Question

编辑：我目前正在撰写关于某种杀虫剂对大黄蜂殖民地的影响的硕士论文。例如，我检查与对照相比，暴露于杀虫剂的菌落中是否有受损/患病的蜂出现更为普遍。

研究设计是分层次的。根据景观特征配对16个田地。在每对中，一个区域被随机分配以用杀虫剂处理，而另一个区域是对照区域。在每个领域有2个盒子，每个盒子里有2个大黄蜂蜂箱。从每个蜂房我每个性别最多十个蛹。

这就是我的数据：

    structure(list(pair = 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, 2L, 2L, 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, 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, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 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, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L
), .Label = c("P01", "P02", "P03", "P04", "P05", "P10", "P11", 
"P12"), class = "factor"), field = structure(c(6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 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, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 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, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 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, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("VR02", "VR03", 
"VR04", "VR05", "VR06", "VR07", "VR09", "VR12", "VR13", "VR14", 
"VR16", "VR17", "VR18", "VR20", "VR21", "VR23"), class = "factor"), 
    treatment = 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, 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, 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, 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, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 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, 
    1L, 1L, 1L, 1L, 1L, 1L, 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, 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), .Label = c("Clothianidin", "Control"), class = "factor"), 
    box.nested = c(11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 
    12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 23, 23, 23, 
    23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 
    24, 24, 24, 24, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 
    3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 19, 19, 19, 19, 19, 19, 
    19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 
    20, 20, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    26, 26, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 
    31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 15, 15, 15, 15, 15, 16, 16, 16, 18, 18, 18, 
    18, 18, 18, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
    6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 21, 21, 21, 21, 21, 21, 21, 
    21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 
    22, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 
    8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 10, 10, 10, 10, 10, 9, 
    9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 30, 30, 30, 30, 29, 29, 29, 
    29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 
    30, 30, 30, 30, 30, 30, 30, 30, 30), hive.nested = c(21L, 
    21L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 
    23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 45L, 45L, 
    45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 46L, 46L, 48L, 
    48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 3L, 3L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 
    38L, 38L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 
    40L, 40L, 40L, 40L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 
    49L, 49L, 49L, 50L, 50L, 51L, 52L, 25L, 25L, 25L, 26L, 26L, 
    26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 61L, 61L, 61L, 61L, 
    61L, 62L, 62L, 62L, 62L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 
    64L, 64L, 64L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 32L, 36L, 
    36L, 36L, 36L, 36L, 36L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 
    34L, 35L, 35L, 35L, 36L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 41L, 41L, 
    41L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 43L, 43L, 
    43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 13L, 14L, 14L, 
    14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 16L, 16L, 16L, 16L, 16L, 16L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 19L, 20L, 20L, 20L, 20L, 17L, 17L, 17L, 17L, 
    17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 
    19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 53L, 53L, 53L, 53L, 
    54L, 54L, 54L, 54L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 56L, 
    56L, 56L, 60L, 60L, 60L, 60L, 57L, 57L, 57L, 57L, 57L, 57L, 
    57L, 58L, 58L, 58L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 59L, 
    59L, 59L, 59L, 60L, 60L, 60L, 60L, 60L, 60L), stage = structure(c(2L, 
    1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 
    1L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 
    3L, 2L, 3L, 1L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 
    2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 
    3L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
    2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 3L, 
    2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 
    1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 
    2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 
    2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 
    3L, 1L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 
    3L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 
    3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 
    2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 
    3L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 3L), .Label = c("1", 
    "2", "3"), class = "factor"), condition = structure(c(2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 
    2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
    2L, 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, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 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, 2L, 2L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L), .Label = c("d", 
    "h"), class = "factor"), sex = 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, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 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, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
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    1L, 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, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
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    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("f", "m", "q"
    ), class = "factor"), diseased = c(0, 1, 1, 1, 1, 1, 1, 1, 
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    0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
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    0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
    1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0)), .Names = c("pair", 
"field", "treatment", "box.nested", "hive.nested", "stage", "condition", 
"sex", "diseased"), class = "data.frame", row.names = c(5L, 7L, 
8L, 9L, 10L, 14L, 15L, 16L, 21L, 23L, 24L, 26L, 28L, 29L, 30L, 
31L, 32L, 33L, 34L, 37L, 38L, 39L, 40L, 42L, 45L, 47L, 48L, 49L, 
50L, 51L, 52L, 53L, 54L, 55L, 58L, 60L, 66L, 67L, 68L, 72L, 73L, 
74L, 77L, 83L, 85L, 87L, 90L, 92L, 95L, 97L, 100L, 104L, 108L, 
115L, 117L, 123L, 125L, 133L, 134L, 137L, 144L, 155L, 156L, 157L, 
158L, 159L, 160L, 161L, 162L, 163L, 164L, 166L, 169L, 170L, 172L, 
175L, 178L, 179L, 180L, 184L, 185L, 189L, 190L, 191L, 192L, 193L, 
194L, 195L, 196L, 197L, 199L, 201L, 202L, 203L, 205L, 206L, 207L, 
211L, 212L, 213L, 215L, 217L, 221L, 222L, 224L, 226L, 230L, 244L, 
247L, 255L, 258L, 262L, 271L, 272L, 274L, 280L, 281L, 284L, 285L, 
288L, 289L, 295L, 296L, 297L, 299L, 300L, 305L, 308L, 309L, 312L, 
314L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 
356L, 359L, 362L, 364L, 366L, 375L, 378L, 381L, 388L, 389L, 390L, 
391L, 392L, 393L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 412L, 
417L, 418L, 420L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 
432L, 433L, 435L, 436L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 
446L, 447L, 450L, 453L, 454L, 455L, 456L, 458L, 459L, 461L, 462L, 
465L, 466L, 468L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 
483L, 484L, 485L, 486L, 487L, 490L, 491L, 494L, 495L, 496L, 500L, 
501L, 508L, 518L, 519L, 521L, 522L, 524L, 525L, 526L, 527L, 528L, 
529L, 530L, 531L, 532L, 533L, 534L, 535L, 538L, 540L, 542L, 543L, 
544L, 548L, 549L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 559L, 
560L, 563L, 568L, 569L, 571L, 572L, 576L, 577L, 578L, 579L, 580L, 
581L, 582L, 583L, 584L, 585L, 587L, 588L, 590L, 594L, 595L, 596L, 
600L, 603L, 604L, 605L, 606L, 607L, 608L, 609L, 616L, 618L, 620L, 
622L, 626L, 628L, 631L, 632L, 635L, 636L, 638L, 639L, 641L, 646L, 
647L, 651L, 652L, 653L, 654L, 655L, 656L, 658L, 659L, 660L, 661L, 
663L, 666L, 667L, 668L, 669L, 670L, 673L, 675L, 676L, 678L, 679L, 
680L, 681L, 682L, 684L, 685L, 686L, 687L, 688L, 689L, 690L))

