我想使用R来分析一个实验，在该实验中，三组参与者分别被展示了两种刺激类型，分别进行了三遍。因变量是一个连续的量度。这是数据集外观的示例。
SubjectID Group Trial StimType Measure
1 1 group1 trial3_stimA A 0.55908866
2 2 group1 trial3_stimA A 0.98884446
3 3 group2 trial3_stimA A 0.00000000
4 4 group2 trial3_stimA A 0.27067991
5 5 group3 trial3_stimA A 0.37169285
6 6 group3 trial3_stimA A 0.42113984
7 1 group1 trial3_stimB B 0.00000000
8 2 group1 trial3_stimB B 0.49892807
9 3 group2 trial3_stimB B 0.14602589
10 4 group2 trial3_stimB B 0.50946555
11 5 group3 trial3_stimB B 0.25572820
12 6 group3 trial3_stimB B 0.22932966
13 1 group1 trial1_stimA A 0.42207604
14 2 group1 trial1_stimA A 0.85599588
15 3 group2 trial1_stimA A 0.36428381
16 4 group2 trial1_stimA A 0.46679336
17 5 group3 trial1_stimA A 0.69379734
18 6 group3 trial1_stimA A 0.55607716
19 1 group1 trial1_stimB B 0.24261465
20 2 group1 trial1_stimB B 0.35176384
21 3 group2 trial1_stimB B 0.21116215
22 4 group2 trial1_stimB B 0.33112544
23 5 group3 trial1_stimB B 0.00000000
24 6 group3 trial1_stimB B 0.00000000
25 1 group1 trial2_stimA A 0.05506943
26 2 group1 trial2_stimA A 0.22537470
27 3 group2 trial2_stimA A 0.00000000
28 4 group2 trial2_stimA A 0.18511144
29 5 group3 trial2_stimA A 0.15586156
30 6 group3 trial2_stimA A 0.04467100
31 1 group1 trial2_stimB B 0.03890585
32 2 group1 trial2_stimB B 0.29787709
33 3 group2 trial2_stimB B 0.00000000
34 4 group2 trial2_stimB B 0.28971992
35 5 group3 trial2_stimB B 0.12993238
36 6 group3 trial2_stimB B 0.05066011
这是我的数据结构
'data.frame': 36 obs. of 5 variables:
$ SubjectID: Factor w/ 6 levels "1","2","3","4",..: 1 2 3 4 5 6 1 2 3 4 ...
$ Group : Factor w/ 3 levels "group1","group2",..: 1 1 2 2 3 3 1 1 2 2 ...
$ Trial : Factor w/ 6 levels "trial1_stimA",..: 5 5 5 5 5 5 6 6 6 6 ...
$ StimType : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 2 2 2 2 ...
$ Measure : num 0.559 0.989 0 0.271 0.372 ...
我需要对受试者因素和试验之间的组进行混合方差分析，而刺激因素的类型则在受试者因素内。我尝试使用三种不同的R包和不同的语法，但R要么返回错误消息，要么输出缺少交互组x试用x激励类型。
例如，当我使用rstatix包中的anova_test（）时>
#Trying mixed ANOVA with rstatix > > mixed.anova <- anova_test( + data = prepared_data, dv = Measure, wid = SubjectID, + between = Group, within = c(Trial,StimType) + ) Error in check.imatrix(X.design) : Terms in the intra-subject model matrix are not orthogonal. > get_anova_table(all_subjects) Error in is.data.frame(x) : object 'all_subjects' not found当我使用来自afex软件包的aov时
> #using afex > > > mixed.anova2 <- aov_car(Measure ~ Group*Trial*StimType + Error(1|SubjectID/(Trial*StimType)), prepared_data) Error: Empty cells in within-subjects design (i.e., bad data structure). table(data[c("Trial", "StimType")]) # StimType # Trial A B # trial1_stimA 6 0 # trial1_stimB 0 6 # trial2_stimA 6 0 # trial2_stimB 0 6 # trial3_stimA 6 0 # trial3_stimB 0 6 > > aov.bww Call: aov(formula = SCR ~ Group * Trial * CSType + Error(SubjectID) + Group, data = sixPhasesAbs2) Grand Mean: 397.1325 Stratum 1: SubjectID Terms: Group Residuals Sum of Squares 187283464 6399838881 Deg. of Freedom 2 81 Residual standard error: 8888.777 18 out of 20 effects not estimable Estimated effects may be unbalanced Stratum 2: Within Terms: Trial Group:Trial Residuals Sum of Squares 158766098 374583941 12799639740 Deg. of Freedom 5 10 405 Residual standard error: 5621.748 12 out of 27 effects not estimable Estimated effects may be unbalanced最后，我尝试使用lmer
