Aliases: detergent
Keywords: datasets
### ** Examples ### set up two-way ANOVA without interactions amod <- aov(plates ~ block + detergent, data = detergent) ### set up all-pair comparisons dht <- glht(amod, linfct = mcp(detergent = "Tukey")) ### see Westfall et al. (1999, p. 190) confint(dht)
Simultaneous Confidence Intervals
Multiple Comparisons of Means: Tukey Contrasts
Fit: aov(formula = plates ~ block + detergent, data = detergent)
Quantile = 3.0642
95% family-wise confidence level
Linear Hypotheses:
Estimate lwr upr
B - A == 0 -2.1333 -4.7929 0.5263
C - A == 0 3.6000 0.9404 6.2596
D - A == 0 2.2000 -0.4596 4.8596
E - A == 0 -4.3333 -6.9929 -1.6737
C - B == 0 5.7333 3.0737 8.3929
D - B == 0 4.3333 1.6737 6.9929
E - B == 0 -2.2000 -4.8596 0.4596
D - C == 0 -1.4000 -4.0596 1.2596
E - C == 0 -7.9333 -10.5929 -5.2737
E - D == 0 -6.5333 -9.1929 -3.8737
### see Westfall et al. (1999, p. 192) summary(dht, test = univariate())
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: aov(formula = plates ~ block + detergent, data = detergent)
Linear Hypotheses:
Estimate Std. Error t value Pr(>|t|)
B - A == 0 -2.1333 0.8679 -2.458 0.025762 *
C - A == 0 3.6000 0.8679 4.148 0.000757 ***
D - A == 0 2.2000 0.8679 2.535 0.022075 *
E - A == 0 -4.3333 0.8679 -4.993 0.000133 ***
C - B == 0 5.7333 0.8679 6.606 6.05e-06 ***
D - B == 0 4.3333 0.8679 4.993 0.000133 ***
E - B == 0 -2.2000 0.8679 -2.535 0.022075 *
D - C == 0 -1.4000 0.8679 -1.613 0.126291
E - C == 0 -7.9333 0.8679 -9.140 9.45e-08 ***
E - D == 0 -6.5333 0.8679 -7.527 1.21e-06 ***
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
## Not run: ##D summary(dht, test = adjusted("Shaffer")) ##D summary(dht, test = adjusted("Westfall")) ##D ## End(Not run)