如何解决R中具有双向方差分析的分类协变量
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,67L,247L,223L,74L,32L,110L,98L,303L,256L,71L,30L,236L,266L,307L,224L,100L,73L,288L,230L,182L,159L,190L,123L,169L,103L,40L,248L,60L,260L,168L,267L,144L,89L,231L,204L,130L,278L,227L,205L,268L,88L,221L,208L,306L,242L,145L,21L,165L,217L,206L,121L,181L,95L,279L,265L,4L,122L,234L,34L,261L,86L,2L,296L,283L,251L,126L,176L,220L,77L,225L,48L,107L,118L,310L,297L,258L,54L,16L,119L,9L,66L,64L,55L,131L,290L,166L,170L,125L,137L,81L,61L,135L,117L,65L,200L,150L,146L,162L,111L,271L,33L,199L,49L,101L,80L,5L,281L,255L,210L,52L,53L,141L,213L,112L,235L,233L,115L,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,1L),.Label = c("Female","Male"
),Age = structure(c(2L,2L),.Label = c("Young","Old"),ROI = structure(c(4L,3L,2L
),.Label = c("A","B","C","D"),value = c(0.326713741,0.349206239,0.365954667,0.313958377,0.480487555,0.431199849,0.446729183,0.337009728,0.331222087,0.386937141,0.372758657,0.305083066,0.504718482,0.414191663,0.40949735,0.271525055,0.30009532,0.50117749,0.387669057,0.330797315,0.390679717,0.452181876,0.423188657,0.396808296,0.388510793,0.298505336,0.412985921,0.327000797,0.304242313,0.277513236,0.394773901,0.4322685,0.440891623,0.439061254,0.453015536,0.385896087,0.452299237,0.296923041,0.443324417,0.420699686,0.282610774,0.303566545,0.535346806,0.393591255,0.32561186,0.309230596,0.417596817,0.281766504,0.445347071,0.353419632,0.354420125,0.429613769,0.385733992,0.155136898,0.485385537,0.439544022,0.436584443,0.458706915,0.600399196,0.440390527,0.362952292,0.37253055,0.37306264,0.371298164,0.469741255,0.573943496,0.283266962,0.391182601,0.663566113,0.517713368,0.327498972,0.353969425,0.443648636,0.449972481,0.434426159,0.305042148,0.422493547,0.194572225,0.331083208,0.418288261,0.447215647,0.429001331,0.339149892,0.336879104,0.471237898,0.408330619,0.393405557,0.486086488,0.427713692,0.379242182,0.40456596,0.326695889,0.393235713,0.452374548,0.332855165,0.323469192,0.396484613,0.372199923,0.257353246,0.405249774,0.326494843,0.420468688,0.335335255,0.267627925,0.379383296,0.338241786,0.416064918,0.381003618,0.284006208,0.442705005,0.494199812,0.464447916,0.370418996,0.293953657,0.34482345,0.47208631,0.378798842,0.407261223,0.34767586,0.424341202,0.434532404,0.342623293,0.628901243,0.381492049,0.540111601,0.392371207,0.459349483,0.373172134,0.270272404,0.413454324,0.375994682,0.470298111,0.340463549,0.31613645,0.470312864,0.410651028,0.276164204,0.341546267,0.402167588,0.465735435,0.434102625,0.328114063,0.394582212,0.331681252,0.387562275,0.3989245,0.44939962,0.29586333,0.398924828,0.559520543,0.392099082,0.589552164,0.397368163,0.375135392,0.348508835,0.447002649,0.407775551,0.404435992,0.666776299,0.265039146,0.25311482,0.354386091,0.44051528,0.416727781,0.460624784,0.455415428,0.445090771,0.502343714,0.393426061,0.463244319,0.345586747,0.291874498,0.564393103,0.400276631,0.41512531,0.308440536,0.373545259,0.272377819,0.434890926,0.358394623,0.414819628,0.761894882,0.409700364,0.403811544,0.469092041,0.397044837,0.312479883,0.294876397,0.314414322,0.428720832,0.329074681,0.311423391,0.444689006,0.254723012,0.248710752,0.270434052,0.416304022,0.38875562,0.396840513,0.296386898,0.454476953,0.474986047,0.427072734,0.270839244,0.426266223,0.586857438,0.348018169,0.386638522,0.349321723,0.418692261,0.295630395,0.463439822,0.286190838,0.336389571,0.422766507,0.231764346,0.358636618,0.562871873,0.381515294,0.28637746)),row.names = c(961L,1171L,608L,805L,889L,818L,334L,596L,848L,468L,987L,318L,1110L,969L,1064L,1221L,1239L,1084L,448L,351L,586L,572L,1026L,632L,914L,658L,1174L,909L,1126L,368L,354L,808L,1082L,453L,612L,337L,644L,1177L,1153L,694L,962L,1040L,923L,1186L,691L,650L,886L,1003L,598L,1112L,779L,500L,433L,861L,1099L,350L,1223L,370L,788L,454L,399L,1069L,1060L,1208L,847L,578L,518L,616L,1075L,951L,527L,1089L,380L,431L,801L,1025L,575L,624L,1052L,1107L,854L,344L,1191L,916L,659L,903L,436L,1118L,796L,1150L,1155L,693L,358L,1210L,1048L,968L,620L,1227L,1019L,515L,674L,405L,739L,660L,629L,365L,1061L,1220L,1100L,759L,745L,1131L,757L,938L,391L,371L,995L,1130L,1076L,364L,1192L,772L,844L,782L,421L,561L,440L,1201L,963L,706L,652L,1129L,669L,1031L,581L,390L,704L,603L,625L,384L,899L,591L,830L,672L,558L,983L,451L,1068L,1042L,545L,543L,1045L,464L,941L,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 是每个主题内的一个感兴趣区域，因此所有主题都有全部 4 个 ROI。
我想计算一个 2-way ANCOVA 4(ROIs [a/b/c/d] - inside) x 2 (Age [young/old] - between) + Gender [covariate] 来确定交互作用age 上的 ROI 和 value，控制 Gender。
为此，我计算了：
#2-way ANOVA
res.aov2 <- df %>%
anova_test(value ~ Gender + Age*ROI,within = ROI,wid= DTI_ID)
get_anova_table(res.aov2)
工作正常并输出：
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
然后我想运行多重比较以生成 p 值，我可以在箱线图上绘制该值，以便分析可视化。
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
我不知道为什么，因为当我删除协变量时，成对比较工作正常。它与用作协变量的分类变量有关吗？我被卡住了，想确保我在图表中报告了适当的 p 值。
解决方法
同时将 Gender 添加到 group_by，使代码能够正常运行。
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