> #using lmer > model = lmer(Measure ~Group*Trial*StimType +(1|SubjectID), data = prepared_data) fixed-effect model matrix is rank deficient so dropping 18 columns / coefficients > > summary(model) Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest'] Formula: Measure ~ Group * Trial * StimType + (1 | SubjectID) Data: prepared_data REML criterion at convergence: -16.8 Scaled residuals: Min 1Q Median 3Q Max -1.1854 -0.5424 0.0000 0.5424 1.1854 Random effects: Groups Name Variance Std.Dev. SubjectID (Intercept) 0.024226 0.15565 Residual 0.006861 0.08283 Number of obs: 36, groups: SubjectID, 6 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 0.639036 0.124672 4.459241 5.126 0.005095 ** Groupgroup2 -0.223497 0.176313 4.459241 -1.268 0.267088 Groupgroup3 -0.014099 0.176313 4.459241 -0.080 0.939728 Trialtrial1_stimB -0.341847 0.082829 15.000000 -4.127 0.000896 *** Trialtrial2_stimA -0.498814 0.082829 15.000000 -6.022 2.34e-05 *** Trialtrial2_stimB -0.470644 0.082829 15.000000 -5.682 4.35e-05 *** Trialtrial3_stimA 0.134931 0.082829 15.000000 1.629 0.124124 Trialtrial3_stimB -0.389572 0.082829 15.000000 -4.703 0.000283 *** Groupgroup2:Trialtrial1_stimB 0.197452 0.117138 15.000000 1.686 0.112550 Groupgroup3:Trialtrial1_stimB -0.283091 0.117138 15.000000 -2.417 0.028865 * Groupgroup2:Trialtrial2_stimA 0.175831 0.117138 15.000000 1.501 0.154095 Groupgroup3:Trialtrial2_stimA -0.025857 0.117138 15.000000 -0.221 0.828271 Groupgroup2:Trialtrial2_stimB 0.199966 0.117138 15.000000 1.707 0.108413 Groupgroup3:Trialtrial2_stimB -0.063997 0.117138 15.000000 -0.546 0.592870 Groupgroup2:Trialtrial3_stimA -0.415129 0.117138 15.000000 -3.544 0.002946 ** Groupgroup3:Trialtrial3_stimA -0.363452 0.117138 15.000000 -3.103 0.007276 ** Groupgroup2:Trialtrial3_stimB 0.301779 0.117138 15.000000 2.576 0.021070 * Groupgroup3:Trialtrial3_stimB 0.007164 0.117138 15.000000 0.061 0.952043 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation matrix not shown by default, as p = 18 > 12. Use print(x, correlation=TRUE) or vcov(x) if you need it fit warnings: fixed-effect model matrix is rank deficient so dropping 18 columns / coefficients > > anova(model) Missing cells for: Trialtrial1_stimB:StimTypeA, Trialtrial2_stimB:StimTypeA, Trialtrial3_stimB:StimTypeA, Trialtrial1_stimA:StimTypeB, Trialtrial2_stimA:StimTypeB, Trialtrial3_stimA:StimTypeB, Groupgroup1:Trialtrial1_stimB:StimTypeA, Groupgroup2:Trialtrial1_stimB:StimTypeA, Groupgroup3:Trialtrial1_stimB:StimTypeA, Groupgroup1:Trialtrial2_stimB:StimTypeA, Groupgroup2:Trialtrial2_stimB:StimTypeA, Groupgroup3:Trialtrial2_stimB:StimTypeA, Groupgroup1:Trialtrial3_stimB:StimTypeA, Groupgroup2:Trialtrial3_stimB:StimTypeA, Groupgroup3:Trialtrial3_stimB:StimTypeA, Groupgroup1:Trialtrial1_stimA:StimTypeB, Groupgroup2:Trialtrial1_stimA:StimTypeB, Groupgroup3:Trialtrial1_stimA:StimTypeB, Groupgroup1:Trialtrial2_stimA:StimTypeB, Groupgroup2:Trialtrial2_stimA:StimTypeB, Groupgroup3:Trialtrial2_stimA:StimTypeB, Groupgroup1:Trialtrial3_stimA:StimTypeB, Groupgroup2:Trialtrial3_stimA:StimTypeB, Groupgroup3:Trialtrial3_stimA:StimTypeB. Interpret type III hypotheses with care. Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Group 0.00723 0.003614 2 3 0.5267 0.6367151 Trial 0.96171 0.192343 5 15 28.0356 4.172e-07 *** Group:Trial 0.44102 0.044102 10 15 6.4283 0.0007411 *** StimType Group:StimType Trial:StimType Group:Trial:StimType --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1我已经搜索了类似的问题，但是没有找到任何可以帮助我解决该问题的答案。我怀疑（鉴于错误消息）我可能不得不更改数据集的结构，但是由于我是R的初学者，所以我不知道如何执行此操作。如何在此数据集中运行混合方差分析？
我想使用R来分析一个实验，在该实验中，三组参与者分别被展示了两种刺激类型，分别进行了三遍。因变量是一个连续的量度。在这里...
由于Trial包含有关Trial和Stim的信息，因此固定效果模型矩阵秩不足。我们可以通过将Trial变量分为两个变量来纠正此问题。