最初我们在考虑单因素方差分析，但似乎我需要做一个双向因为我有两个自变量。对于每个宏观上的每个鳄鱼洞，会话（采取迷你陷阱采样的时间）和TRAP（每个陷阱平均每个孔4个）CPUE将是因变量，然后是ID列。
SESSION TRAP CPUE ID
One M1E1 3 1
One M1E2 0 2
One M1E3 0 3
One M1E4 2 4
One M1W1 0 5
One M1W2 0 6
One M1W3 0 7
One M1W4 0 8
One M2E1 0 9
One M2E2 0 10
One M2E3 0 11
One M2E4 0 12
One M2W1 0 13
One M2W2 1 14
One M2W3 1 15
One M2W4 0 16
One M3E1 5 17
One M3E2 2 18
One M3E3 0 19
One M3E4 3 20
One M3W1 0 21
One M3W2 0 22
One M3W3 0 23
One M3W4 2 24
One M4E1 0 25
One M4E2 1 26
One M4E3 0 27
One M4E4 0 28
One M4W1 0 29
One M4W2 0 30
One M4W3 0 31
One M4W4 8 32
Two M4E1 23 33
Two M4E2 5 34
Two M4E3 0 35
Two M4E4 10 36
Two M4W1 23 37
Two M4W2 7 38
Two M4W3 1 39
Two M4W4 7 40
Two M3E1 6 41
Two M3E2 3 42
Two M3E3 5 43
Two M3E4 10 44
Two M3W1 8 45
Two M3W2 0 46
Two M3W3 1 47
Two M3W4 5 48
Two M2E1 12 49
Two M2E2 15 50
Two M2E3 3 51
Two M2E4 10 52
Two M2W1 5 53
Two M2W2 11 54
Two M2W3 6 55
Two M2W4 4 56
Two M1E1 13 57
Two M1E2 19 58
Two M1E3 3 59
Two M1E4 30 60
Two M1W1 16 61
Two M1W2 2 62
Two M1W3 4 63
Two M1W4 27 64
Three M4E1 0 65
Three M4E2 26 66
Three M4E3 3 67
Three M4E4 13 68
Three M4W1 9 69
Three M4W2 0 70
Three M4W3 4 71
Three M4W4 2 72
Three M3E1 29 73
Three M3E2 0 74
Three M3E3 0 75
Three M3E4 11 76
Three M3W1 27 77
Three M3W2 5 78
Three M3W3 8 79
Three M3W4 3 80
Three M2E1 5 81
Three M2E2 11 82
Three M2E3 62 83
Three M2E4 31 84
Three M2W1 11 85
Three M2W2 1 86
Three M2W3 0 87
Three M2W4 9 88
Three M1E1 48 89
Three M1E2 78 90
Three M1E3 14 91
Three M1E4 7 92
Three M1W1 3 93
Three M1W2 63 94
Three M1W3 43 95
Three M1W4 31 96
我正在使用此命令：
> output = ezANOVA(data = CSV.Repeated.Measures.ANOVA.Minnow._2cm.R.Data.Sheet, dv= CPUE, wid = ID, within = .(SESSION, TRAP), detailed = TRUE, type = 3)
我收到此错误消息：
ezANOVA_main出错（data = data，dv = dv，wid = wid，within = 在，：一个或多个单元格缺少数据。尝试使用ezDesign（） 检查你的数据。
我不知道exDesign（）试图告诉我的是什么。
答案 0 :(得分：0)
我会尝试使用ezANOVA解决您的问题。当然，有必要了解实验的所有细节，以便对您的问题做出完整而正确的答案。
如果我没有错，你写道，min鱼陷阱是实验的样本单位，并且对这些单位进行重复测量（在不同的实验条件下）。因此，样本单位的ID不是存储在ID列中的ID;需要生成一个新的id变量
这是数据集：
df <- structure(list(SESSION = 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, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 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("One", "Three",
"Two"), class = "factor"), TRAP = structure(c(1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("1",
"2", "3", "4"), class = "factor"), CPUE = c(3L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 5L, 2L, 0L, 3L, 0L,
0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 8L, 23L, 5L, 0L, 10L,
23L, 7L, 1L, 7L, 6L, 3L, 5L, 10L, 8L, 0L, 1L, 5L, 12L, 15L, 3L,
10L, 5L, 11L, 6L, 4L, 13L, 19L, 3L, 30L, 16L, 2L, 4L, 27L, 0L,
26L, 3L, 13L, 9L, 0L, 4L, 2L, 29L, 0L, 0L, 11L, 27L, 5L, 8L,
3L, 5L, 11L, 62L, 31L, 11L, 1L, 0L, 9L, 48L, 78L, 14L, 7L, 3L,
63L, 43L, 31L), ID = structure(1:96, .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92",
"93", "94", "95", "96"), class = "factor"), MACROCOSM = 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, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1",
"2", "3", "4"), class = "factor"), HOLE = structure(c(1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("E",
"W"), class = "factor")), .Names = c("SESSION", "TRAP", "CPUE",
"ID", "MACROCOSM", "HOLE"), row.names = c(NA, -96L), class = "data.frame")
这是代码（希望）能够为您找到解决问题的方法：
df$MACROCOSM <- factor(substr(df$TRAP, 2, 2))
df$HOLE <- factor(substr(df$TRAP, 3, 3))
df$TRAP <- factor(substr(df$TRAP, 4, 4))
library(ez)
ezOut <- ezANOVA(data = df,
dv=CPUE, wid = .(TRAP), within = .(SESSION,HOLE,MACROCOSM),
detailed = TRUE, type = 1)
print(ezOut)
#############
$ANOVA
Effect DFn DFd SSn SSd F p p<.05 ges
1 SESSION 2 6 4372.5625 753.35417 17.4123780 0.003174556 * 0.30542230
2 HOLE 1 3 276.7604 56.11458 14.7961760 0.031011624 * 0.02707856
3 MACROCOSM 3 9 2030.5313 1466.76042 4.1530939 0.041961697 * 0.16957246
4 SESSION:HOLE 2 6 216.2708 60.47917 10.7278677 0.010436491 * 0.02128617
5 SESSION:MACROCOSM 6 18 2327.6875 3995.39583 1.7477774 0.167180534 0.18968127
6 HOLE:MACROCOSM 3 9 198.6146 1070.34375 0.5566845 0.656642963 0.01958241
7 SESSION:HOLE:MACROCOSM 6 18 461.4792 2541.43750 0.5447458 0.767574519 0.04435012