这是我的数据集的随机样本：
structure(list(DTI_ID = structure(c(31L, 241L, 84L, 298L, 185L,
269L, 198L, 24L, 286L, 177L, 228L, 158L, 57L, 293L, 218L, 8L,
180L, 39L, 211L, 134L, 291L, 309L, 99L, 70L, 154L, 138L, 250L,
41L, 276L, 262L, 96L, 139L, 232L, 12L, 294L, 38L, 244L, 289L,
280L, 196L, 58L, 44L, 188L, 152L, 143L, 302L, 201L, 27L, 24L,
67L, 247L, 223L, 74L, 32L, 110L, 98L, 303L, 256L, 71L, 30L, 236L,
266L, 307L, 224L, 100L, 73L, 288L, 230L, 182L, 159L, 190L, 123L,
241L, 169L, 103L, 40L, 248L, 293L, 60L, 260L, 168L, 267L, 144L,
89L, 139L, 231L, 204L, 130L, 278L, 227L, 205L, 268L, 88L, 221L,
208L, 306L, 242L, 145L, 21L, 165L, 217L, 159L, 206L, 70L, 121L,
181L, 95L, 279L, 265L, 4L, 122L, 177L, 234L, 34L, 261L, 86L,
2L, 296L, 39L, 283L, 251L, 126L, 188L, 176L, 220L, 77L, 225L,
73L, 48L, 107L, 280L, 118L, 38L, 310L, 297L, 258L, 89L, 205L,
4L, 54L, 16L, 95L, 119L, 40L, 9L, 66L, 64L, 55L, 131L, 290L,
166L, 170L, 182L, 139L, 125L, 201L, 302L, 137L, 8L, 81L, 61L,
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199L, 49L, 180L, 101L, 271L, 80L, 84L, 293L, 5L, 170L, 74L, 279L,
281L, 255L, 210L, 52L, 248L, 53L, 121L, 190L, 141L, 213L, 138L,
112L, 234L, 235L, 40L, 233L, 115L, 154L, 11L, 76L, 29L, 19L,
249L, 1L, 207L), .Label = c("5356", "5357", "5358", "5359", "5360",
"5363", "5373", "5381", "5383", "5386", "5395", "5397", "5400",
"5401", "5444", "5445", "5446", "5448", "5450", "5451", "5454",
"5472", "5473", "5475", "5476", "5477", "5478", "5480", "5481",
"5483", "5487", "5494", "5495", "5504", "5505", "5506", "5507",
"5508", "5509", "5513", "5514", "5515", "5516", "5517", "5518",
"5519", "5521", "5523", "5524", "5526", "5527", "5528", "5544",
"5545", "5546", "5547", "5551", "5552", "5553", "5554", "5555",
"5558", "5559", "5560", "5562", "5564", "5566", "5573", "5574",
"5575", "5576", "5577", "5578", "5579", "5584", "5585", "5587",
"5588", "5589", "5591", "5594", "5595", "5604", "5611", "5612",
"5613", "5615", "5616", "5619", "5620", "5621", "5622", "5626",
"5627", "5628", "5631", "5632", "5634", "5635", "5643", "5652",
"5653", "5654", "5655", "5656", "5657", "5659", "5660", "5661",
"5664", "5665", "5666", "5669", "5671", "5672", "5673", "5678",
"5680", "5688", "5689", "5690", "5691", "5692", "5698", "5699",
"5700", "5702", "5703", "5704", "5706", "5708", "5709", "5710",
"5730", "5731", "5732", "5733", "5734", "5735", "5739", "5740",
"5741", "5742", "5743", "5744", "5745", "5746", "5747", "5748",
"5749", "5750", "5753", "5754", "5755", "5766", "5767", "5776",
"5777", "5778", "5779", "5780", "5781", "5787", "5788", "5789",
"5790", "5791", "5792", "5793", "5797", "5798", "5799", "5800",
"5801", "5810", "5811", "5812", "5813", "5814", "5819", "5820",
"5821", "5822", "5823", "5824", "5825", "5827", "5828", "5829",
"5830", "5857", "5859", "5874", "5875", "5876", "5877", "5878",
"5879", "5883", "5884", "5886", "5887", "5888", "5889", "5890",
"5892", "5893", "5896", "5899", "5900", "5909", "5910", "5918",
"5919", "5920", "5921", "5922", "5923", "5927", "5929", "5931",
"5932", "5933", "5934", "5936", "5937", "5941", "5943", "5944",
"5949", "5950", "5951", "5952", "5956", "5957", "5958", "5959",
