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

par lenny868 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 !

par Eric Wajnberg 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.

par lenny868 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", 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"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", 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"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", 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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")

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

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

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

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

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