[R] lattice --- different properties of lines corresponding to type=c("l", "a") respectively

[Kjetil Halvorsen]

I think the subject says it all. I want to make a simple lattice plot,
using xyplot with the
argument     type=c("l","a").
The problem then is that in the resulting plot it is
difficult/impossible to see which plot corresponds to the average
and which to the individual profiles. I triedthings like extra
arguments  lwd=c(1,3)   or   col=c("blue","red")
hoping this would be interpreteded parallely to the type=  argument,
but no.  Like:

xyplot(response ~ time|group, repa0, groups=~Participant, type=c("b",
"a"), lwd=c(1, 3),
       ylim=c(0, 10))

and many other variants ...

Then I vent for writing my own panel functions:
mypanel <- function(x, y, ..., type){
    panel.average(x, y, ...,  horizontal=FALSE)
    panel.xyplot(x, y, ..., type=type)
  }
xyplot(response ~ time|group, repa0, groups=~Participant, type="l",
ylim=c(0, 10),
       panel=panel.superpose, panel.groups=mypanel)

(which doesn't work)
???
Kjetil Halvorsen
To recreate the data:

repa0 <-
structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"),
    Participant = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
    11L, 12L, 13L, 14L), time = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
    3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
    4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
    5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
    2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), response = c(2L,
    10L, 8L, 4L, 0L, 4L, 10L, 2L, 3L, 4L, 1L, 2L, 3L, 3L, 5L,
    2L, 10L, 3L, 0L, 3L, 10L, 1L, 7L, 3L, 2L, 10L, 8L, 5L, 8L,
    0L, 10L, 2L, 0L, 2L, 10L, 10L, 3L, 2L, 10L, 4L, 6L, 0L, 5L,
    0L, 6L, 2L, 0L, 2L, 10L, 5L, 3L, 2L, 6L, 4L, 5L, 0L, 5L,
    0L, 5L, 0L, 0L, 0L, 10L, 0L, 0L, 2L, 5L, 0L, 5L, 0L, 3L,
    8L, 6L, 1L, 3L, 0L, 5L, 4L, 10L, 0L, 3L, 3L, 6L, 5L, 2L,
    0L, 2L, 0L, 4L, 0L, 5L, 4L, 5L, 1L, 0L, 2L, 0L, 5L, 3L, 1L,
    8L, 2L, 4L, 0L, 0L, 0L, 10L, 4L, 3L, 0L, 0L, 10L, 3L, 3L,
    6L, 1L, 4L, 0L, 0L, 0L, 10L, 3L, 3L, 2L, 2L, 10L, 2L, 3L,
    5L, 0L, 1L, 0L, 0L, 0L, 10L, 2L, 3L, 2L, 0L, 6L)), .Names = c("group",
"Participant", "time", "response"), row.names = c("1.1", "2.1",
"3.1", "4.1", "5.1", "6.1", "7.1", "8.1", "9.1", "10.1", "11.1",
"12.1", "13.1", "14.1", "1.2", "2.2", "3.2", "4.2", "5.2", "6.2",
"7.2", "8.2", "9.2", "10.2", "11.2", "12.2", "13.2", "14.2",
"1.3", "2.3", "3.3", "4.3", "5.3", "6.3", "7.3", "8.3", "9.3",
"10.3", "11.3", "12.3", "13.3", "14.3", "1.4", "2.4", "3.4",
"4.4", "5.4", "6.4", "7.4", "8.4", "9.4", "10.4", "11.4", "12.4",
"13.4", "14.4", "1.5", "2.5", "3.5", "4.5", "5.5", "6.5", "7.5",
"8.5", "9.5", "10.5", "11.5", "12.5", "13.5", "14.5", "1.11",
"2.11", "3.11", "4.11", "5.11", "6.11", "7.11", "8.11", "9.11",
"10.11", "11.11", "12.11", "13.11", "14.11", "1.21", "2.21",
"3.21", "4.21", "5.21", "6.21", "7.21", "8.21", "9.21", "10.21",
"11.21", "12.21", "13.21", "14.21", "1.31", "2.31", "3.31", "4.31",
"5.31", "6.31", "7.31", "8.31", "9.31", "10.31", "11.31", "12.31",
"13.31", "14.31", "1.41", "2.41", "3.41", "4.41", "5.41", "6.41",
"7.41", "8.41", "9.41", "10.41", "11.41", "12.41", "13.41", "14.41",
"1.51", "2.51", "3.51", "4.51", "5.51", "6.51", "7.51", "8.51",
"9.51", "10.51", "11.51", "12.51", "13.51", "14.51"), class = "data.frame")