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GLM, GOF, AIC, ma démarche est-elle correcte ?

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GLM, GOF, AIC, ma démarche est-elle correcte ?

Message par lenny868 le Mar 1 Mai 2018 - 9:36

Bonjour à tous,

Voilà je souhaite proposer une modélisation pour un comportement donnée en le liant à des variables individuelles et contextuelles. Je souhaite avoir votre avis sur ma démarche sur R:

(1) mon dataset :
Code:
res=structure(list(Genre = c("Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male"
), Times = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L,
15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 32L, 32L, 33L, 33L,
33L, 33L, 33L, 33L, 33L, 33L, 33L, 35L, 35L, 35L, 36L, 36L, 36L,
36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 38L, 39L, 43L, 43L, 43L,
43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 50L, 50L,
50L, 50L, 50L, 51L, 51L, 51L, 52L, 58L, 58L, 58L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 20L, 20L, 20L, 20L, 20L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L,
33L, 33L, 33L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L,
37L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 47L, 47L,
50L, 50L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 52L, 52L, 58L, 58L,
58L, 58L), Type = c("Irregular", "Irregular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Irregular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Irregular", "Irregular", "Irregular", "Irregular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Irregular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Irregular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Irregular", "Irregular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Irregular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Irregular", "Irregular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular"), Age = c(20L,
43L, 50L, 18L, 23L, 24L, 25L, 26L, 30L, 35L, 37L, 53L, 56L, 36L,
38L, 63L, 10L, 19L, 21L, 25L, 26L, 27L, 33L, 34L, 36L, 37L, 39L,
40L, 44L, 51L, 54L, 56L, 59L, 25L, 33L, 35L, 43L, 50L, 51L, 16L,
18L, 22L, 25L, 27L, 32L, 33L, 34L, 35L, 36L, 39L, 40L, 46L, 47L,
50L, 52L, 54L, 56L, 57L, 59L, 60L, 61L, 27L, 25L, 28L, 31L, 37L,
48L, 53L, 55L, 17L, 22L, 19L, 22L, 25L, 26L, 27L, 37L, 40L, 50L,
51L, 55L, 56L, 58L, 28L, 34L, 43L, 45L, 49L, 74L, 46L, 48L, 50L,
55L, 45L, 60L, 20L, 24L, 27L, 30L, 35L, 38L, 39L, 41L, 44L, 47L,
50L, 22L, 30L, 51L, 53L, 60L, 52L, 18L, 19L, 22L, 28L, 33L, 39L,
49L, 55L, 58L, 60L, 64L, 65L, 16L, 65L, 21L, 28L, 29L, 30L, 31L,
37L, 38L, 43L, 49L, 50L, 52L, 54L, 55L, 59L, 62L, 59L, 25L, 46L,
33L, 19L, 20L, 22L, 26L, 31L, 41L, 43L, 49L, 50L, 51L, 55L, 56L,
57L, 18L, 20L, 29L, 33L, 35L, 37L, 38L, 39L, 43L, 44L, 45L, 47L,
50L, 53L, 64L, 28L, 41L, 18L, 20L, 42L, 47L, 48L, 50L, 53L, 54L,
57L, 21L, 19L, 26L, 53L, 25L, 18L, 20L, 25L, 26L, 30L, 36L, 58L,
60L, 57L, 39L, 50L, 22L, 28L, 37L, 45L, 47L, 54L, 55L, 56L, 58L,
62L, 30L, 43L, 28L, 28L, 44L, 47L, 52L, 53L, 56L, 20L, 40L, 44L,
46L, 47L, 52L, 53L, 56L, 22L, 41L, 50L, 51L, 55L, 28L, 50L, 59L,
56L, 60L, 42L, 46L, 18L, 20L, 21L, 22L, 25L, 28L, 30L, 33L, 34L,
36L, 38L, 40L, 42L, 45L, 47L, 55L, 67L, 17L, 42L, 44L, 50L, 14L,
15L, 20L, 30L, 33L, 37L, 39L, 41L, 42L, 46L, 48L, 54L, 57L, 23L,
27L, 20L, 28L, 30L, 31L, 33L, 34L, 35L, 38L, 39L, 40L, 41L, 43L,
44L, 53L, 54L, 55L, 56L, 58L, 22L, 33L, 39L, 19L, 22L, 25L, 37L,
38L, 50L, 28L, 31L, 39L, 52L, 22L, 25L, 27L, 29L, 37L, 39L, 40L,
41L, 43L, 48L, 51L, 52L, 53L, 56L, 18L, 22L, 35L, 37L, 52L, 14L,
30L, 17L, 22L, 26L, 30L, 32L, 33L, 35L, 36L, 37L, 39L, 40L, 41L,
42L, 43L, 45L, 50L, 51L, 57L, 60L, 51L, 26L, 30L, 31L, 35L, 36L,
38L, 52L, 56L, 57L, 29L, 35L, 42L, 50L, 54L, 27L, 41L, 42L, 22L,
24L, 27L, 29L, 30L, 34L, 35L, 39L, 43L, 44L, 50L, 52L, 54L, 55L,
56L, 57L, 62L, 45L, 38L, 18L, 21L, 22L, 29L, 37L, 44L, 45L, 48L,
53L, 62L, 31L, 18L, 19L, 23L, 26L, 28L, 34L, 41L, 42L, 43L, 45L,
46L, 49L, 52L, 54L, 55L, 57L, 62L, 53L, 35L, 18L, 19L, 23L, 26L,
27L, 35L, 38L, 43L, 48L, 50L, 56L, 23L, 33L, 46L, 58L, 16L, 20L,
32L, 33L, 41L, 50L, 52L, 54L, 36L, 17L, 29L, 32L, 41L, 42L, 43L,
52L, 59L, 32L, 50L, 54L, 22L, 26L, 39L, 45L, 47L, 48L, 51L, 53L,
58L, 20L, 33L, 52L, 26L, 30L, 25L, 29L, 42L, 45L, 55L, 59L, 49L,
53L, 28L, 31L, 32L, 45L), Yes = c(0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L,
0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), No = c(1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 0L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 2L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 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, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 2L, 2L, 0L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 0L, 0L,
3L, 2L, 1L, 0L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 2L, 2L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 2L, 1L,
2L, 0L, 1L, 1L, 2L, 2L, 2L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
2L, 1L, 1L, 0L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 2L, 2L, 2L,
0L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 0L,
2L, 0L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("Genre",
"Times", "Type", "Age", "Yes", "No"), row.names = c(NA, -474L
), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), vars = c("Genre",
"Times", "Type"), drop = TRUE)
(2) proposition du modèle : converti le comportement en (yes,no) avec yes=1 et no= 0 si apparition du comportement et vice versa. Je propose de lier l'apparition du choix a genre , type de client, age et temps de reflexion.
Code:
attach(res)
model1=glm(cbind(Yes,No) ~ Genre + Times + Type + Age, family=binomial)
summary(model1)

