Emmeans 连续自变量 [英] Emmeans continuous independant variable
本文介绍了Emmeans 连续自变量的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我想用实验的Type_space和Exhaustion_product的比率和数量变量Age来解释Type_f.
I want to explan Type_f with Type_space of the experiment and the rate of Exhaustion_product and quantitative variable Age.
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,
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0, 0, 0, 0, 0, 0)), .Names = c("Type_space", "Id", "Age",
"Type_product", "Exhaustion_product", "Type_f"), row.names = c(1L,
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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) 在分类变量的情况下，结果很清楚.但是在年龄在 GLM 中很重要的情况下，emmeans 中生成的值是多少?5.455426.那是平均值吗?我该如何解释?
(1) In the case of categorical variable the results are clear. But in the case of Age which is significant in the GLM, what is the value generated in the emmeans ?5.455426.Is that is means ? How can I interpret this ?
(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.这也没有道理.
(2)I want to generate graphic representationof the interaction age and Exhaustion_product. Also this do not make sens.
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
推荐答案
因为这个问题好像是自学的，所以我准备做一个类似的例子，不是同一个数据.但结构是一样的，只有一个因子和一个协变量作为预测变量.
Because this question seems like a self-learning one, I am going to do a similar example, not the same data. But the structure is the same, with one factor and one covariate as predictors.
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.
The example is the emmeans::fiber dataset. Its response variable is fiber strength, the continuous predictor is the diameter, and the factor is the machine it was made on.
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)
emmeans 分析基于参考网格，默认情况下，该网格包含因子的所有水平和协变量的均值:
Analysis with emmeans is based on the reference grid, which by default consists of all levels of the factor and the mean of the covariate:
> 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.
You can confirm in R that mean(fiber$diameter) is 24.133. I emphasize this is the mean of the diameter values, not of anything in the model.
> 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 对 machine 和 diameter 的每种组合的预测.现在查看 machine
Those summary values are the predictions from mod at each combination of machine and diameter. Now look at EMMs for 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
... 我们得到完全相同的三个预测.但是如果我们看一下diameter:
... we get exactly the same three predictions. But if we look at 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 等于参考网格中三个预测值的平均值.请注意，它在注释中说结果是 machine 的平均值，因此值得一读.
... we get the EMM is equal to the average of the three predicted values in the reference grid. And note that it says in the annotations that results were averaged over machine, so it is worth reading that.
要获得模型结果的图形表示，我们可以这样做
To get a graphical representation of the model results, we can do
> emmip(mod, machine ~ diameter, cov.reduce = range)
添加参数 cov.reduce = range 使参考网格使用最小和最大直径，而不是其平均值.没有它，我们会得到三个点而不是三条线.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:diameter 效果被添加到machine 效果.因此，每条线的共同斜率为 0.023557(参见 summary(mod) 的输出.
The argument cov.reduce = range is added to cause the reference grid to use the min and max diameter, rather than its average. Without that, we'd have gotten three dots instead of three lines. This plot still shows the model predictions, just over a more detailed grid of values. Notice that all three lines have the same slope. That is vbecause the model was specified that way: the diameter effect is added to the machine effect. Each line thus has the common slope of 0.023557 (see the output from summary(mod).
diameter 不需要事后测试，因为它的one效果已经在summary(mod).
There is no post hoc test needed for diameter, since its one effect is already tested in 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 相同规模的 EMM，只需添加 type = "response":
One last thing. The model used log(strength) as the response. If we want the EMMs on the same scale as strength, just add 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.
Again, the annotations below the results help explain the output.
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