[R-sig-ME] plot glmmPQL

[Mario V. Balzan]

Dear all,

I am investigating the influence of some experimental treatments
(A,B,C,...) on fecundity of a lepidoptera. These data have been
collected over time from different experimental plots (A1, A2, ... ).
I am using glmmPQL function with quasipoisson errors since these are
count data and with overdispersion.

the model which I came up with is as follows:
glmmPQL(Fecundity~Treatment,random=~Time|Plot, family="quasipoisson")

I would like to plot a curve of fecundity against time for each
treatment from this model. Is that possible? Would appreciate if
someone could help me out with this.

this is a sample of my data:
  Time. Fecundity Plot Treatment
      2        40   A1         A
      4        10   A1         A
      6         0   A1         A
      8         0   A1         A
      2        21   A2         A
      4         0   A2         A
      6         0   A2         A
      2         9   A3         A
      4         0   A3         A
    2        23   A4         A
     4         0   A4         A
     6         2   A4         A
     2         0   B1         B
    4         0   B1         B
    6        10   B1         B
     8         0   B1         B
   11         0   B1         B
    2         1   B2         B
    4         2   B2         B
    6         0   B2         B
    8        25   B2         B
   11         6   B2         B
    13         4   B2         B
    15        10   B2         B
    18         8   B2         B
     2         5   B3         B
     4         6   B3         B
     6        20   B3         B
     8         2   B3         B
    11         2   B3         B
     2        23   C1         C
     4         8   C1         C
     6         4   C1         C
     8         0   C1         C
    10         2   C1         C
   13         0   C1         C
     2        15   C2         C
     4        15   C2         C
     6         4   C2         C
     8         0   C2         C
    10         0   C2         C
    13         0   C2         C
     2        65   C3         C
     4         2   C3         C
     6         8   C3         C
     8        10   C3         C
    10         6   C3         C
   13         2   C3         C
...


Thanks in advance,
Mario