Nested and crossed random effects

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