I've been trying to analyse data from a factorial design with missing cells without much success until now. I'm using Anova from car package, that, according with I've read, can handle unbalanced data with type III sum of squares. But when I detect an interaction effect I don't know how to unfold the degree of freedom of the interaction and discover what is the best combination of factors. I think I have to do something like in this article
da Rocha, F et al. An approach to the decomposition of interaction in a factorial experiment with five factors, Acta Sci., Agron. 34(1), 2012.
Some one know's how to unfold the degrees of freedom?
Here follows a reproducible example
fdata <- read.table("http://labpib.ffclrp.usp.br/~rsilva/fdata.txt") library(car) options(contrasts=c("contr.sum","contr.poly")) fdata.hi <- aov(log(resp+1) ~ PIE*NS*La*Li*Lf, data=fdata) # trying to analyse as a full factorial design Anova(fdata.hi, type="III", singular.ok=TRUE) # trying to analyse as an one-way anova library(agricolae) model1 <- aov(log(resp+1) ~ ALL, fdata) HSD.test(model1, "ALL") x11(); par(mfrow=c(3,2)) apply(combn(2:6,2)[,1:6], 2, function(x) interaction.plot(fdata[,x], fdata[,x], fdata$resp)) x11(); par(mfrow=c(2,2)) apply(combn(2:6,2)[,7:10], 2, function(x) interaction.plot(fdata[,x], fdata[,x], fdata$resp)) par(mfrow=c(1,1))
That is what I'm doing until now, try to verify graphically what is the best factor combination, knowing that I have a significant interaction among four factors.