[R] restricting pairwise comparisons of interaction effects
Levi Waldron
leviwaldron at gmail.com
Mon Apr 28 23:07:29 CEST 2008
I'm interested in restricting the pairwise comparisons of interaction
effects in a multi-way factorial ANOVA, because I find comparisons of
interactions between all different variables different to interpret.
For example (supposing a p<0.10 cutoff just to be able to use this
example):
> summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks))
Df Sum Sq Mean Sq F value Pr(>F)
wool 1 450.7 450.7 3.7653 0.0582130 .
tension 2 2034.3 1017.1 8.4980 0.0006926 ***
wool:tension 2 1002.8 501.4 4.1891 0.0210442 *
Residuals 48 5745.1 119.7
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> TukeyHSD(fm1,conf.level=0.90)
Tukey multiple comparisons of means
90% family-wise confidence level
Fit: aov(formula = breaks ~ wool * tension, data = warpbreaks)
$wool
diff lwr upr p adj
B-A -5.777778 -10.77183 -0.7837284 0.058213
$tension
diff lwr upr p adj
M-L -10.000000 -17.66710 -2.332900 0.0228554
H-L -14.722222 -22.38932 -7.055122 0.0005595
H-M -4.722222 -12.38932 2.944878 0.4049442
$`wool:tension`
diff lwr upr p adj
B:L-A:L -16.3333333 -30.112566 -2.554101 0.0302143
A:M-A:L -20.5555556 -34.334788 -6.776323 0.0029580
B:M-A:L -15.7777778 -29.557010 -1.998545 0.0398172
A:H-A:L -20.0000000 -33.779233 -6.220767 0.0040955
B:H-A:L -25.7777778 -39.557010 -11.998545 0.0001136
A:M-B:L -4.2222222 -18.001455 9.557010 0.9626541
B:M-B:L 0.5555556 -13.223677 14.334788 0.9999978
A:H-B:L -3.6666667 -17.445899 10.112566 0.9797123
B:H-B:L -9.4444444 -23.223677 4.334788 0.4560950
B:M-A:M 4.7777778 -9.001455 18.557010 0.9377205
A:H-A:M 0.5555556 -13.223677 14.334788 0.9999978
B:H-A:M -5.2222222 -19.001455 8.557010 0.9114780
A:H-B:M -4.2222222 -18.001455 9.557010 0.9626541
B:H-B:M -10.0000000 -23.779233 3.779233 0.3918767
B:H-A:H -5.7777778 -19.557010 8.001455 0.8705572
It would seem to make sense (and please correct me if I'm wrong) to
restrict the pairwise comparisons of wool:tension to terms like
B:L-A:L, and A:M-A:L, and not calculate or try to interpret
differences like B:M-A:L. How can I accomplish this (note that there
are actually 5 factors in the experiment I'm analyzing)?
More information about the R-help
mailing list