Guillaume Théroux Rancourt writes: > > Hello! > > I have read somewhere (somehow, I can't seem to find it again, > it's been a couple of months) that when > analyzing factorial block design, the position where you put > the block factor is important, even more > when there are missing values. > > I understand that when using anova.lm, the order is sequential, > so that if I want to check for a treatment > effect, I should put my blocking factor before in order to . > It's just that I got confused with all the > answers from previous posts and books, and I don't know if > the missing values are being handled properly. > > My code is: > P.biom = lm(biomass ~ Bloc + Trt*Clone, data=mydata) > P.aov = anova.lm(P.biom, test="F") >
[snip] Sorry to make things even more complicated, but *if* you are dealing with a large number of missing values you might want to consider using the lme() function (in the nlme package), along with its companion book by Pinheiro and Bates (2000); the methods used in that package should be more robust to lack of balance than lm/aov ... Ben Bolker ______________________________________________ R-help@r-project.org 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.
