Normality for repeated measures 2x3 ANOVA in R

Question

I've tried different suggestions for similar questions here, but so far I haven't been able to figure this out.

I have data from 5 subjects who performed a task under six different conditions, where I have two factors, A and B, A has 2 levels and B 3. I am interested in seeing whether there is an A, B, or AxB effect. This is what my data look like:

> data
        A1B1       A1B2       A1B3       A2B1        A2B2      A2B3
       <dbl>      <dbl>      <dbl>      <dbl>       <dbl>     <dbl>
1 0.23694106 0.24721370 0.24243452 0.18634988 0.185587802 0.2272034
2 0.28706070 0.29954235 0.26044499 0.22570883 0.249875419 0.2134954
3 0.10159571 0.14058360 0.08654673 0.05068541 0.031204063 0.0278373
4 0.21372617 0.15595512 0.18168078 0.22170997 0.137082131 0.1698777
5 0.04138218 0.01654181 0.02714175 0.02570313 0.006663436 0.0648169

This is how I structured the data frame:

nsubs<-length(data$Var1)
resp<-c(data$A1B1,data$A1B2,data$A1B3,data$A2B1,data$A2B2,data$A2B3)

myData<-data.frame(subs=rep(seq(from=1, to= nsubs, by=1),6),response=resp,A=c(rep("A1", nsubs*3),rep("A2", nsubs*3)),B=c(rep("B1", nsubs),rep("B2", nsubs),rep("B3", nsubs), rep("B1", nsubs),rep("B2", nsubs),rep("B3", nsubs)))

myData<-within(myData,{subs <-factor(subs)
A<-factor(A)
B<-factor(B)})

So myData looks so:

  subs    response A B
1     1 0.236941060 A1 B1
2     2 0.287060695 A1 B1
3     3 0.101595710 A1 B1
4     4 0.213726170 A1 B1
5     5 0.041382181 A1 B1
6     1 0.247213703 A1 B2
7     2 0.299542346 A1 B2
8     3 0.140583600 A1 B2
9     4 0.155955122 A1 B2
10    5 0.016541809 A1 B2
11    1 0.242434520 A1 B3
12    2 0.260444991 A1 B3
13    3 0.086546733 A1 B3
14    4 0.181680780 A1 B3
15    5 0.027141747 A1 B3
16    1 0.186349883  A2 B1
17    2 0.225708832  A2 B1
18    3 0.050685407  A2 B1
19    4 0.221709965  A2 B1
20    5 0.025703135  A2 B1
21    1 0.185587802  A2 B2
22    2 0.249875419  A2 B2
23    3 0.031204063  A2 B2
24    4 0.137082131  A2 B2
25    5 0.006663436  A2 B2
26    1 0.227203355  A2 B3
27    2 0.213495365  A2 B3
28    3 0.027837305  A2 B3
29    4 0.169877668  A2 B3
30    5 0.064816901  A2 B3

I can then run a repeated measures 2x3 ANOVA:

response.aov<-with(myData,aov(response ~ A * B + Error(subs /(A*B))))
print(summary(response.aov))

The problem is that first I should make sure the residuals are normally distributed. I've tried

myData.res = ERP
myData.res$M1.Fit = fitted(myData.mod1)
myData.res$M1.Resid = resid(myData.mod1)
print(ggplot(myData.res, aes(sample = M1.Resid)) + stat_qq())
result <- shapiro.test(myData.res$M1.Resid)

however, fitted(myData.mod1) returns NULL. I also tried:

m <- aov(resp ~ A*B+Error(subs /(A*B)), data=myData)
m.res<-proj(m)

but m.res contains residuals for subs, subs:A, subs:B, and subs:A:B. Which are the relevant residuals? Does anyone know a better method for checking normality for this design?

This link may be helpful: https://stackoverflow.com/questions/26169153/how-to-get-residuals-from-repeated-measures-anova-model-in-r — Sal Mangiafico, Oct 24 '17 at 16:13
Also : https://stackoverflow.com/questions/16026072/extract-residuals-from-aov — Sal Mangiafico, Oct 24 '17 at 16:24
A different approach would be to conduct the repeated measures analysis with a mixed model. The following gives an example of a model in both aov and with lme. https://stats.stackexchange.com/questions/188230/lme-versus-aov-repeated-measures-variance-specification . It is easy to get the residuals from the lme object. — Sal Mangiafico, Oct 24 '17 at 16:42
Or to use lme or gls with a correlation structure. The answer and comments here have some very useful information about the assumptions in using aov for repeated measures and how to use lme or gls in an equivalent way: https://stats.stackexchange.com/questions/14088/why-do-lme-and-aov-return-different-results-for-repeated-measures-anova-in-r . — Sal Mangiafico, Oct 24 '17 at 17:09

Normality for repeated measures 2x3 ANOVA in R

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