Code:
Coefficients:
             Estimate Std. Error z value Pr(>|z|)  
(Intercept)  0.208933   0.478947   0.436  0.66267  
GenreMale   -0.260949   0.233441  -1.118  0.26364  
[b]Times       -0.027336   0.010159  -2.691  0.00713 **
TypeRegular -0.976515   0.323165  -3.022  0.00251 **[/b]
Age         -0.006787   0.009069  -0.748  0.45420  

Type de client et temps de réflexion sont significatif
(3)Evalutation GOF (Goodness Of Fit)

Code:
c(deviance(model1), 1-pchisq(deviance(model1),469))
[1] 431.4650065   0.8921334
> #Pearson residual
> pearres2 = residuals(model1,type="pearson")
> pearson.tvalue = sum(pearres2^2)
> c(pearson.tvalue, 1-pchisq(pearson.tvalue,469))
[1] 480.1987723   0.3503006
P>0.05, h0 stipule le GOF, Nous ne rejetons pas le H0 dans le deux cas

(4)AIC : détermination de la meilleur combinaison des variables

Code:
step(glm(cbind(Yes,No) ~  Genre + Times + Type + Age , family=binomial), direction="both")
Start:  AIC=463.2
cbind(Yes, No) ~ Genre + Times + Type + Age

        Df Deviance    AIC
- Age    1   432.03 461.76
- Genre  1   432.72 462.45
<none>       431.47 463.20
- Times  1   439.31 469.05
- Type   1   439.86 469.60

Step:  AIC=461.76
cbind(Yes, No) ~ Genre + Times + Type

        Df Deviance    AIC
- Genre  1   433.17 460.91
<none>       432.03 461.76
+ Age    1   431.47 463.20
- Type   1   440.58 468.32
- Times  1   440.60 468.34

Step:  AIC=460.91
cbind(Yes, No) ~ Times + Type

        Df Deviance    AIC
<none>       433.17 460.91
+ Genre  1   432.03 461.76
+ Age    1   432.72 462.45
- Times  1   441.72 467.46
- Type   1   441.92 467.66

Call:  glm(formula = cbind(Yes, No) ~ Times + Type, family = binomial)

Coefficients:
(Intercept)        Times  TypeRegular  
   -0.14906     -0.02823     -0.99418  

Degrees of Freedom: 473 Total (i.e. Null);  471 Residual
Null Deviance:      452.2
Residual Deviance: 433.2        AIC: 460.9

(5) Best Fit modele:
Code:
(Yes, No) ~ β0 + Times + Type

est-ce que ma démarche est correcte ainsi ?

