multcomp package

Thanks Evan and Gabriel for the reply. I think it might help me make the
question clearer if I show the data and the model here (I actually asked
questions related to this data before but I still need some help). The data
looks like the following:

   response individual time method
1    102.9          3    0      3
2    103.0          3    3      3
3    103.0          3    6      3
4    102.8          3    9      3
5    102.2          3   12      3
6    102.5          3   15      3
7    103.0          3   18      3
8    102.0          3   24      3
9    102.8          1    0      3
10   102.7          1    3      3
11   103.0          1    6      3
12   102.2          1    9      3
13   103.0          1   12      3
14   102.8          1   15      3
15   102.8          1   18      3
16   102.9          1   24      3
17   102.2          2    0      3
18   102.6          2    3      3
19   103.4          2    6      3
20   102.3          2    9      3
21   101.3          2   12      3
22   102.1          2   15      3
23   102.1          2   18      3
24   102.2          2   24      3
25   102.7          4    0      3
26   102.3          4    3      3
27   102.6          4    6      3
28   102.7          4    9      3
29   102.8          4   12      3
30   102.5          5    0      3
31   102.4          5    3      3
32   102.1          5    6      3
33   102.3          6    0      3
34   102.3          6    3      3
35   101.9          7    0      3
36   102.0          7    3      3
37   107.4          3    0      1
38   101.3          3   12      1
39    92.8          3   15      1
40    73.7          3   18      1
41   104.7          3   24      1
42    92.6          1    0      1
43   101.9          1   12      1
44   106.3          1   15      1
45   104.1          1   18      1
46    95.6          1   24      1
47    79.8          2    0      1
48    89.7          2   12      1
49    97.0          2   15      1
50   108.4          2   18      1
51   103.5          2   24      1
52    96.4          4    0      1
53    89.3          4   12      1
54   112.6          5    0      1
55    93.3          6    0      1
56    99.6          7    0      1
57   109.5          3    0      2
58    98.5          3   12      2
59   103.5          3   24      2
60   113.5          1    0      2
61    94.5          1   12      2
62    88.5          1   24      2
63    99.5          2    0      2
64    97.5          2   12      2
65    98.5          2   24      2
66   103.5          4    0      2
67    89.5          5    0      2
68    87.5          6    0      2
69    82.5          7    0      2

I used the following random intercept and random slope model for this data.

Denote as y_ijk the response value from *j*th individual within *i*th
method at time point *k*. Assume the following model for y_ijk:

      y_ijk= (alpha_0+ tau_i +a_j(i))+(beta_i+b_j(i)) T_k + e_ijk

Here alpha_0 is the grand mean;
          tau_i is the fixed effect for ith method;
          a_j(i) is random intercept corresponding to the *j*th individual
within *i*th method, assumed to be common for all three methods;
          beta_i is the fixed slope corresponding to the ith method;
          b_j(i) is the random slope corresponding to jth individual for
the ith method, assumed to be common for all three methods;
          T_k is the time corresponding to y_ijk;
          e_ijk is the residual.

Here I used the following specification for the lme function

mod1 <- lme(fixed= reponse ~ method*time, random=~ 1 +time | individual,
data=one, weights= varIdent(form=~1|method),
            control = lmeControl(opt = "optim"))

I did not add the method in random effects  because here I assumed common
random slope for all three methods.

This model is used to initially check whether there fixed slopes are equal.
I wanted to further evaluate whether each fixed slope (beta_1, beta_2 and
beta 3) is significantly different from zero. I was hoping to evaluate this
based on the same model.

The output is as follows. Does the highlighted part below already gives the
result for testing beta_1=0; beta_2=0 and beta_3=0?

Thanks very much.
   Hanna