Nested model in R

Question

I'm having a huge problem with a nested model I am trying to fit in R.

I have response time experiment with 2 conditions with 46 people each and 32 measures each. I would like measures to be nested within people and people nested within conditions, but I can't get it to work.

The code I thought should make sense was:

nestedmodel <- lmer(responsetime ~ 1 + condition + 
 (1|condition:person) + (1|person:measure), data=dat) 

However, all I get is an error:

Error in checkNlevels(reTrms$flist, n = n, control) : 
  number of levels of each grouping factor must be < number of observations

Unfortunately, I do not even know where to start looking what the problem is here. Any ideas? Please, please, please? =) Cheers!

Answer 1

This might be more appropriate on CrossValidated, but: lme4 is trying to tell you that one or more of your random effects is confounded with the residual variance. As you've described your data, I don't quite see why: you should have 2*46*32=2944 total observations, 2*46=92 combinations of condition and person, and 46*32=1472 combinations of measure and person.

If you do

lf <- lFormula(responsetime ~ 1 + condition + 
                (1|condition:person) + (1|person:measure), data=dat) 

and then

lapply(lf$reTrms$Ztlist,dim)

to look at the transposed random-effect design matrices for each term, what do you get? You should (based on your description of your data) see that these matrices are 1472 by 2944 and 92 by 2944, respectively.

As @MrFlick says, a reproducible example would be nice. Other things you could show us are:

• fit the model anyway, using lmerControl(check.nobs.vs.nRE="ignore") to ignore the test, and show us the results (especially the random effects variances and the statement of the numbers of groups)
• show us the results of with(dat,table(table(interaction(condition,person))) to give information on the number of replicates per combination (and similarly for measure)