Emmeans непрерывная независимая переменная

Question

Я хочу объяснить Type_f с помощью Type_space эксперимента и показателя Exhaustion_product и количественной переменной Age.

Вот мои данные:

res=structure(list(Type_space = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("", 
    "29-v1", "29-v2", "88-v1", "88-v2"), class = "factor"), Id = c(1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
    16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
    29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
    42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 
    55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 
    68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 
    81L, 82L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
    13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
    26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 
    39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
    52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 
    65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 
    78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 
    91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 
    103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 
    114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 
    125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 
    136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 
    147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 
    158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
    16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
    29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
    42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 
    55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 
    68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 
    81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 
    94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 1L, 2L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
    17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
    30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 
    43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 
    56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 
    69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 
    82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 
    95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 
    106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 
    117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 
    128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 
    139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 
    150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 
    161L, 162L, 163L, 164L), Age = c(3, 10, 1, 5, 4, 2, 1, 8, 2, 
    13, 1, 6, 3, 5, 2, 1, 3, 8, 3, 6, 1, 3, 7, 1, 2, 2, 2, 1, 2, 
    5, 4, 1, 6, 3, 6, 8, 2, 3, 4, 7, 3, 2, 6, 2, 3, 7, 1, 5, 4, 1, 
    4, 3, 2, 3, 5, 5, 2, 1, 1, 5, 8, 7, 2, 2, 4, 3, 4, 4, 2, 2, 10, 
    7, 5, 3, 3, 5, 7, 5, 3, 4, 5, 4, 1, 8, 6, 1, 12, 1, 6, 3, 4, 
    4, 13, 5, 2, 7, 7, 20, 1, 1, 1, 7, 1, 4, 3, 8, 2, 2, 4, 1, 1, 
    2, 3, 2, 2, 6, 11, 2, 5, 5, 9, 4, 4, 2, 7, 2, 7, 10, 6, 9, 2, 
    2, 5, 11, 1, 8, 8, 4, 1, 2, 14, 11, 13, 20, 3, 3, 4, 16, 2, 6, 
    11, 9, 11, 4, 5, 6, 19, 5, 2, 6, 1, 7, 11, 3, 9, 2, 3, 6, 20, 
    8, 6, 2, 11, 18, 9, 3, 7, 3, 2, 1, 8, 3, 5, 6, 2, 5, 8, 11, 4, 
    9, 7, 2, 12, 8, 2, 9, 5, 4, 15, 5, 13, 5, 10, 13, 7, 6, 1, 12, 
    12, 10, 4, 2, 16, 7, 17, 11, 18, 4, 3, 12, 1, 3, 7, 3, 6, 5, 
    11, 10, 12, 6, 14, 8, 6, 7, 8, 5, 10, 12, 6, 13, 3, 11, 14, 7, 
    9, 9, 4, 13, 4, 2, 1, 2, 2, 1, 7, 9, 3, 10, 3, 2, 1, 3, 1, 4, 
    2, 4, 5, 4, 2, 13, 4, 1, 3, 1, 11, 4, 1, 3, 3, 7, 5, 4, 5, 6, 
    1, 2, 1, 2, 1, 6, 1, 7, 6, 9, 5, 1, 6, 3, 2, 3, 3, 8, 8, 3, 2, 
    2, 4, 2, 5, 2, 6, 8, 11, 1, 6, 3, 3, 4, 5, 5, 7, 4, 2, 7, 3, 
    3, 1, 3, 9, 5, 2, 4, 12, 1, 4, 5, 2, 7, 6, 1, 2, 6, 4, 2, 7, 
    3, 5, 5, 3, 7, 1, 5, 2, 1, 15, 3, 5, 2, 5, 13, 6, 2, 3, 5, 2, 
    8, 4, 2, 6, 7, 2, 4, 1, 13, 8, 2, 1, 2, 1, 1, 5, 2, 1, 6, 11, 
    4, 1, 7, 7, 4, 3, 5, 1, 4, 10, 1, 2, 6, 1, 11, 3, 8, 9, 2, 6, 
