Hi all,
I'm currently analyzing data using random regression and I'm a bit puzzled
how to fit a more complex covariance structure. In my data I have one
two-level fixed factor (say F1), one random effect of "family" with 90
families and another fixed effect with 3 levels (say F2). All families have
individuals found in all combinations of F1 and F2. I also have a
continuous variable Time.

If I get it right - the simplest random regression model would look like
this (only the random part):
~str(~family+family:time, ~us(2):id(90))

Also - if I understand correctly - if I wanted to add F1 to the structure I
could fit
~at(F1):str(~family+family:time, ~us(2):id(90)) which would give me a
covariance structure of (sorry for sloppy matrices, each row represents 4
elements of a 4 rows matrix):


V.F1a(intercept)        COV.F1a         0               0
COV.F1a                 V.F1a(slope)    0               0
0                       0               V.F1b(int)      COV.F1b
0                       0               COV.F1b         V.F1b(slope)

And now the tricky part: how I can specify that (if its possible) to
estimate also covariances between intercepts and slopes in different F1
groups? i.e.


V.F1a(intercept)        COV.F1a         COV.Fab(i)      0
COV.F1a                 V.F1a(slope)    0               COV.Fab(s)
COV.Fab(i)              0               V.F1b(int)      COV.F1b
0                       COV.Fab(s)      COV.F1b         V.F1b(slope)


Of course there's yet another level for this complexity (it's the F2 factor
but this one could be incorporated by creating an additional F3 with levels
being combinations of F1 and F2.

Cheers,
szymek

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