Frederic,
You're performing 8*7/2 = 28 multiple comparisons controlling the FWE at
the .05 level. Using a Bonferroni's adjustment (admittedly more
conservative than the Holm's or Tukey's approach), that's testing each
comparison at the .05/28 = 0.0018 level. With only 100 observations spread
over 8 levels, you won't have much power to detect a difference.
Regards,
-Cody
Frederic Jean
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Subject
[R] significant anova but no
03/02/2007 01:52 distinct groups ?
PM
Dear all,
I am studying a dataset using the aov() function.
The independant variable 'cds' is a factor() with 8 levels and here is
the result in studying the dependant variable 'rta' with aov() :
> summary(aov(rta ~ cds))
Df Sum Sq Mean Sq F value Pr(>F)
cds 7 0.34713 0.04959 2.3807 0.02777
Residuals 92 1.91635 0.02083
The dependant variable 'rta' is normally distributed and variances are
homogeneous.
But when studying the result with TukeyHSD, no differences in 'rta'
are seen among groups of 'cds' :
> TukeyHSD(aov(rta ~ cds), which="cds")
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = rta ~ cds)
$cds
diff lwr upr p adj
1-0 -0.1046092796 -0.4331100 0.22389141 0.9751178
2-0 0.0359991860 -0.1371359 0.20913425 0.9980970
3-0 0.0261665235 -0.1348524 0.18718540 0.9996165
4-0 0.0004502442 -0.1805448 0.18144531 1.0000000
5-0 -0.1438949939 -0.3104752 0.02268526 0.1422670
[...]
7-5 0.0621598639 -0.1027595 0.22707926 0.9386170
7-6 0.0256519274 -0.1757408 0.22704465 0.9999248
I tried a pairwise.t.test (holm correction) which also was not able to
detect differences in 'rta' among groups of 'cds'
I've never been confronted to such a situation before : is it just a
problem of power of the /a posteriori/ tests used ? Do I miss
something important in basic stats or in R ?
How to highlight differences among 'cds' groups seen with aov() ?
Any help appreciated
Thanks in advance,
Fred J.
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