"5971", "5972", "5973", "5976", "5979", "5980", "5981", "6001",
"6002", "6003", "6004", "6005", "6009", "6027", "6028", "6033",
"6042", "6054", "6063", "6067", "6073", "6076", "6077", "6078",
"6079", "6080", "6081", "6082", "6083", "6098", "6102", "6103",
"6104", "6105", "6106", "6107", "6111", "6119", "6133", "6146",
"6147", "6157", "6158", "6160", "6161", "6162", "6163", "6164",
"6165", "6166", "6167", "6168", "6169", "6170", "6171", "6172",
"6173", "6174", "6175", "6190", "6193", "6195", "6196", "6197",
"6208", "6228", "6229", "6232", "6255", "6268", "6269", "6270",
"6275"), class = "factor"), Gender = structure(c(2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L), .Label = c("Female", "Male"
), class = "factor"), Age = structure(c(2L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L), .Label = c("Young", "Old"), class = "factor"),
ROI = structure(c(4L, 4L, 1L, 2L, 3L, 3L, 3L, 2L, 2L, 1L,
3L, 2L, 4L, 1L, 1L, 2L, 4L, 4L, 1L, 4L, 4L, 4L, 1L, 1L, 4L,
2L, 1L, 2L, 2L, 2L, 4L, 1L, 1L, 3L, 3L, 3L, 4L, 3L, 1L, 4L,
2L, 2L, 3L, 4L, 2L, 2L, 1L, 2L, 3L, 1L, 4L, 4L, 3L, 4L, 4L,
1L, 3L, 4L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 4L, 2L, 1L, 4L, 3L,
2L, 2L, 3L, 4L, 1L, 2L, 1L, 4L, 2L, 1L, 3L, 1L, 2L, 2L, 4L,
1L, 1L, 4L, 4L, 3L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 4L, 4L, 1L,
2L, 4L, 1L, 2L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 4L, 3L, 2L, 4L,
1L, 1L, 3L, 3L, 3L, 1L, 2L, 4L, 3L, 4L, 1L, 4L, 3L, 2L, 1L,
4L, 4L, 4L, 2L, 4L, 1L, 4L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 3L,
1L, 1L, 2L, 4L, 4L, 1L, 4L, 1L, 3L, 3L, 4L, 1L, 3L, 4L, 2L,
2L, 1L, 1L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 2L, 4L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 4L, 4L, 3L, 3L, 4L, 3L, 1L, 4L, 2L, 2L, 3L,
2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 4L, 1L, 1L, 2L, 1L,
4L, 4L, 1L, 2L, 1L, 2L, 4L, 2L, 4L, 1L, 4L, 2L, 4L, 3L, 2L
), .Label = c("A", "B","C", "D"), class = "factor"),
value = c(0.326713741, 0.349206239, 0.365954667,
0.313958377, 0.480487555, 0.431199849, 0.446729183, 0.337009728,
0.331222087, 0.386937141, 0.372758657, 0.305083066, 0.504718482,
0.414191663, 0.40949735, 0.271525055, 0.30009532, 0.50117749,
0.387669057, 0.330797315, 0.390679717, 0.452181876, 0.423188657,
0.396808296, 0.388510793, 0.298505336, 0.412985921, 0.327000797,
0.304242313, 0.277513236, 0.394773901, 0.4322685, 0.440891623,
0.439061254, 0.453015536, 0.385896087, 0.452299237, 0.296923041,
0.443324417, 0.420699686, 0.282610774, 0.303566545, 0.535346806,
0.393591255, 0.32561186, 0.309230596, 0.417596817, 0.281766504,
0.445347071, 0.353419632, 0.354420125, 0.429613769, 0.385733992,
0.155136898, 0.485385537, 0.439544022, 0.436584443, 0.458706915,
0.600399196, 0.440390527, 0.362952292, 0.37253055, 0.37306264,
0.371298164, 0.469741255, 0.573943496, 0.283266962, 0.391182601,
0.663566113, 0.517713368, 0.327498972, 0.353969425, 0.443648636,
0.449972481, 0.434426159, 0.305042148, 0.422493547, 0.194572225,
0.331083208, 0.418288261, 0.447215647, 0.429001331, 0.339149892,
0.336879104, 0.471237898, 0.408330619, 0.393405557, 0.486086488,
