r - Emmeans 连续自变量-6ren

r - Emmeans 连续自变量

转载作者：行者123 更新时间：2023-12-05 09:15:28

我想用实验的Type_space和Exhaustion_product的速率和定量变量Age来解释Type_f .

这是我的数据:

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, 
        1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 
        1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 
        1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 
        1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 
        1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 
        1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 
        1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 
        1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
        0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 
        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 
        0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 
        0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 
        0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 
        0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 
        1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 
        0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 
        1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 
        0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 
        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
        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, 
    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, 73L, 75L, 76L, 79L, 80L, 81L, 82L, 84L, 85L, 
    86L, 91L, 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, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 
    177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 
    188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 197L, 198L, 199L, 
    201L, 202L, 203L, 204L, 206L, 207L, 208L, 209L, 210L, 212L, 213L, 
    214L, 215L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 225L, 227L, 
    229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 
    242L, 243L, 244L, 246L, 247L, 248L, 249L, 250L, 251L, 253L, 254L, 
    256L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 269L, 270L, 
    272L, 273L, 274L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 
    284L, 285L, 287L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 
    297L, 298L, 300L, 301L, 302L, 303L, 306L, 308L, 309L, 311L, 312L, 
    313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 322L, 323L, 325L, 
    326L, 327L, 328L, 329L, 331L, 332L, 334L, 335L, 336L, 338L, 339L, 
    340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 
    352L, 353L, 354L, 356L, 357L, 358L, 359L, 360L, 361L, 363L, 364L, 
    365L, 366L, 367L, 368L, 369L, 370L, 372L, 373L, 374L, 375L, 376L, 
    377L, 378L, 379L, 380L, 381L, 382L, 384L, 385L, 387L, 388L, 389L, 
    391L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 
    404L, 407L, 408L, 409L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 
    418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 
    429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 
    440L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 
    452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 
    463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 
    474L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 486L, 
    487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 
    498L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 
    510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 
    521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 
    532L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 
    547L, 548L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 
    559L, 560L, 561L, 562L, 563L, 565L, 566L, 567L, 568L, 569L, 570L, 
    571L, 572L, 573L, 575L, 577L, 579L, 580L, 581L, 582L, 583L, 585L, 
    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) 对于分类变量，结果很清楚。但是对于在 GLM 中很重要的年龄，emmeans 中生成的值是多少？5.455426。这就是手段吗？我该如何解释？

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

(2) 我想生成交互 age 和 Exhaustion_product 的图形表示。这也没有意义。

emmip(gl, Exhaustion_product ~ Age)

编辑 1 对比结果

$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

最佳答案

因为这道题好像是自学题，所以我准备做一个类似的例子，而不是相同的数据。但结构是相同的，具有一个因子和一个协变量作为预测变量。

示例是 emmeans::fiber 数据集。它的响应变量是纤维强度，连续预测变量是直径，因子是制造它的机器。

型号:

> 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)

使用 emmeans 的分析基于引用网格，默认情况下它由所有水平的因子和协变量的平均值组成:

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

您可以在 R 中确认 mean(fiber$diameter) 是 24.133。我强调这是直径值的平均值，而不是模型中任何值的平均值。

> 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.

这些汇总值是 mod 在 machine 和 diameter 的每种组合下的预测。现在查看 machine

的 EMM

> 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

...我们得到完全相同的三个预测。但是如果我们看一下 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

...我们得到 EMM 等于引用网格中三个预测值的平均值。请注意，它在注释中说结果是在 machine 上平均的，因此值得一读。

要获得模型结果的图形表示，我们可以这样做

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

添加了参数 cov.reduce = range 以使引用网格使用最小和最大直径，而不是其平均值。如果没有它，我们就会得到三个点而不是三行。该图仍然显示模型预测，只是在更详细的值网格上。请注意，所有三条线都具有相同的斜率。那是因为模型是这样指定的:直径 效果添加到机器 效果。因此，每条线的共同斜率为 0.023557(请参阅 summary(mod) 的输出。

diameter 不需要事后测试，因为它的一个效果已经在summary(mod).

最后一件事。该模型使用 log(strength) 作为响应。如果我们想要与 strength 规模相同的 EMM，只需添加 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

同样，结果下方的注释有助于解释输出。

关于r - Emmeans 连续自变量，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/52381434/

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r - Emmeans 连续自变量