我从lme4包中运行了二项式glmer模型，以测试大黄蜂群中是否存在疾病/损伤迹象受到杀虫剂的影响。

damage.prev <- glmer(diseased ~ treatment + sex + stage
                 + (1|pair/field/box.nested/hive.nested)
                 ,data=df.cocoons.white, 
                 family=binomial)

我一直试图获得估计和置信区间。感谢@Benjamin 我更接近解决方案，但估计似乎太高了。

这就是我试图获取CI和估计数据的方式：

fixed <- fixef(damage.prev)
wald <-confint(damage.prev,method="Wald") 

estCloth.damage.ratio <- exp(fixed[1])
estCont.damage.ratio <- exp(fixed[1]  +  fixed[2])
lwrCloth.damage.ratio <- exp(wald[1,1]) 
lwrCont.damage.ratio <- exp(wald[1,1] + wald[2,1]) 
uprCloth.damage.ratio <- exp(wald[1,2]) 
uprCont.damage.ratio <- exp(wald[1,2] + wald[2,2]) 

estCloth.damage <- estCloth.damage.ratio/ (1+estCloth.damage.ratio)
estCont.damage <- estCont.damage.ratio / (1+ estCont.damage.ratio)
lwrCloth.damage <- lwrCloth.damage.ratio/ (1+ lwrCloth.damage.ratio)
lwrCont.damage <- lwrCont.damage.ratio /(1+ lwrCont.damage.ratio)
uprCloth.damage <- uprCloth.damage.ratio /(1+uprCloth.damage.ratio)
uprCont.damage <- uprCont.damage.ratio /(1+uprCont.damage.ratio )

treat.damage <- data.frame(Treatment,Estimate,lwr,upr)

仍然让我感到困惑的是高估计超过94％，但

sum(df.cocoons.white$diseased)/length(df.cocoons.white$diseased)

给我不到70％。看起来不太现实。知道什么可能是错的吗？

Answer 1

您的模型正在使用logit转换。

我看待广义线性模型的方式是它们与简单的线性回归没有任何不同。在简单线性回归中，您的响应变量是（理论上）连续的整个实数行（-Inf，Inf）。

在逻辑回归中，您的响应是一个比例，它在区间[0,1]上是连续的。赔率计算（p /（1-p）），其在[0，inf）的区间内是连续的。日志赔率log(p / (1-p))在整个时间间隔内是连续的（-Inf，Inf）。

这个完整的转换（log(p / (1-p))）被称为logit转换，在逻辑回归中非常标准。

glmer模型的结果是逻辑回归的随机效应版本，它使用相同的变换，因此估计的系数在（-Inf，Inf）的范围内。如果你想要比值比，你可以对系数进行取幂，这将给你以（0，Inf）的标度测量的几率，其中1.0是空值。

[r]：解释glmer的结果，重新转换估计值

1 个答案: