Lattice: raw data and prediction of a non linear fitted function

Dear all,

I have a question about lattice use. I would like to use it to represent:
- my raw data as points,
- and the results of a non linear fit as a line,
- having 2 groups data (and so 2 colors).

However, as I have few raw data, I would like to increase the number of
points to smooth the line which correspond to the fit.

So my questions are:
- How can I use the group argument to make new predictions inside panel
argument ?
- How can I use this prediction inside the panel argument to draw the fit
per group?

Hereafter a minimal example:

#==================================================
library(lattice)
set.seed(2)

# Dummy dataframe
foo <- data.frame(
  time = seq(0, 60, 5),
  char = sample(c("A", "B"), size = 13, replace = TRUE)
  );

# Simulated response vector according a Gompertz function + rnorn(0, 5)
foo$y <- SSgompertz(foo$time, Asym = 100, b2 = ifelse(foo$char == 'A', 6,
10),
  b3 = ifelse(foo$char == "A", .91, .8)) +
  rnorm(nrow(foo), mean=0, sd = 5);

# Non-linear fit on simulation data
res_nls <-  nls(
  y ~ SSgompertz(time, Asym = 100, b2[char], b3[char]), data = foo,
  start = list( b2 = c(7,7), b3 = c(0.9, 0.9)));

# My problem
xyplot(y ~ time, groups = char, data = foo,
  panel = function(x, y, groups=groups, model = model, ...){
    panel.xyplot(x,y, groups = groups, ...);

    newdata <- data.frame(time = x, char = groups);
    newdata$y <- predict(model, newdata = newdata);

    panel.superpose(x = newdata$time, y=newdata$y,  groups = groups, ...,
      panel.groups = function(x,y, col, col.symbol, ...){
        panel.lines(x,y, col.line = col.symbol)
      })

  }, model = res_nls);
#==================================================

Many thanks,
Francois