(B)  Pour un autre dataset ou le GOF n'est pas bon, comment faire pour l'améliorer ?
Code:
res=structure(list(Motif = 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, 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, 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, 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, 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, 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,
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), .Label = c("Home",
"Other"), class = "factor"), Type = 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, 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, 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, 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, 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, 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, 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, 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), .Label = c("Irregular",
"Regular"), class = "factor"), Genre = structure(c(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, 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, 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, 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, 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, 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, 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, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Female",
"Male"), class = "factor"), Times = c(4L, 6L, 6L, 6L, 9L, 9L,
9L, 9L, 11L, 14L, 14L, 17L, 20L, 23L, 26L, 58L, 6L, 6L, 6L, 11L,
14L, 14L, 14L, 16L, 17L, 20L, 23L, 30L, 36L, 43L, 50L, 51L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L,
16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 18L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 25L, 25L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 32L, 33L, 33L, 33L,
33L, 33L, 33L, 33L, 33L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L,
36L, 36L, 36L, 36L, 37L, 38L, 39L, 43L, 43L, 43L, 43L, 43L, 43L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 50L, 50L, 50L, 50L, 50L,
51L, 51L, 51L, 52L, 58L, 58L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 29L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L,
33L, 33L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 37L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 47L, 47L, 50L, 51L, 51L,
51L, 51L, 51L, 51L, 52L, 52L, 58L, 58L, 58L, 4L, 4L, 9L, 9L,
17L, 18L, 23L, 25L, 32L, 33L, 35L, 6L, 9L, 9L, 11L, 11L, 14L,
18L, 25L, 28L, 4L, 9L, 30L, 6L, 9L, 17L, 23L, 33L, 33L, 58L),
    Age = c(20L, 36L, 38L, 63L, 33L, 35L, 43L, 51L, 27L, 17L,
    22L, 45L, 52L, 65L, 33L, 60L, 42L, 44L, 50L, 39L, 28L, 31L,
    52L, 14L, 17L, 29L, 42L, 35L, 36L, 54L, 52L, 30L, 18L, 23L,
    24L, 25L, 26L, 30L, 35L, 37L, 53L, 56L, 10L, 19L, 21L, 25L,
    26L, 27L, 33L, 34L, 36L, 37L, 39L, 40L, 44L, 51L, 54L, 56L,
    59L, 16L, 18L, 22L, 25L, 27L, 32L, 33L, 34L, 35L, 36L, 39L,
    40L, 46L, 47L, 50L, 52L, 54L, 56L, 57L, 59L, 60L, 61L, 25L,
    28L, 31L, 37L, 48L, 53L, 55L, 19L, 22L, 25L, 26L, 27L, 37L,
    40L, 50L, 51L, 55L, 56L, 58L, 28L, 34L, 43L, 45L, 49L, 74L,
    46L, 48L, 50L, 55L, 20L, 24L, 27L, 30L, 35L, 38L, 39L, 41L,
    44L, 47L, 50L, 30L, 51L, 53L, 60L, 18L, 19L, 22L, 28L, 33L,
    39L, 49L, 55L, 58L, 60L, 64L, 65L, 21L, 28L, 29L, 30L, 31L,
    37L, 38L, 43L, 49L, 50L, 52L, 54L, 55L, 59L, 62L, 25L, 46L,
    19L, 20L, 22L, 26L, 31L, 41L, 43L, 49L, 50L, 51L, 55L, 56L,
    57L, 18L, 20L, 29L, 33L, 35L, 37L, 38L, 39L, 43L, 44L, 45L,
    47L, 50L, 53L, 64L, 28L, 41L, 20L, 42L, 47L, 48L, 50L, 53L,
    54L, 57L, 19L, 26L, 53L, 18L, 20L, 25L, 26L, 30L, 36L, 58L,
    60L, 39L, 50L, 22L, 28L, 37L, 45L, 47L, 54L, 55L, 56L, 58L,
    62L, 30L, 43L, 28L, 28L, 44L, 47L, 52L, 53L, 56L, 20L, 40L,
    44L, 46L, 47L, 52L, 53L, 56L, 22L, 41L, 50L, 51L, 55L, 28L,
    50L, 59L, 56L, 42L, 46L, 18L, 20L, 21L, 22L, 25L, 28L, 30L,
    33L, 34L, 36L, 38L, 40L, 42L, 45L, 47L, 55L, 67L, 14L, 15L,
    20L, 30L, 33L, 37L, 39L, 41L, 42L, 46L, 48L, 54L, 57L, 20L,
    28L, 30L, 31L, 33L, 35L, 38L, 39L, 40L, 41L, 43L, 44L, 53L,
    54L, 55L, 56L, 58L, 19L, 22L, 25L, 37L, 38L, 50L, 22L, 25L,
    27L, 29L, 37L, 39L, 40L, 41L, 43L, 48L, 51L, 52L, 53L, 56L,
    18L, 22L, 35L, 37L, 52L, 30L, 22L, 26L, 30L, 32L, 33L, 35L,
    36L, 37L, 39L, 40L, 41L, 42L, 43L, 45L, 50L, 51L, 57L, 60L,
    26L, 30L, 31L, 35L, 36L, 38L, 52L, 56L, 57L, 35L, 42L, 50L,
    54L, 27L, 41L, 22L, 24L, 27L, 29L, 30L, 34L, 35L, 39L, 43L,
    44L, 50L, 52L, 54L, 55L, 56L, 57L, 62L, 38L, 18L, 21L, 22L,
    29L, 37L, 44L, 45L, 48L, 53L, 62L, 18L, 19L, 23L, 26L, 28L,
    34L, 41L, 42L, 43L, 45L, 46L, 49L, 52L, 54L, 55L, 57L, 62L,
    53L, 18L, 19L, 23L, 26L, 27L, 35L, 38L, 43L, 48L, 50L, 56L,
    23L, 33L, 46L, 58L, 16L, 32L, 33L, 41L, 50L, 52L, 17L, 29L,
    32L, 41L, 42L, 43L, 52L, 59L, 32L, 50L, 22L, 26L, 39L, 45L,
    47L, 48L, 51L, 53L, 58L, 20L, 33L, 26L, 25L, 29L, 42L, 45L,
    55L, 59L, 49L, 53L, 28L, 32L, 45L, 43L, 50L, 25L, 50L, 60L,
    22L, 16L, 59L, 21L, 25L, 57L, 17L, 23L, 27L, 22L, 33L, 39L,
    51L, 45L, 31L, 35L, 39L, 18L, 30L, 34L, 39L, 35L, 20L, 54L,
    31L), Yes = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
    0L, 1L, 1L), No = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    2L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
    1L, 1L, 1L, 0L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
    1L, 1L, 1L, 1L, 2L, 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, 0L, 2L,
    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, 1L, 1L,
    2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 1L,
    3L, 2L, 1L, 1L, 0L, 0L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
    2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
    1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L,
    1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 0L, 1L, 2L, 1L, 1L,
    1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 0L, 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, 0L, 1L, 1L,
    0L, 1L, 0L, 0L)), .Names = c("Motif", "Type",
"Genre", "Times", "Age", "Yes", "No"), row.names = c(NA, -479L
), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), vars = c("Motif",
"Type", "Genre", "Times"), drop = TRUE)

Code:

attach(res)
model1=glm(cbind(Yes,No) ~ Motif+ Type + Genre + Times + Age, family=binomial)
summary(model1)
#deviance residuals
> c(deviance(model1), 1-pchisq(deviance(model1),473))
[1] 109.6982   1.0000
> #Pearson residual
> pearres2 = residuals(model1,type="pearson")
> pearson.tvalue = sum(pearres2^2)
> c(pearson.tvalue, 1-pchisq(pearson.tvalue,473))
[1] 778.2204   0.0000

Merci beaucoup !

lenny868

Nombre de messages : 42
Date d'inscription : 16/01/2018

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Re: GLM, GOF, AIC, ma démarche est-elle correcte ?

