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...x<-eigen(cov(population1))$vectors[,1] y<-eigen(cov(population2))$vectors[,1] angle (in degree): ((acos((x%*%y)/(sqrt(x%*%y)*sqrt(y%*%y))))*180)/pi Thus by using normalized vectors, the divisor becomes meaningless being 1. Does anybody have an idea to get raw eigenvectors? Thanks a lot Kay Lucek Aquatic Ecology & Macroevolution University of Bern Baltzerstr. 6 3012 Bern Switzerland

...2740 0.03237 -0.846 Hence my question is, is it correct what I am doing? Or should I use another way to estimate the amount of variance explained by each factor (i.e. Treatment, Source and their interaction). For example, would the effect sizes be a more appropriate way to go? Thanks! Kay Lucek -- View this message in context: http://r.789695.n4.nabble.com/How-to-estimate-variance-components-with-lmer-for-models-with-random-effects-and-compare-them-with-ls-tp4634492.html Sent from the R help mailing list archive at Nabble.com.