[R] Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

[maximilian.mueller]

Hello everyone,

I am also trying to do within subjects repeated measures anova followed by
the
test of sphericity (see sample dataset attached below) and I am facing some
problems. 

I tried to assign the example to my research question, but could not really
solve it.

Here is the syntax:

> options(contrasts=c("contr.sum", "contr.poly"))
> read.csv2("test21.csv") -> dat3
> mod3 <- lm(cbind(umsatz_t1, umsatz_t2, umsatz_t3, umsatz_t4) +
> cbind(ebitda_t1, ebitda_t2, ebitda_t3, ebitda_t4)
+ 	~ 1, data=dat3)
> idata3 <- data.frame(Umsatz=factor(1:4), EBITDA=factor(1:4))
> aov3 <- Anova(mod3, idata=idata3, idesign= ~Umsatz+EBITDA, type="III")
Fehler in check.imatrix(X.design) : 
  Terms in the intra-subject model matrix are not orthogonal.
> summary(aov3, multivariate=F) 

with the data set  "dat3"

> dat3
    umsatz_t1  umsatz_t2  umsatz_t3  umsatz_t4  ebitda_t1  ebitda_t2 
ebitda_t3
1  0.10239021 0.18241379 0.28875806 0.28875806 0.07622154 0.05543311
0.06776469
2  0.10239021 1.21769038 0.05840222 0.05840222 0.04794192 0.04642960
0.03727069
3  0.05124775 0.65373713 0.25976771 0.25976771 0.04794192 0.04435191
0.03167950
4  0.10239021 0.18241379 0.28875806 0.28875806 0.07622154 0.05543311
0.06776469
5  0.10239021 1.21769038 0.05840222 1.21769038 0.04794192 0.04642960
0.03727069
6  0.06776469 0.28875806 0.06776469 0.65373713 0.07622154 0.18241379
0.10239021
7  0.03727069 0.05840222 0.03727069 0.18241379 0.04794192 0.25976771
0.04794192
8  0.03727069 0.25976771 0.06776469 1.21769038 0.04557920 0.65373713
0.07622154
9  0.06776469 0.28875806 0.06776469 0.28875806 0.07622154 0.18241379
0.04794192
10 0.03727069 0.05840222 0.03727069 0.05840222 0.04794192 0.04642960
0.04557920
11 0.25976771 0.05543311 0.03727069 0.05543311 0.07622154 0.06776469
0.03167950
12 0.28875806 0.04642960 0.07622154 0.04642960 0.04794192 0.03727069
0.06776469
13 0.05840222 0.18241379 0.04794192 0.25976771 0.07622154 0.03167950
0.03727069
14 0.06776469 1.21769038 0.07622154 0.28875806 0.04794192 0.06776469
0.04642960
15 0.03727069 0.28875806 0.03727069 0.03727069 0.06776469 0.03727069
0.04435191
16 0.04794192 0.04642960 0.04557920 0.04557920 0.06776469 0.28875806
0.06776469
17 0.07622154 0.06776469 0.03167950 0.07622154 0.03727069 0.05840222
0.03727069
18 0.04794192 0.04642960 0.03727069 0.04435191 0.07622154 0.03167950
0.03727069
19 0.04794192 0.04435191 0.03167950 0.05543311 0.04794192 0.06776469
0.04642960
    ebitda_t4
1  0.04642960
2  0.04435191
3  0.05543311
4  0.04642960
5  0.18241379
6  0.25976771
7  0.04794192
8  0.07622154
9  0.04794192
10 0.04557920
11 0.07622154
12 0.04794192
13 0.04557920
14 0.03167950
15 0.03167950
16 0.65373713
17 0.18241379
18 0.04557920
19 0.03167950
> 

Thanks four your help...

Sincerely, 
Max



-----
M a x i m i l i a n  M ue l l e r

PhD-Student
Department of Business Studies
Leuphana Universität Lüneburg

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