Hi All,
I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard.
One thing that has really frustrated me is that lme has an extremely counterintuitive way for specifying random terms. I can usually figure out how to express a single random term, but if there are multiple terms or random interactions, the documentation available just doesn't hold up.
Here's an example: a split block (strip plot) design evaluated in SAS with PROC MIXED (an excerpt of the model and random statements):
model DryMatter = Compacting|Variety / outp = residuals ddfm = satterthwaite;
random Rep Rep*Compacting Rep*Variety;
Now the fixed part of that model is easy enough in lme: "DryMatter~Compacting*Variety"
But I can't find anything that adequately explains how to simply add the random terms to the model, ie "rep + rep:compacting + rep:variety"; anything to do with random terms in lme seems to go off about grouping factors, which just isn't intuitive for me.
The grouping factor is rep because the random effects are associated with the levels of rep.
I don't always understand the SAS notation so you may need to help me out here. Do you expect to get a single variance component estimate for Rep*Compacting and a single variance component for Rep*Variety? If so, you would specify the model in lmer by first creating factors for the interaction of Rep and Compacting and the interaction of Rep and Variety.
dat$RepC <- with(dat, Rep:Compacting)[drop=TRUE] dat$RepV <- with(dat, Rep:Variety)[drop=TRUE] fm <- lmer(DryMatter ~ Compacting*Variety+(1|Rep)+(1|RepC)+(1|RepV), dat)
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