0.427713692, 0.379242182, 0.40456596, 0.326695889, 0.393235713,
0.452374548, 0.332855165, 0.323469192, 0.396484613, 0.372199923,
0.257353246, 0.405249774, 0.326494843, 0.420468688, 0.335335255,
0.267627925, 0.379383296, 0.338241786, 0.416064918, 0.381003618,
0.284006208, 0.442705005, 0.494199812, 0.464447916, 0.370418996,
0.293953657, 0.34482345, 0.47208631, 0.378798842, 0.407261223,
0.34767586, 0.424341202, 0.434532404, 0.342623293, 0.628901243,
0.381492049, 0.540111601, 0.392371207, 0.459349483, 0.373172134,
0.270272404, 0.413454324, 0.375994682, 0.470298111, 0.340463549,
0.31613645, 0.470312864, 0.410651028, 0.276164204, 0.341546267,
0.402167588, 0.465735435, 0.434102625, 0.328114063, 0.394582212,
0.331681252, 0.387562275, 0.3989245, 0.44939962, 0.29586333,
0.398924828, 0.559520543, 0.392099082, 0.589552164, 0.397368163,
0.375135392, 0.348508835, 0.447002649, 0.407775551, 0.404435992,
0.666776299, 0.265039146, 0.25311482, 0.354386091, 0.44051528,
0.416727781, 0.460624784, 0.455415428, 0.445090771, 0.502343714,
0.393426061, 0.463244319, 0.345586747, 0.291874498, 0.564393103,
0.400276631, 0.41512531, 0.308440536, 0.373545259, 0.272377819,
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0.403811544, 0.469092041, 0.397044837, 0.312479883, 0.294876397,
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0.254723012, 0.248710752, 0.270434052, 0.416304022, 0.38875562,
0.396840513, 0.296386898, 0.454476953, 0.474986047, 0.427072734,
0.270839244, 0.426266223, 0.586857438, 0.348018169, 0.386638522,
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0.336389571, 0.422766507, 0.231764346, 0.358636618, 0.562871873,
0.381515294, 0.28637746)), row.names = c(961L, 1171L, 84L,
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545L, 40L, 543L, 1045L, 464L, 941L, 76L, 959L, 329L, 1179L, 621L,
517L), class = "data.frame")
看起来像：
# A tibble: 10 x 5
DTI_ID Gender Age ROI value
<fct> <fct> <fct> <fct> <dbl>
1 5927 Male Old A 0.395
2 5634 Male Old C 0.433
3 5547 Female Old B 0.257
4 5979 Male Old C 0.404
5 5660 Male Old A 0.398
6 5876 Female Old D 0.426
7 5518 Male Old A 0.404
8 6001 Female Old D 0.392
9 6042 Male Old A 0.388
10 5821 Male Old A 0.344
ROI是每个受试者中感兴趣的区域，因此所有受试者都具有全部4个ROI。
我想计算一个双向ANCOVA 4(ROI[a/b/c/d]-内部(x2(年龄[年轻/老年]-之间(+性别[协变]，以确定age和ROI对value的相互作用效应，控制Gender。
为了做到这一点，我计算了一下：
#2-way ANOVA
res.aov2 <- df %>%
anova_test(value ~ Gender + Age*ROI, within = ROI, wid= DTI_ID)
get_anova_table(res.aov2)
它工作良好并输出：
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 Gender 1 1227 5.196 2.30e-02 * 0.004000
2 Age 1 1227 0.732 3.92e-01 0.000596
3 ROI 3 1227 228.933 6.13e-118 * 0.359000
4 Age:ROI 3 1227 22.258 4.90e-14 * 0.052000
然后，我想运行多次比较来生成p值，我可以在方框图上绘制这些值，以实现分析的可视化。
我正在使用emmeans_test:
# Pairwise comparisons
pwc2 <- df %>%
group_by(ROI) %>%
emmeans_test(value ~ Age, covariate = Gender,
p.adjust.method = "bonferroni")
但收到错误：
Error in contrast.emmGrid(res.emmeans, by = grouping.vars, method = method, : Nonconforming number of contrast coefficients
我不明白为什么，因为当我去除协变量时，成对比较效果很好。它是否与一个分类变量被用作协变量有关？我被卡住了，想确保我在图表中报告了适当的p值。
将Gender也添加到group_by中，使代码能够正常运行。