Message par Eric Wajnberg le Mer 2 Mai 2018 - 5:36

A priori, ca ressemble à une bonne idée, à un seul détail près. Vous dites :
lenny868 a écrit:(2) proposition du modèle : converti le comportement en (yes,no) avec yes=1 et no= 0 si apparition du comportement et vice versa.
Comment se fait-il alors que certaines valeurs de "No" valent "2"? On n'est plus sur du binomial ! Ce point doit être résolu.

Enfin, si Yes et No sont vraiment à 0 & 1, et si Yes+No vaut toujours 1, alors l'ajustement de la régression logistique peut se faire avec:

Code:
model1=glm(Yes ~ Genre + Times + Type + Age, family=binomial)

Pas besoin du cbind().

HTH, Eric.
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Eric Wajnberg

Nombre de messages : 964
Date d'inscription : 14/09/2012

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Re: GLM, GOF, AIC, ma démarche est-elle correcte ?

Message par lenny868 le Mer 2 Mai 2018 - 7:39

Merci de 'avoir averti ! fallait voir tout le dataset pour s'en apercevoir bounce

Voilà après correction les données
Dataset du point (A)

Code:
res=structure(list(Times = c(9L, 4L, 25L, 23L, 50L, 9L, 4L, 20L,
36L, 25L, 28L, 32L, 28L, 26L, 26L, 26L, 26L, 4L, 16L, 9L, 25L,
26L, 28L, 32L, 4L, 6L, 6L, 6L, 6L, 6L, 4L, 44L, 15L, 9L, 4L,
4L, 9L, 6L, 26L, 33L, 44L, 44L, 4L, 36L, 14L, 4L, 4L, 36L, 9L,
32L, 32L, 4L, 44L, 26L, 9L, 6L, 4L, 33L, 26L, 26L, 26L, 23L,
26L, 9L, 14L, 36L, 44L, 4L, 35L, 32L, 28L, 28L, 9L, 36L, 6L,
4L, 14L, 36L, 26L, 9L, 9L, 9L, 4L, 4L, 14L, 33L, 15L, 4L, 4L,
58L, 26L, 4L, 33L, 9L, 4L, 4L, 4L, 39L, 26L, 9L, 6L, 33L, 28L,
33L, 20L, 33L, 6L, 14L, 20L, 50L, 58L, 17L, 36L, 28L, 51L, 33L,
50L, 16L, 26L, 4L, 33L, 50L, 9L, 26L, 28L, 4L, 58L, 9L, 17L,
6L, 14L, 58L, 28L, 9L, 6L, 50L, 9L, 9L, 9L, 4L, 26L, 9L, 9L,
14L, 36L, 44L, 20L, 26L, 50L, 6L, 6L, 9L, 16L, 14L, 11L, 44L,
9L, 58L, 9L, 14L, 9L, 36L, 28L, 17L, 28L, 23L, 11L, 33L, 6L,
14L, 36L, 9L, 9L, 11L, 17L, 17L, 20L, 9L, 14L, 11L, 20L, 6L,
4L, 9L, 14L, 11L, 4L, 6L, 14L, 23L, 36L, 23L, 20L, 11L, 9L, 9L,
14L, 26L, 9L, 6L, 16L, 18L, 23L, 43L, 23L, 6L, 6L, 9L, 28L, 20L,
58L, 36L, 11L, 51L, 20L, 26L, 33L, 9L, 6L, 9L, 17L, 14L, 58L,
11L, 20L, 6L, 17L, 14L, 28L, 16L, 6L, 6L, 28L, 6L, 6L, 9L, 28L,
9L, 22L, 14L, 6L, 6L, 14L, 17L, 36L, 37L, 20L, 20L, 35L, 23L,
9L, 25L, 25L, 23L, 23L, 33L, 18L, 51L, 6L, 9L, 6L, 6L, 9L, 17L,
9L, 29L, 28L, 20L, 28L, 14L, 50L, 14L, 17L, 6L, 11L, 11L, 28L,
20L, 28L, 20L, 6L, 6L, 9L, 9L, 47L, 36L, 36L, 9L, 9L, 11L, 17L,
23L, 23L, 44L, 20L, 36L, 52L, 17L, 17L, 44L, 28L, 11L, 14L, 28L,
23L, 9L, 9L, 17L, 18L, 22L, 28L, 9L, 14L, 14L, 14L, 23L, 23L,
52L, 17L, 28L, 14L, 28L, 9L, 6L, 6L, 28L, 23L, 23L, 4L, 37L,
51L, 51L, 14L, 23L, 6L, 28L, 20L, 17L, 26L, 11L, 35L, 15L, 14L,
20L, 18L, 4L, 29L, 6L, 30L, 51L, 23L, 11L, 9L, 23L, 14L, 23L,
14L, 15L, 36L, 9L, 37L, 29L, 28L, 30L, 23L, 51L, 51L, 17L, 17L,
30L, 18L, 23L, 28L, 15L, 14L, 9L, 28L, 33L, 14L, 23L, 9L, 14L,
26L, 9L, 23L, 14L, 9L, 44L, 43L, 15L, 4L, 14L, 14L, 23L, 52L,
23L, 14L, 32L, 17L, 17L, 44L, 20L, 30L, 28L, 43L, 33L, 23L, 9L,
44L, 33L, 23L, 18L, 26L, 26L, 26L, 9L, 6L, 11L, 6L, 18L, 30L,
17L, 51L, 44L, 23L, 43L, 30L, 23L, 17L, 44L, 43L, 23L, 15L, 28L,
17L, 18L, 23L, 26L, 14L, 9L, 28L, 15L, 16L, 9L, 17L, 30L, 15L,
20L, 6L, 23L, 28L, 18L, 32L, 30L, 18L, 17L, 23L, 18L, 18L, 6L,
17L, 30L, 51L, 44L, 23L, 28L, 18L, 15L, 18L, 28L, 26L, 44L, 23L,
23L, 17L, 28L, 30L, 17L, 44L, 43L, 30L, 38L, 17L, 28L, 26L, 17L,
17L, 18L, 23L, 28L, 6L, 30L, 17L, 9L, 28L, 28L, 28L, 11L, 17L,
17L, 20L, 9L, 30L, 18L, 47L, 30L, 23L, 33L, 18L, 30L, 17L, 36L,
30L, 23L, 17L, 30L, 33L, 14L, 18L, 15L, 32L, 23L, 23L, 30L, 23L,
30L, 30L, 43L, 30L, 30L, 17L, 36L, 17L, 17L, 51L, 30L, 17L, 15L,
50L, 11L, 11L, 4L, 32L, 26L, 17L), Genre = c("Male", "Female",
"Male", "Female", "Female", "Male", "Female", "Female", "Female",
"Female", "Male", "Female", "Male", "Male", "Female", "Male",
"Male", "Male", "Female", "Female", "Female", "Female", "Male",
"Female", "Male", "Female", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Male", "Female", "Female", "Male", "Male",
