You can use the LSD.test or waller.test of the package agricolae that less
conservatives than tukey.
________________________________
From: [EMAIL PROTECTED] on behalf of Frederic Jean
Sent: Fri 3/2/2007 4:52 PM
To: [EMAIL PROTECTED]
Subject: [R] significant anova but no distinct groups ?
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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and provide commented, minimal, self-contained, reproducible code.
