R中的TukeyHSD错误

Question

我正在进行混合设计方差分析，并希望为其后Hoc测试运行TukeyHSD。
总是收到错误 “（TukeyHSD”），在UseMethod错误“： 应用于类的对象关于 'TukeyHSD' 不适用方法 ”C（ 'aovlist'， 'listof'）“

I”已经关于一些˚F& Q网站这个错误看出几个问题，但我仍然无法找到一个解决方案

我的数据集是这样的：

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，方差分析的结果是：

ANOVA = AOV（响应〜（组*时间）+误差（受试者/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）
错误UseMethod（ “TukeyHSD”）： 关于 'TukeyHSD' 不适用方法应用到 “（ 'listof' 'aovlist'）C”

的类中的对象

我的命令或数据集有什么问题吗？ 我知道这是一个非常主要的问题......但如果有人可以帮助我，它将不胜感激！谢谢。

Answer 1

你正在处理的是重复测量方差分析，你需要为此做正确的事后测试。见下面的链接进行额外的信息：

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

我想你最好建立与此规定误差结构的线性混合效应模型，如上述建议链接。这是接近你的，事后测试从multcomp -package为模型的人工例如数据集构建的使用NLME -package：

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")) 

此输出可能的组合相当长的名单，但我们注意task1，特别是task1.time3，与预期的完全不同：

  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)