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...s test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c( rep("A",300),seq(1:100)) > > population2 <- c( rep("A",100),seq(101:200)) > > > for (j in 1:nreps.outer){ + n1 <- sample(30:100,1) + n2 <- sample(30:100,1) + + group1 <- sample(population1, n1, replace=T) + group2 <- sample(population2, n2, replace=T) + + pvalue.fisher[j] <- fisher.test(table(c(gro...

...to do so seem to normalize the eigenvectors to length 1. Although the helpfile of eigen() states, that using eigen(, symmetric = FALSE, EISPACK =TRUE) skips normalization this is (I guess) not applicable for me as my matrices are symmetric. 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 Baltzer...