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I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Hello, I want to do this model: Y_ml=Mu+M+L(M)+Error M is a fixed factor with two levels, 1 and 2 L is a random factor and nested in M, with 9 levels (9 places inside each level of M) amarc is the dependent variable (animal abundance) datos is the matrix I used the next orders in R, but there is a problem with the Df calculation: > m [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

I'm working with aov function, I have a problem with the experimental designs of various factors (nested and orthogonal mixed) because aov function is wrong. Is there someone using aov function? Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",