我正在研究混合设计方差分析，并希望运行TukeyHSD进行后Hoc测试。
我一直收到错误，“UseMethod错误（”TukeyHSD“）：
没有适用于'TukeyHSD'的方法适用于类“c（'aovlist'，'listof'）”的对象。
我在一些F＆amp; Q网站上看到过有关此错误的几个问题，但我仍然无法找到解决方案。
我的数据集如下所示：
subject response time group
1 S1 0.99676 time1 task1
2 S2 1.00220 time1 task1
3 S3 1.00420 time1 task1
4 S4 0.99467 time1 task1
5 S5 0.97906 time1 task1
6 S6 0.99162 time1 task1
7 S7 0.99771 time1 task1
8 S8 1.01780 time1 task2
9 S9 0.98682 time1 task2
10 S10 0.99124 time1 task2
11 S11 1.01670 time1 task2
12 S12 0.99769 time1 task2
13 S13 1.02090 time1 task2
14 S14 1.01740 time1 task2
15 S15 0.98851 time1 task3
16 S16 1.00690 time1 task3
17 S17 0.99717 time1 task3
18 S18 0.98945 time1 task3
19 S19 1.00270 time1 task3
20 S20 1.02690 time1 task3
21 S21 1.00050 time1 task3
22 S1 0.96908 time2 task1
23 S2 0.94024 time2 task1
......
然后，anova结果是：
anova = aov（响应〜（组*时间）+错误（主题/时间），数据） 摘要（ANOVA）
Error: subject
Df Sum Sq Mean Sq F value Pr(>F)
group 2 0.00381 0.001907 0.701 0.509
Residuals 18 0.04896 0.002720
Error: subject:time
Df Sum Sq Mean Sq F value Pr(>F)
time 3 0.08205 0.027351 42.80 2.68e-14 ***
group:time 6 0.00272 0.000454 0.71 0.643
Residuals 54 0.03451 0.000639
如果我运行TukeyHSD：
TukeyHSD（ANOVA）
UseMethod（“TukeyHSD”）中的错误： 没有适用于“TukeyHSD”的方法适用于“c（'aovlist'，'listof'）类的对象”
我的命令或数据集有什么问题吗？ 我知道这是一个非常重要的问题......但如果有人能帮助我，我们将不胜感激！感谢。
答案 0 :(得分：9)
您正在处理的是重复测量ANOVA，您需要对此进行正确的事后测试。请参阅以下链接以获取更多信息：
Post hoc tests with ezANOVA output
Post hoc test after ANOVA with repeated measures using R
[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA
我认为您最好使用此指定的错误结构构建线性混合效果模型，如上面的链接所示。这是一个靠近你的人工示例数据集，以及使用 nlme -package构建的模型的 multcomp -package的事后测试：
set.seed(1)
dat <- cbind(expand.grid(time = paste("time", 1:3, sep=""), group = paste("task", 1:3, sep=""), subject = paste("S", 1:20, sep="")), response = rnorm(3*3*20))
# Add task1-specific effect (== task1.timeANY)
dat$response <- dat$response + as.numeric(dat$group=="task1")
# Extra effect in the last timepoint of task1 (== task1.time3)
dat$response <- dat$response + as.numeric(dat$group=="task1")*as.numeric(dat$time=="time3")
# Randomness specific for each subject
dat$response <- dat$response + rep(rnorm(20), each=3)
dat$grtim <- interaction(dat$group, dat$time)
# Interaction term specified above
#> head(dat)
# time group subject response grtim
#1 time1 task1 S1 -0.85777723 task1.time1
#2 time2 task1 S1 -0.04768010 task1.time2
#3 time3 task1 S1 -0.06695203 task1.time3
#4 time1 task2 S1 2.57917637 task2.time1
#5 time2 task2 S1 1.31340334 task2.time2
#6 time3 task2 S1 0.16342719 task2.time3
# Reason why TukeyHSD-function fails:
#anova = aov(response~(group*time)+Error(subject/time),dat)
#summary(anova)
#TukeyHSD(anova)
#Error in UseMethod("TukeyHSD") :
# no applicable method for 'TukeyHSD' applied to an object of class "c('aovlist', 'listof')"
#> class(anova)
#[1] "aovlist" "listof"
require(nlme)
