Question

What is wrong with the following model:

 # simulated data yr = 2; vg = 4, fm = 5, gen = 5
    mbb <- data.frame( trait1 = rnorm(200, 15, 4),yr = c(rep (1:2, each = 100)), 
   vg = c(rep(rep(1:4, each =25), 2)), fm = rep(rep(1:5, each = 5), 8), 
    gen = sample(c(1:5), 200, replace = T))
    require(lme4) 
    lmer(trait1 ~ (yr + vg + gen)^3 + (yr + vg + gen|fm:vg), data= mbb)# full model 

I am getting following error:

Error in validObject(.Object) : 
  invalid class "mer" object: Slot Zt must by dims['q']  by dims['n']*dims['s']
In addition: Warning messages:
1: In fm:vg : numerical expression has 200 elements: only the first used
2: In fm:vg : numerical expression has 200 elements: only the first used

Answer 1

The problem is precisely that fm and vg are stored as numeric, not as factors, and so lmer tries to interpret fm:vg as a sequence operator (see ?seq) rather than an interaction operator (see ?interaction). You can:

• convert fm and vg to factors within the data frame (mbb <- transform(mbb,vg=factor(vg),fm=factor(fm))) [it’s not clear from your setup whether you want vg and fm to be factors or continuous predictors … that distinction would be very important, of course … if you want them as continuous predictors, then it’s a bit weird to treat them as factors for the purposes of grouping … ]

• write the interaction explicitly as interaction(fm,vg) on the fly

• convert to factors on the fly ((yr+vg+gen|factor(fm):factor(vg)))

• use Jim M.’s solution

  I think these will all work, although I have to admit that I haven’t tested them.