"Female", "Male", "Female", "Male", "Female", "Male", "Male",
"Male", "Male", "Female", "Female", "Male", "Female", "Male",
"Male", "Female", "Female", "Male", "Male", "Male", "Male", "Male",
"Female", "Female", "Male", "Female", "Female", "Male", "Male",
"Female", "Female", "Female", "Male", "Male", "Female", "Female",
"Female", "Male", "Female", "Male", "Female", "Female", "Male",
"Male", "Male", "Female", "Male", "Female", "Female", "Female",
"Female", "Male", "Male", "Female", "Female", "Male", "Male",
"Male", "Male", "Female", "Female", "Female", "Male", "Female",
"Female", "Male", "Female", "Female", "Male", "Male", "Female",
"Male", "Female", "Female", "Male", "Male", "Male", "Female",
"Female", "Female", "Female", "Male", "Female", "Male", "Female",
"Female", "Male", "Male", "Male", "Female", "Male", "Female",
"Male", "Female", "Male", "Female", "Female", "Female", "Male",
"Female", "Female", "Female", "Male", "Male", "Female", "Female",
"Female", "Female", "Female", "Female", "Female", "Female", "Male",
"Female", "Female", "Female", "Female", "Male", "Male", "Male",
"Male", "Female", "Female", "Male", "Male", "Female", "Male",
"Male", "Male", "Male", "Male", "Female", "Male", "Male", "Female",
"Male", "Female", "Male", "Female", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Female", "Female", "Female", "Female",
"Male", "Female", "Male", "Male", "Male", "Male", "Female", "Male",
"Male", "Male", "Male", "Female", "Male", "Male", "Female", "Male",
"Male", "Female", "Male", "Male", "Male", "Female", "Female",
"Female", "Male", "Male", "Male", "Female", "Female", "Female",
"Male", "Female", "Female", "Female", "Male", "Female", "Female",
"Male", "Male", "Male", "Male", "Male", "Male", "Female", "Male",
"Female", "Female", "Female", "Female", "Female", "Male", "Male",
"Female", "Female", "Female", "Female", "Female", "Male", "Female",
"Female", "Female", "Male", "Male", "Male", "Female", "Female",
"Male", "Female", "Female", "Male", "Male", "Female", "Female",
"Male", "Male", "Male", "Female", "Female", "Male", "Female",
"Female", "Female", "Female", "Female", "Male", "Female", "Female",
"Female", "Male", "Female", "Male", "Female", "Male", "Female",
"Female", "Female", "Male", "Female", "Male", "Female", "Female",
"Female", "Male", "Male", "Male", "Female", "Female", "Female",
"Male", "Male", "Female", "Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Male", "Male", "Male", "Male",
"Female", "Female", "Female", "Male", "Male", "Female", "Male",
"Female", "Female", "Female", "Male", "Male", "Female", "Male",
"Female", "Male", "Female", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Female", "Male", "Male", "Female",
"Female", "Female", "Female", "Male", "Male", "Male", "Female",
"Female", "Female", "Male", "Female", "Female", "Female", "Female",
"Female", "Male", "Male", "Female", "Male", "Female", "Female",
"Female", "Male", "Female", "Female", "Female", "Female", "Male",
"Female", "Male", "Male", "Male", "Male", "Male", "Female", "Male",
"Female", "Female", "Male", "Female", "Male", "Male", "Male",
"Male", "Male", "Female", "Female", "Male", "Male", "Female",
"Male", "Male", "Male", "Male", "Male", "Male", "Female", "Male",
"Male", "Female", "Male", "Female", "Male", "Female", "Male",
"Female", "Male", "Male", "Male", "Female", "Female", "Male",
"Male", "Male", "Male", "Female", "Male", "Male", "Male", "Female",
"Female", "Female", "Male", "Female", "Male", "Female", "Female",
"Female", "Female", "Female", "Male", "Female", "Male", "Male",
"Female", "Female", "Female", "Male", "Female", "Male", "Female",
"Male", "Female", "Female", "Male", "Female", "Female", "Male",
"Female", "Male", "Male", "Female", "Male", "Female", "Female",
"Male", "Male", "Female", "Female", "Female", "Male", "Female",
"Male", "Male", "Female", "Male", "Female", "Male", "Female",
"Male", "Female", "Female", "Female", "Male", "Male", "Male",
"Female", "Female", "Female", "Male", "Female", "Female", "Female",
