[R-sig-ME] Nested and crossed random effects

[Patrick Onyango]

Dear All,
I am in transition from SPSS to R and so I am trying to read as much  
as I can to address some of my immediate statistical needs. And so,  
as with most transitions, I am plagued by haste that some of you may  
find quite sublime; but I ask for leniency.

I am trying to model a response variable, response,  as a function of  
the following fixed terms: a, b, c, d, and e; and 3 random effects:  
f, g, h such that g denotes the ID of my sampling subjects sampled  
over time and the subjects are distributed in 5 groups, here denoted  
by f. An important note is that I sampled g as part of a pair with h  
such that overall I have 131 samples involving 39 different gs and 45  
different hs where h may be or may not be at 2 levels of two of the  
fixed terms, let's for convenience call those terms d and e, each  
with levels 1 and 2. The number of samples among h are pretty uneven.

The main idea is to find out what g does given the level of h; and  
how is that influenced by a,b,c as well as vary by the IDs f,g,h  
where f is the highest level of random effects and g and h should  
probably be crossed terms?

This is what I did:
model<-lmer(response~a+b+c+d+e+(1|f)+(1|f/g)+(1|g)+(1|h), method='ML')

but got the following warning message
Error: length(f1) == length(f2) is not TRUE
In addition: Warning messages:
1: In g:f:
   numerical expression has 131 elements: only the first used
2: In g:f:
   numerical expression has 131 elements: only the first used
3: In h:f :
   numerical expression has 131 elements: only the first used
4: In h:f :
   numerical expression has 131 elements: only the first used

Then I tried
model<-lmer(response~a+b+c+d+e+(1|f)+(1+f|g)+(1|g)+(1|h),  
method='ML') only making one change at the nested term; and got the  
following Error message

In mer_finalize(ans) : singular convergence (7)

Further, the output from the latter call found a perfect correlation  
for the intercept and slope of g indicating overparametization; and  
so I sought to simplify the model by removing the correlation term  
and assuming homoscedasticity for g in respect to f by replacing the  
nested term in the previous call with (1|f:g), and also adding corr =  
FALSE in the call

But got the following
Error: length(f1) == length(f2) is not TRUE

I go in your records as probably having the longest post. An  
achievement indeed.

I need and will be most thankful for help in figuring how to handle  
my data and in deciphering the error messages.

Many thanks in advance; and please let me if further clarifications  
would suffice.
Patrick