I have a repeated measures design, where A and B are within-subjects factors. I tried
m1 <- aov(y ~ A * B + Error(subject/(A*B)))
summary(m1)
But the results came back:
Error: subject
Df Sum Sq Mean Sq F value Pr(>F)
A 1 176166607 176166607 20.89 0.00600 **
B 1 143823233 143823233 17.05 0.00909 **
Residuals 5 42166494 8433299
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: subject:A
Df Sum Sq Mean Sq F value Pr(>F)
A 1 2263122 2263122 4.404 0.0899 .
B 2 1056720 528360 1.028 0.4227
Residuals 5 2569581 513916
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: subject:B
Df Sum Sq Mean Sq F value Pr(>F)
B 3 2.531e+09 843650464 60.871 6.22e-10 ***
A:B 2 8.212e+07 41060723 2.963 0.0759 .
Residuals 19 2.633e+08 13859601
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: subject:A:B
Df Sum Sq Mean Sq F value Pr(>F)
A:B 3 2075177 691726 1.152 0.351
Residuals 21 12610432 600497
Error: Within
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 477 442259616 927169
Why is each effect showing up with so many error terms? Am I misunderstanding something? Or am I doing something wrong?
table(A, B, subject)
. Every item should be 1. If it's not, fix that first and rerun and repost the results. $\endgroup$