"Male", "Female", "Male", "Male", "Male", "Male", "Female", "Male",
"Male", "Male", "Female", "Male", "Male", "Female", "Female",
"Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Male", "Male", "Male", "Female", "Male",
"Male", "Male", "Male", "Female", "Male", "Female", "Female",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Male",
"Male", "Male", "Female", "Male", "Male", "Female", "Male", "Male",
"Male"), Type = c("Irregular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
"Irregular", "Irregular", "Irregular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Irregular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Irregular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Irregular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
"Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
"Regular", "Regular"), Age = c(27L, 18L, 38L, 16L, 50L, 30L,
26L, 65L, 28L, 25L, 57L, 26L, 28L, 53L, 26L, 21L, 21L, 25L, 55L,
16L, 59L, 22L, 45L, 19L, 40L, 10L, 54L, 51L, 30L, 20L, 22L, 22L,
37L, 39L, 50L, 35L, 20L, 44L, 26L, 32L, 20L, 26L, 56L, 36L, 31L,
30L, 38L, 58L, 40L, 58L, 53L, 34L, 48L, 55L, 27L, 48L, 47L, 16L,
29L, 45L, 19L, 49L, 48L, 34L, 26L, 52L, 39L, 30L, 39L, 21L, 19L,
34L, 39L, 62L, 63L, 21L, 50L, 43L, 50L, 25L, 54L, 55L, 42L, 43L,
29L, 26L, 43L, 37L, 25L, 31L, 21L, 23L, 30L, 30L, 55L, 18L, 45L,
28L, 51L, 43L, 15L, 18L, 39L, 52L, 52L, 36L, 20L, 52L, 64L, 52L,
42L, 45L, 17L, 19L, 29L, 60L, 55L, 48L, 43L, 67L, 58L, 26L, 34L,
56L, 62L, 36L, 32L, 51L, 30L, 54L, 56L, 60L, 49L, 50L, 40L, 51L,
28L, 59L, 35L, 20L, 53L, 35L, 54L, 27L, 22L, 46L, 33L, 33L, 41L,
34L, 42L, 39L, 46L, 58L, 25L, 58L, 33L, 28L, 39L, 22L, 25L, 59L,
49L, 50L, 46L, 54L, 37L, 20L, 50L, 22L, 32L, 30L, 25L, 25L, 60L,
26L, 55L, 44L, 53L, 19L, 29L, 36L, 28L, 54L, 56L, 48L, 35L, 39L,
28L, 37L, 41L, 22L, 54L, 50L, 57L, 56L, 40L, 22L, 34L, 21L, 14L,
35L, 65L, 54L, 42L, 38L, 14L, 28L, 55L, 64L, 46L, 37L, 39L, 45L,
42L, 20L, 20L, 35L, 17L, 46L, 20L, 19L, 45L, 55L, 28L, 33L, 45L,
52L, 42L, 30L, 37L, 33L, 18L, 56L, 36L, 60L, 50L, 47L, 27L, 22L,
25L, 19L, 51L, 24L, 55L, 32L, 60L, 19L, 50L, 44L, 41L, 45L, 46L,
28L, 56L, 25L, 51L, 30L, 46L, 32L, 19L, 37L, 39L, 60L, 18L, 28L,
45L, 58L, 29L, 22L, 50L, 17L, 33L, 26L, 28L, 31L, 23L, 49L, 52L,
22L, 30L, 37L, 33L, 32L, 33L, 45L, 29L, 22L, 27L, 37L, 17L, 24L,
30L, 40L, 18L, 54L, 49L, 41L, 47L, 44L, 53L, 48L, 40L, 20L, 21L,
54L, 23L, 22L, 31L, 41L, 47L, 36L, 22L, 51L, 27L, 30L, 50L, 56L,
44L, 38L, 43L, 54L, 52L, 42L, 59L, 43L, 38L, 57L, 20L, 50L, 25L,
25L, 25L, 30L, 39L, 33L, 50L, 39L, 49L, 53L, 57L, 74L, 48L, 35L,
51L, 53L, 41L, 27L, 18L, 28L, 30L, 27L, 33L, 59L, 25L, 39L, 37L,
52L, 47L, 56L, 30L, 53L, 64L, 47L, 55L, 50L, 55L, 47L, 45L, 56L,
26L, 27L, 31L, 28L, 39L, 61L, 50L, 54L, 22L, 54L, 40L, 40L, 44L,
40L, 31L, 55L, 38L, 51L, 28L, 35L, 33L, 25L, 41L, 35L, 53L, 29L,
27L, 33L, 35L, 39L, 47L, 42L, 20L, 34L, 56L, 41L, 55L, 53L, 53L,
25L, 56L, 57L, 53L, 18L, 57L, 58L, 57L, 38L, 44L, 22L, 50L, 32L,
59L, 47L, 50L, 44L, 50L, 43L, 24L, 45L, 53L, 52L, 18L, 45L, 27L,
30L, 55L, 31L, 39L, 50L, 45L, 45L, 50L, 43L, 39L, 48L, 22L, 39L,
41L, 34L, 39L, 52L, 53L, 53L, 31L, 35L, 62L, 53L, 60L, 41L, 30L,
23L, 42L, 56L, 43L, 35L, 56L, 34L, 56L, 38L, 41L, 52L, 62L, 30L,
51L, 44L, 54L, 24L, 53L, 47L, 42L, 43L, 57L, 18L, 62L, 40L, 37L,
36L, 52L, 41L, 42L, 48L, 41L, 33L, 26L, 43L, 37L, 33L, 26L, 32L,
42L, 31L, 18L, 26L, 20L, 43L, 35L, 33L, 38L, 50L, 37L, 42L, 35L,
52L, 43L, 35L, 50L, 37L, 30L, 49L, 46L, 54L, 29L, 38L, 54L, 27L,
57L, 52L, 26L, 23L, 36L, 56L, 38L, 50L, 59L, 19L, 42L, 18L, 22L,
22L, 22L, 24L, 23L, 37L, 40L), No = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 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, 0L, 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, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L), Yes = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L)), .Names = c("Times",
"Genre", "Type", "Age", "No", "Yes"), row.names = c(NA, -545L
), class = "data.frame")