# Below call does not work for glht, thus we created the interaction term in the data frame
#model <- lme(response ~ group*time, random = ~ 1 | subject / time, dat)
model <- lme(response ~ grtim, random = ~ 1 | subject / time, dat)
require(multcomp)
summary(glht(model, linfct=mcp(grtim="Tukey")), test = adjusted(type = "bonferroni"))
这会输出相当长的可能组合列表，但我们注意到task1，尤其是task1.time3，与其他组合完全不同：
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: lme.formula(fixed = response ~ grtim, data = dat, random = ~1 |
subject/time)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
task2.time1 - task1.time1 == 0 -0.66574 0.40907 -1.627 1.000000
task3.time1 - task1.time1 == 0 -0.21758 0.40907 -0.532 1.000000
task1.time2 - task1.time1 == 0 0.46382 0.40907 1.134 1.000000
task2.time2 - task1.time1 == 0 -0.63987 0.40907 -1.564 1.000000
task3.time2 - task1.time1 == 0 -0.86698 0.40907 -2.119 1.000000
task1.time3 - task1.time1 == 0 1.17238 0.40907 2.866 0.149667
task2.time3 - task1.time1 == 0 -1.15241 0.40907 -2.817 0.174433
task3.time3 - task1.time1 == 0 -0.70811 0.40907 -1.731 1.000000
task3.time1 - task2.time1 == 0 0.44816 0.40907 1.096 1.000000
task1.time2 - task2.time1 == 0 1.12956 0.40907 2.761 0.207272
task2.time2 - task2.time1 == 0 0.02587 0.40907 0.063 1.000000
task3.time2 - task2.time1 == 0 -0.20124 0.40907 -0.492 1.000000
task1.time3 - task2.time1 == 0 1.83812 0.40907 4.493 0.000252 ***
task2.time3 - task2.time1 == 0 -0.48667 0.40907 -1.190 1.000000
task3.time3 - task2.time1 == 0 -0.04237 0.40907 -0.104 1.000000
task1.time2 - task3.time1 == 0 0.68140 0.40907 1.666 1.000000
task2.time2 - task3.time1 == 0 -0.42229 0.40907 -1.032 1.000000
task3.time2 - task3.time1 == 0 -0.64940 0.40907 -1.587 1.000000
task1.time3 - task3.time1 == 0 1.38996 0.40907 3.398 0.024451 *
task2.time3 - task3.time1 == 0 -0.93483 0.40907 -2.285 0.802723
task3.time3 - task3.time1 == 0 -0.49053 0.40907 -1.199 1.000000
task2.time2 - task1.time2 == 0 -1.10369 0.40907 -2.698 0.251098
task3.time2 - task1.time2 == 0 -1.33080 0.40907 -3.253 0.041077 *
task1.time3 - task1.time2 == 0 0.70856 0.40907 1.732 1.000000
task2.time3 - task1.time2 == 0 -1.61623 0.40907 -3.951 0.002802 **
task3.time3 - task1.time2 == 0 -1.17193 0.40907 -2.865 0.150188
task3.time2 - task2.time2 == 0 -0.22711 0.40907 -0.555 1.000000
task1.time3 - task2.time2 == 0 1.81225 0.40907 4.430 0.000339 ***
task2.time3 - task2.time2 == 0 -0.51254 0.40907 -1.253 1.000000
task3.time3 - task2.time2 == 0 -0.06824 0.40907 -0.167 1.000000
task1.time3 - task3.time2 == 0 2.03936 0.40907 4.985 2.23e-05 ***
task2.time3 - task3.time2 == 0 -0.28543 0.40907 -0.698 1.000000
task3.time3 - task3.time2 == 0 0.15887 0.40907 0.388 1.000000
task2.time3 - task1.time3 == 0 -2.32479 0.40907 -5.683 4.76e-07 ***
task3.time3 - task1.time3 == 0 -1.88049 0.40907 -4.597 0.000154 ***
task3.time3 - task2.time3 == 0 0.44430 0.40907 1.086 1.000000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- bonferroni method)