    8, 11, 14, 16, 4, 1, 4, 2, 1, 10, 4, 9, 3, 12, 8, 11, 8, 8, 5, 
    1, 4, 13, 3, 8, 5, 14, 3, 5, 5, 12, 1, 3, 4, 5, 2, 7, 6, 9, 6, 
    10, 5, 2, 3, 2, 10, 10, 10, 10, 10, 1, 14, 3, 5, 9, 6, 2, 2, 
    2, 4, 4, 11, 14, 2, 2, 2, 8, 7, 2, 10, 12, 1, 6, 10, 2, 3, 5, 
    10, 6, 1, 8, 4, 11, 5, 4, 3, 6, 2, 4, 6, 9, 3, 9, 11, 7, 3, 15, 
    3, 7, 3, 5, 4, 6, 9, 13, 8, 5, 7, 8, 8, 5, 10), Type_product = c("f", 
    "s", "f", "f", "f", "f", "s", "c", "s", "f", "c", "f", "f", "f", 
    "s", "s", "f", "f", "c", "f", "s", "f", "f", "s", "f", "c", "f", 
    "f", "s", "f", "f", "c", "f", "c", "f", "f", "f", "f", "f", "c", 
    "c", "c", "f", "f", "c", "c", "f", "c", "c", "c", "c", "c", "s", 
    "f", "c", "c", "c", "s", "f", "c", "f", "f", "c", "c", "f", "c", 
    "c", "c", "f", "c", "c", "c", "c", "c", "c", "c", "f", "c", "c", 
    "c", "c", "f", "c", "f", "f", "s", "f", "c", "f", "f", "f", "c", 
    "f", "f", "f", "f", "f", "s", "c", "c", "f", "f", "c", "c", "f", 
    "f", "c", "c", "f", "f", "s", "f", "c", "c", "f", "f", "f", "c", 
    "f", "f", "f", "c", "f", "f", "f", "f", "f", "f", "c", "f", "f", 
    "f", "f", "c", "s", "f", "c", "f", "f", "c", "f", "f", "f", "c", 
    "f", "c", "c", "c", "f", "f", "f", "f", "c", "c", "c", "f", "f", 
    "c", "c", "f", "c", "f", "f", "c", "c", "c", "c", "f", "f", "f", 
    "c", "c", "c", "f", "c", "f", "c", "f", "f", "f", "c", "f", "c", 
    "c", "c", "c", "c", "f", "c", "c", "c", "c", "c", "c", "c", "f", 
    "f", "f", "c", "f", "c", "f", "f", "c", "c", "f", "f", "f", "c", 
    "c", "c", "f", "c", "c", "c", "c", "c", "f", "c", "f", "f", "c", 
    "c", "f", "c", "f", "c", "f", "c", "c", "c", "f", "c", "c", "c", 
    "c", "c", "c", "c", "f", "c", "c", "f", "c", "c", "f", "f", "c", 
    "f", "f", "s", "c", "s", "c", "f", "c", "c", "s", "c", "c", "s", 
    "c", "m", "c", "c", "f", "f", "f", "f", "f", "f", "s", "f", "f", 
    "c", "c", "f", "c", "f", "f", "f", "c", "f", "f", "f", "s", "f", 
    "f", "c", "f", "c", "f", "m", "c", "c", "c", "f", "s", "f", "f", 
    "f", "c", "s", "c", "m", "f", "c", "m", "c", "f", "c", "f", "f", 
    "f", "c", "m", "f", "c", "c", "f", "c", "f", "c", "c", "c", "c", 
    "c", "f", "f", "f", "c", "m", "f", "m", "m", "c", "c", "c", "c", 
    "m", "m", "c", "f", "m", "m", "m", "m", "m", "m", "m", "m", "m", 
    "c", "c", "f", "f", "f", "f", "c", "f", "m", "f", "f", "f", "c", 
    "f", "f", "f", "c", "f", "f", "c", "c", "f", "c", "f", "c", "m", 
    "f", "c", "f", "c", "f", "f", "f", "f", "c", "c", "f", "f", "c", 
    "c", "f", "f", "f", "f", "f", "f", "c", "f", "c", "c", "f", "c", 
    "f", "f", "f", "f", "f", "f", "f", "c", "f", "c", "f", "c", "f", 
    "c", "f", "c", "f", "f", "c", "c", "c", "c", "c", "f", "f", "f", 
    "c", "f", "c", "f", "f", "c", "c", "f", "f", "c", "f", "c", "f", 
    "c", "c", "c", "f", "f", "c", "f", "c", "c", "f", "c", "f", "c", 
    "f", "c", "f", "c", "m", "c", "c", "m", "c", "c", "f", "c", "c", 
    "f", "c", "c", "c", "f", "c", "c", "m", "c", "m", "m", "c", "c", 
    "f", "c", "c", "c", "c", "m", "c", "c", "c", "m", "m", "m", "c", 
    "c", "c", "c", "m", "m", "f", "m", "m", "m", "m", "m", "m", "m", 
    "m", "m", "m", "m", "m", "m", "m", "m"), Exhaustion_product = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L), .Label = c("(0,10]", "(10,20]", "(20,30]", "(30,40]", "(40,50]", 
    "(50,60]", "(60,70]", "(70,80]", "(80,90]", "(90,100]"), class = "factor"), 
        Type_f = c(1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 
        1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 
        1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 
        0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 
        0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 
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        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0)), .Names = c("Type_space", "Id", "Age", 
    "Type_product", "Exhaustion_product", "Type_f"), row.names = c(1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
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    586L, 587L, 590L, 592L, 599L, 606L, 608L), class = "data.frame")

