Sven Garbade <garbade at psy.uni-muenchen.de> writes:
> Hi all,
>
> I can't figure out how to compute Wilks Lambda in a one way repeated
> measure design. My matrix looks like:
>
> > t2.m
> Blank ECR ENC UEA UED
> 1 -0.15 0.14 0.16 0.09 0.14
> 2 0.30 0.08 0.14 0.14 0.14
> [...]
>
> where each row is a case and the columns are levels of one factor (named
> trial):
>
> > t2.fit <- manova(t2.m ~ 1)
> > summary(t2.fit, intercept=T, test="Wilks")
> Df Wilks approx F num Df den Df Pr(>F)
> (Intercept) 1 0.26869 1.63302 5 3 0.3642
> Residuals 7
>
> ist this correct? I ask because SPSS gives me a different result:
>
> Effect: Trial
> Wilks: 0.392
> F: 1.554
> Df: 4
> error Df: 4
> Pr: 0.340
>
> Thanks for any hints, Sven
Hmm. The ways of SPSS are sometimes mysterious, but the Df suggest
that you're testing that the 5 variables all have mean zero, whereas
SPSS might be testing whether they have the *same* mean. You can check
that by looking at
t2.m2 <- t2.m[,-1] - t2.m[,1]
t2.fit <- manova(t2.m ~ 1)
--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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