" <ibanez at bioef.org> Cc: <r-help at 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 mi?, 07-03-2007 a las 13:09 +0100, Berta escribi?:
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
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