dataset du point (B)

Code:
res=structure(list(Motif = structure(c(2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 1L, 1L, 1L, 2L, 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, 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, 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, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 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, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 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,
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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 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, 1L, 1L, 1L, 1L), .Label = c("Home",
"Other"), class = "factor"), Type = structure(c(1L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 2L, 2L, 2L, 2L), .Label = c("Irregular",
"Regular"), class = "factor"), Times = c(9L, 4L, 25L, 23L, 50L,
9L, 4L, 20L, 36L, 25L, 28L, 32L, 28L, 26L, 26L, 26L, 26L, 4L,
16L, 9L, 25L, 26L, 28L, 32L, 4L, 6L, 6L, 6L, 6L, 6L, 4L, 44L,
15L, 9L, 4L, 4L, 9L, 6L, 26L, 33L, 44L, 44L, 4L, 36L, 14L, 4L,
4L, 36L, 9L, 32L, 32L, 4L, 44L, 26L, 9L, 6L, 4L, 33L, 26L, 26L,
26L, 23L, 26L, 9L, 14L, 36L, 44L, 4L, 35L, 32L, 28L, 28L, 9L,
36L, 6L, 4L, 14L, 36L, 26L, 9L, 9L, 9L, 4L, 4L, 14L, 33L, 15L,
4L, 4L, 58L, 26L, 4L, 33L, 9L, 4L, 4L, 4L, 39L, 26L, 9L, 6L,
33L, 28L, 33L, 20L, 33L, 6L, 14L, 20L, 50L, 58L, 17L, 36L, 28L,
51L, 33L, 50L, 16L, 26L, 4L, 33L, 50L, 9L, 26L, 28L, 4L, 58L,
9L, 17L, 6L, 14L, 58L, 28L, 9L, 6L, 50L, 9L, 9L, 9L, 4L, 26L,
9L, 9L, 14L, 36L, 44L, 20L, 26L, 50L, 6L, 6L, 9L, 16L, 14L, 11L,
44L, 9L, 58L, 9L, 14L, 9L, 36L, 28L, 17L, 28L, 23L, 11L, 33L,
6L, 14L, 36L, 9L, 9L, 11L, 17L, 17L, 20L, 9L, 14L, 11L, 20L,
6L, 4L, 9L, 14L, 11L, 4L, 6L, 14L, 23L, 36L, 23L, 20L, 11L, 9L,
9L, 14L, 26L, 9L, 6L, 16L, 18L, 23L, 43L, 23L, 6L, 6L, 9L, 28L,
20L, 58L, 36L, 11L, 51L, 20L, 26L, 33L, 9L, 6L, 9L, 17L, 14L,
58L, 11L, 20L, 6L, 17L, 14L, 28L, 16L, 6L, 6L, 28L, 6L, 6L, 9L,
28L, 9L, 22L, 14L, 6L, 6L, 14L, 17L, 36L, 37L, 20L, 20L, 35L,
23L, 9L, 25L, 25L, 23L, 23L, 33L, 18L, 51L, 6L, 9L, 6L, 6L, 9L,
17L, 9L, 29L, 28L, 20L, 28L, 14L, 50L, 14L, 17L, 6L, 11L, 11L,
28L, 20L, 28L, 20L, 6L, 6L, 9L, 9L, 47L, 36L, 36L, 9L, 9L, 11L,
17L, 23L, 23L, 44L, 20L, 36L, 52L, 17L, 17L, 44L, 28L, 11L, 14L,
28L, 23L, 9L, 9L, 17L, 18L, 22L, 28L, 9L, 14L, 14L, 14L, 23L,
23L, 52L, 17L, 28L, 14L, 28L, 9L, 6L, 6L, 28L, 23L, 23L, 4L,
37L, 51L, 51L, 14L, 23L, 6L, 28L, 20L, 17L, 26L, 11L, 35L, 15L,
14L, 20L, 18L, 4L, 29L, 6L, 30L, 51L, 23L, 11L, 9L, 23L, 14L,
23L, 14L, 15L, 36L, 9L, 37L, 29L, 28L, 30L, 23L, 51L, 51L, 17L,
17L, 30L, 18L, 23L, 28L, 15L, 14L, 9L, 28L, 33L, 14L, 23L, 9L,
14L, 26L, 9L, 23L, 14L, 9L, 44L, 43L, 15L, 4L, 14L, 14L, 23L,
52L, 23L, 14L, 32L, 17L, 17L, 44L, 20L, 30L, 28L, 43L, 33L, 23L,
9L, 44L, 33L, 23L, 18L, 26L, 26L, 26L, 9L, 6L, 11L, 6L, 18L,
30L, 17L, 51L, 44L, 23L, 43L, 30L, 23L, 17L, 44L, 43L, 23L, 15L,
28L, 17L, 18L, 23L, 26L, 14L, 9L, 28L, 15L, 16L, 9L, 17L, 30L,
15L, 20L, 6L, 23L, 28L, 18L, 32L, 30L, 18L, 17L, 23L, 18L, 18L,
6L, 17L, 30L, 51L, 44L, 23L, 28L, 18L, 15L, 18L, 28L, 26L, 44L,
23L, 23L, 17L, 28L, 30L, 17L, 44L, 43L, 30L, 38L, 17L, 28L, 26L,
17L, 17L, 18L, 23L, 28L, 6L, 30L, 17L, 9L, 28L, 28L, 28L, 11L,
17L, 17L, 20L, 9L, 30L, 18L, 47L, 30L, 23L, 33L, 18L, 30L, 17L,
36L, 30L, 23L, 17L, 30L, 33L, 14L, 18L, 15L, 32L, 23L, 23L, 30L,
23L, 30L, 30L, 43L, 30L, 30L, 17L, 36L, 17L, 17L, 51L, 30L, 17L,
15L, 50L, 11L, 11L, 4L, 32L, 26L, 17L), Genre = structure(c(2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L
), .Label = c("Female", "Male"), class = "factor"), Age = c(27L,
