[R-sig-ME] Fitting piecewise linear models with lmer

[alex]

Hello
I would like to fit piecewise linear growth curves to a number of
subjects (each with a number of observations at different abscissa),
where all the slopes for a common interval (e.g, 2 <= x < 3)
presumable arise from the same normal distribution, and therefore
should be "shrunk" towards each other.  What is the best way to about
doing this in lme4?  I have a toy, cleaned up (although I am not very
good with R) example of the type of data I am looking at.
Thank you for your time

set.seed(1)
# our growth functions are piecewise linear with these breaks
breaks <- seq(0,5)
mean.slopes <- seq(5,2,-.6)

# generate the true slope parameters for 20 subjects (variance isn't
really constant or known, just simplifying)...
true.slopes <- sapply(mean.slopes,function(x) rnorm(n=20,sd=3,mean=x))

# ...and the intercepts - they really don't come from any particular
distribution
true.intercepts <- runif(n=20,min=30,max=50)

# generate sample data - we don't necessarily have the same number of
observations per subject
mat <- matrix(nr=20*10,nc=3)
for (i in 1:20)
{
       xs <- runif(n=10,min=0,max=6)
       slopes <- true.slopes[i,]
       ys <- true.intercepts[i] + rnorm(n=10,mean=0,sd=0.9) +
sapply(xs, function(x) sum(slopes[1:floor(x)]) + (x - floor(x)) *
slopes[ceiling(x)])
       id <- rep(i,10)
       mat[(1+10*(i-1)):(10*i),] <- cbind(xs,ys,id)
}
mat <- as.data.frame(mat)
names(mat) <- c("x","y","id")
# qplot(x=x,y=y,colour=as.factor(id),data=mat)

Eventually I might like to shrink the slopes for each individual
towards each other, in the sense that if for a given subject the slope
in the interval [2,3) is very small compared to the slopes of other
subjects in the same interval, then the same would hold for the slope
in [1,2), but I would like to get the basics first.