A question on setting up a generalized additive mixed effect model

Dear R experts

I am new to R & generalized additive models and wonder whether I could get
some help from you all. The question I have is as follows:

I have 30 subjects with each subject being scanned one to three times in
the first year of life.

The brain volume (BrainVolume) from each scan was measured.

The scan time was randomly distributed from birth to 1 year, indexed by
subjIndexF. i.e., first three scans are from the same subject, the fourth
is from the second, subjIndexF=1,1,1,2...

Each subject has chronological age (age) from birth to 1 year old.

Now, I want to look at how predictors, such as subject's age will explain
the changes in brain volume. I also want to model both random slope and
intercept for random effects within each subject in the model. My model
ends up like this:


gam=gam(brainVolume~ s(age) + s(subjIndexF, bs=?re?) +  s(subjIndexF, age,
bs="re"), method="REML", data=mydata)


In which, s(subjeIndexF, bs=?re?) is for modeling random intercepts and
s(subjIndexF, age, bs=?re?) is for modeling different slopes. When I tried
to run the model, I was given a ?coefficients more than the data? error. So
my questions are as follows:


(1) Does this model make sense, especially the part dealing with the
repeated measures within subjects as random effects?

(2) If it does, what I can do to reduce the required parameters? The model
runs if I only model random intercepts without interaction term, but a more
realistic scenario would be each subject has random slope for smooths as
well.


Your help will be greatly appreciated!


I set up the model by raining following the suggestions in the following
two links:

http://www.sfs.uni-tuebingen.de/~jvanrij/Tutorial/GAMM.html

http://r.789695.n4.nabble.com/Random-effects-in-package-mgcv-td4720162.html