    an=Anova(glm(Type_f ~  Type_space  + Exhaustion_product + Age , family=binomial,data=res))
    gl=glm(Type_f ~  Type_space  + Exhaustion_product + Age  , family=binomial,data=res)
    library("emmeans")
    emmp <- emmeans( gl, pairwise ~ Exhaustion_product + Age)
    summary( emmp, infer=TRUE)

(1) В случае категориальной переменной результаты очевидны.Но в случае возраста, который имеет значение в GLM, какое значение генерируется в emmeans? 5.455426. Это означает?Как я могу интерпретировать это?

 (0,10]             5.455426  0.36901411 0.2935894 Inf -0.20641061  0.94443883   1.257  0.2088

(2) Я хочу создать графическое представление взаимодействия age и Exhaustion_product.Также это не имеет смысла.

emmip(gl, Exhaustion_product ~ Age)

Редактировать 1 Контрастность

$contrasts
 contrast                                                estimate        SE  df   asymp.LCL asymp.UCL z.ratio p.value
 (0,10],5.45542635658915 - (10,20],5.45542635658915    0.33231353 0.4078967 Inf -0.95814279 1.6227698   0.815  0.9984
 (0,10],5.45542635658915 - (20,30],5.45542635658915   -0.53694399 0.4194460 Inf -1.86393835 0.7900504  -1.280  0.9582
 (0,10],5.45542635658915 - (30,40],5.45542635658915   -0.16100309 0.4139472 Inf -1.47060101 1.1485948  -0.389  1.0000
 (0,10],5.45542635658915 - (40,50],5.45542635658915    0.40113723 0.4021403 Inf -0.87110757 1.6733820   0.998  0.9925
 (0,10],5.45542635658915 - (50,60],5.45542635658915    0.60576562 0.4106536 Inf -0.69341247 1.9049437   1.475  0.9022
 (0,10],5.45542635658915 - (60,70],5.45542635658915    1.38800301 0.4319258 Inf  0.02152631 2.7544797   3.214  0.0430
 (0,10],5.45542635658915 - (70,80],5.45542635658915    1.01677522 0.4147441 Inf -0.29534399 2.3288944   2.452  0.2952
 (0,10],5.45542635658915 - (80,90],5.45542635658915    1.99085692 0.4747929 Inf  0.48876247 3.4929514   4.193  0.0011
 (0,10],5.45542635658915 - (90,100],5.45542635658915   2.03923289 0.4745872 Inf  0.53778910 3.5406767   4.297  0.0007

Answer 1

Поскольку этот вопрос кажется самообучающимся, я собираюсь привести аналогичный пример, а не те же данные.Но структура та же, с одним фактором и одним ковариатом в качестве предикторов.

