|
|
|
|
|
NNTS test |
|
|
|
Model 1 |
n1 |
Model 2 |
n2 |
α |
M0 = 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
Watson |
Rao Mean |
Rao Disp. |
Uniform |
20 |
von Mises |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
25 |
92 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
51 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
59 |
100 |
|
50 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
60 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
79 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
91 |
100 |
|
|
|
100 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
88 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
94 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
95 |
100 |
|
|
|
NNTS |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
56 |
79 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
75 |
95 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
81 |
97 |
|
|
|
50 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
77 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
91 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
94 |
100 |
|
|
|
100 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
96 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
99 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
99 |
100 |
|
Uniform |
50 |
von Mises |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
29 |
93 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
50 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
62 |
100 |
|
|
|
50 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
67 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
82 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
89 |
100 |
|
|
100 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
91 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
98 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
100 |
100 |
|
|
NNTS |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
51 |
77 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
76 |
96 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
83 |
98 |
|
|
|
50 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
81 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
93 |
100 |
|
|
|
100 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
100 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
100 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
100 |
100 |
|
Uniform |
100 |
von Mises |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
31 |
92 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
54 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
63 |
100 |
|
|
50 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
73 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
85 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
90 |
100 |
|
|
|
100 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
95 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
97 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
NNTS |
20 |
0.01 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
63 |
78 |
|
|
|
|
0.05 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
77 |
95 |
|
|
|
|
0.1 |
100 |
100 |
100 |
|
|
|
|
|
|
|
100 |
82 |
98 |
|
|
|
50 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
87 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
95 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
100 |
98 |
100 |
|
|
|
100 |
0.01 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
99 |
100 |
|
|
|
|
0.05 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
|
|
|
|
0.1 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
100 |
|
von Mises |
20 |
NNTS |
20 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
2 |
0 |
6 |
|
|
|
|
0.05 |
5 |
3 |
5 |
|
|
|
|
|
|
|
12 |
3 |
8 |
|
|
|
|
0.1 |
7 |
4 |
10 |
|
|
|
|
|
|
|
23 |
9 |
13 |
|
|
|
50 |
0.01 |
1 |
0 |
3 |
|
|
|
|
|
|
|
5 |
0 |
1 |
|
|
|
|
0.05 |
3 |
1 |
10 |
|
|
|
|
|
|
|
12 |
4 |
9 |
|
|
|
|
0.1 |
9 |
8 |
18 |
|
|
|
|
|
|
|
17 |
10 |
14 |
|
|
|
100 |
0.01 |
0 |
0 |
2 |
|
|
|
|
|
|
|
3 |
1 |
2 |
|
|
|
|
0.05 |
1 |
2 |
15 |
|
|
|
|
|
|
|
15 |
4 |
5 |
|
|
|
|
0.1 |
7 |
7 |
29 |
|
|
|
|
|
|
|
20 |
5 |
17 |
von Mises |
50 |
NNTS |
20 |
0.01 |
0 |
1 |
2 |
|
|
|
|
|
|
|
2 |
0 |
7 |
|
|
|
|
0.05 |
7 |
6 |
16 |
|
|
|
|
|
|
|
15 |
4 |
10 |
|
|
|
|
0.1 |
18 |
12 |
27 |
|
|
|
|
|
|
|
20 |
13 |
15 |
|
|
|
50 |
0.01 |
3 |
2 |
11 |
18 |
14 |
12 |
|
|
|
|
5 |
0 |
1 |
|
|
|
|
0.05 |
10 |
14 |
29 |
34 |
27 |
30 |
|
|
|
|
23 |
5 |
6 |
|
|
|
|
0.1 |
16 |
24 |
46 |
50 |
41 |
36 |
|
|
|
|
28 |
10 |
15 |
|
|
|
100 |
0.01 |
3 |
6 |
24 |
32 |
38 |
17 |
|
|
|
|
12 |
1 |
5 |
|
|
|
|
0.05 |
11 |
27 |
45 |
59 |
55 |
48 |
|
|
|
|
28 |
5 |
7 |
|
|
|
|
0.1 |
24 |
33 |
64 |
68 |
66 |
59 |
|
|
|
|
41 |
12 |
14 |
von Mises |
100 |
NNTS |
20 |
0.01 |
3 |
1 |
3 |
|
|
|
|
|
|
|
4 |
0 |
6 |
|
|
|
|
0.05 |
11 |
7 |
7 |
|
|
|
|
|
|
|
13 |
3 |
11 |
|
|
|
|
0.1 |
19 |
12 |
25 |
|
|
|
|
|
|
|
22 |
8 |
13 |
|
|
|
50 |
0.01 |
4 |
8 |
17 |
19 |
17 |
19 |
|
|
|
|
7 |
2 |
1 |
|
|
|
|
0.05 |
17 |
19 |
41 |
47 |
38 |
30 |
|
|
|
|
26 |
5 |
5 |
|
|
|
|
0.1 |
28 |
30 |
49 |
59 |
53 |
49 |
|
|
|
|
32 |
13 |
10 |
|
|
|
100 |
0.01 |
9 |
11 |
32 |
50 |
42 |
40 |
30 |
27 |
24 |
19 |
11 |
0 |
2 |
|
|
|
|
0.05 |
20 |
30 |
59 |
69 |
67 |
65 |
60 |
57 |
47 |
46 |
29 |
5 |
6 |
|
|
|
|
0.1 |
28 |
40 |
72 |
81 |
79 |
74 |
68 |
66 |
65 |
59 |
42 |
12 |
12 |
|