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.