18L, 38L, 16L, 50L, 30L, 26L, 65L, 28L, 25L, 57L, 26L, 28L, 53L,
26L, 21L, 21L, 25L, 55L, 16L, 59L, 22L, 45L, 19L, 40L, 10L, 54L,
51L, 30L, 20L, 22L, 22L, 37L, 39L, 50L, 35L, 20L, 44L, 26L, 32L,
20L, 26L, 56L, 36L, 31L, 30L, 38L, 58L, 40L, 58L, 53L, 34L, 48L,
55L, 27L, 48L, 47L, 16L, 29L, 45L, 19L, 49L, 48L, 34L, 26L, 52L,
39L, 30L, 39L, 21L, 19L, 34L, 39L, 62L, 63L, 21L, 50L, 43L, 50L,
25L, 54L, 55L, 42L, 43L, 29L, 26L, 43L, 37L, 25L, 31L, 21L, 23L,
30L, 30L, 55L, 18L, 45L, 28L, 51L, 43L, 15L, 18L, 39L, 52L, 52L,
36L, 20L, 52L, 64L, 52L, 42L, 45L, 17L, 19L, 29L, 60L, 55L, 48L,
43L, 67L, 58L, 26L, 34L, 56L, 62L, 36L, 32L, 51L, 30L, 54L, 56L,
60L, 49L, 50L, 40L, 51L, 28L, 59L, 35L, 20L, 53L, 35L, 54L, 27L,
22L, 46L, 33L, 33L, 41L, 34L, 42L, 39L, 46L, 58L, 25L, 58L, 33L,
28L, 39L, 22L, 25L, 59L, 49L, 50L, 46L, 54L, 37L, 20L, 50L, 22L,
32L, 30L, 25L, 25L, 60L, 26L, 55L, 44L, 53L, 19L, 29L, 36L, 28L,
54L, 56L, 48L, 35L, 39L, 28L, 37L, 41L, 22L, 54L, 50L, 57L, 56L,
40L, 22L, 34L, 21L, 14L, 35L, 65L, 54L, 42L, 38L, 14L, 28L, 55L,
64L, 46L, 37L, 39L, 45L, 42L, 20L, 20L, 35L, 17L, 46L, 20L, 19L,
45L, 55L, 28L, 33L, 45L, 52L, 42L, 30L, 37L, 33L, 18L, 56L, 36L,
60L, 50L, 47L, 27L, 22L, 25L, 19L, 51L, 24L, 55L, 32L, 60L, 19L,
50L, 44L, 41L, 45L, 46L, 28L, 56L, 25L, 51L, 30L, 46L, 32L, 19L,
37L, 39L, 60L, 18L, 28L, 45L, 58L, 29L, 22L, 50L, 17L, 33L, 26L,
28L, 31L, 23L, 49L, 52L, 22L, 30L, 37L, 33L, 32L, 33L, 45L, 29L,
22L, 27L, 37L, 17L, 24L, 30L, 40L, 18L, 54L, 49L, 41L, 47L, 44L,
53L, 48L, 40L, 20L, 21L, 54L, 23L, 22L, 31L, 41L, 47L, 36L, 22L,
51L, 27L, 30L, 50L, 56L, 44L, 38L, 43L, 54L, 52L, 42L, 59L, 43L,
38L, 57L, 20L, 50L, 25L, 25L, 25L, 30L, 39L, 33L, 50L, 39L, 49L,
53L, 57L, 74L, 48L, 35L, 51L, 53L, 41L, 27L, 18L, 28L, 30L, 27L,
33L, 59L, 25L, 39L, 37L, 52L, 47L, 56L, 30L, 53L, 64L, 47L, 55L,
50L, 55L, 47L, 45L, 56L, 26L, 27L, 31L, 28L, 39L, 61L, 50L, 54L,
22L, 54L, 40L, 40L, 44L, 40L, 31L, 55L, 38L, 51L, 28L, 35L, 33L,
25L, 41L, 35L, 53L, 29L, 27L, 33L, 35L, 39L, 47L, 42L, 20L, 34L,
56L, 41L, 55L, 53L, 53L, 25L, 56L, 57L, 53L, 18L, 57L, 58L, 57L,
38L, 44L, 22L, 50L, 32L, 59L, 47L, 50L, 44L, 50L, 43L, 24L, 45L,
53L, 52L, 18L, 45L, 27L, 30L, 55L, 31L, 39L, 50L, 45L, 45L, 50L,
43L, 39L, 48L, 22L, 39L, 41L, 34L, 39L, 52L, 53L, 53L, 31L, 35L,
62L, 53L, 60L, 41L, 30L, 23L, 42L, 56L, 43L, 35L, 56L, 34L, 56L,
38L, 41L, 52L, 62L, 30L, 51L, 44L, 54L, 24L, 53L, 47L, 42L, 43L,
57L, 18L, 62L, 40L, 37L, 36L, 52L, 41L, 42L, 48L, 41L, 33L, 26L,
43L, 37L, 33L, 26L, 32L, 42L, 31L, 18L, 26L, 20L, 43L, 35L, 33L,
38L, 50L, 37L, 42L, 35L, 52L, 43L, 35L, 50L, 37L, 30L, 49L, 46L,
54L, 29L, 38L, 54L, 27L, 57L, 52L, 26L, 23L, 36L, 56L, 38L, 50L,
59L, 19L, 42L, 18L, 22L, 22L, 22L, 24L, 23L, 37L, 40L), No = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 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, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 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, 0L, 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, 0L, 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, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 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, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 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
), Yes = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L)), .Names = c("Motif", "Type", "Times",
"Genre", "Age", "No", "Yes"), row.names = c(NA, -545L), class = "data.frame")

lenny868

Nombre de messages : 42
Date d'inscription : 16/01/2018

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Re: GLM, GOF, AIC, ma démarche est-elle correcte ?

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