Примером является набор данных emmeans::fiber.Его переменная ответа - прочность волокна, непрерывный предиктор - диаметр, а фактор - машина, на которой он был изготовлен.

Модель:

> mod = glm(log(strength) ~ machine + diameter, data = fiber)
> summary(mod)
... (output has been abbreviated) ...
Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  3.124387   0.068374  45.695 6.74e-14
machineB     0.026025   0.023388   1.113    0.290
machineC    -0.044593   0.025564  -1.744    0.109
diameter     0.023557   0.002633   8.946 2.22e-06

(Dispersion parameter for gaussian family taken to be 0.001356412)

Анализ с использованием emmeans основан на справочной информациисетка, которая по умолчанию состоит из всех уровней фактора и среднего значения ковариаты:

> ref_grid(mod)
'emmGrid' object with variables:
    machine = A, B, C
    diameter = 24.133
Transformation: “log” 

Вы можете подтвердить в R, что mean(fiber$diameter) равно 24,133.Я подчеркиваю, что это среднее значение диаметра , а не чего-либо в модели.

> summary(.Last.value)
 machine diameter prediction         SE  df
 A       24.13333   3.692901 0.01670845 Inf
 B       24.13333   3.718925 0.01718853 Inf
 C       24.13333   3.648307 0.01819206 Inf

Results are given on the log (not the response) scale.

Эти итоговые значения являются предсказаниями mod для каждой комбинации machineи diameter.Теперь посмотрим на EMM для machine

> emmeans(mod, "machine")
 machine   emmean         SE  df asymp.LCL asymp.UCL
 A       3.692901 0.01670845 Inf  3.660153  3.725649
 B       3.718925 0.01718853 Inf  3.685237  3.752614
 C       3.648307 0.01819206 Inf  3.612652  3.683963

Results are given on the log (not the response) scale. 
Confidence level used: 0.95

... мы получаем точно такие же три предсказания.Но если мы посмотрим на diameter:

> emmeans(mod, "diameter")
 diameter   emmean          SE  df asymp.LCL asymp.UCL
 24.13333 3.686711 0.009509334 Inf  3.668073  3.705349

Results are averaged over the levels of: machine 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95

... мы получим, что EMM равно среднему из трех предсказанных значений в эталонной сетке.И обратите внимание, что в аннотациях говорится, что результаты были усреднены по machine, поэтому стоит прочитать это.

Чтобы получить графическое представление результатов модели, мы можем сделать

> emmip(mod, machine ~ diameter, cov.reduce = range)

Аргумент cov.reduce = range добавлен, чтобы заставить опорную сетку использовать минимальный и максимальный диаметр, а не ее среднее значение.Без этого мы бы получили три точки вместо трех линий.На этом графике все еще показаны предсказания модели, только более детальная сетка значений.Обратите внимание, что все три линии имеют одинаковый наклон.Это потому, что модель была указана таким образом: эффект diameter равен , добавлен к эффекту machine.Таким образом, каждая линия имеет общий наклон 0,023557 (см. Вывод summary(mod).

. Для diameter теста post hoc не требуется, поскольку one Эффект уже протестирован в summary(mod).

И последнее. Модель использует log(strength) в качестве ответа. Если мы хотим, чтобы EMM были в том же масштабе, что и strength, просто добавьте type = "response":

> emmeans(mod, "machine", type = "response")
 machine response        SE  df asymp.LCL asymp.UCL
 A       40.16118 0.6710311 Inf  38.86728  41.49815
 B       41.22008 0.7085126 Inf  39.85455  42.63239
 C       38.40960 0.6987496 Inf  37.06421  39.80384

Confidence level used: 0.95 
Intervals are back-transformed from the log scale

Опять же, аннотации под результатами помогают объяснить вывод.