Aliases: litter
Keywords: datasets
### ** Examples ### fit ANCOVA model to data amod <- aov(weight ~ dose + gesttime + number, data = litter) ### define matrix of linear hypotheses for `dose' doselev <- as.integer(levels(litter$dose)) K <- rbind(contrMat(table(litter$dose), "Tukey"), otrend = c(-1.5, -0.5, 0.5, 1.5), atrend = doselev - mean(doselev), ltrend = log(1:4) - mean(log(1:4))) ### set up multiple comparison object Kht <- glht(amod, linfct = mcp(dose = K), alternative = "less") ### cf. Westfall (1997, Table 2) summary(Kht, test = univariate())
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Linear Hypotheses:
Estimate Std. Error t value Pr(<t)
5 - 0 >= 0 -3.3524 1.2908 -2.597 0.00575 **
50 - 0 >= 0 -2.2909 1.3384 -1.712 0.04576 *
500 - 0 >= 0 -2.6752 1.3343 -2.005 0.02448 *
50 - 5 >= 0 1.0615 1.3973 0.760 0.77498
500 - 5 >= 0 0.6772 1.3394 0.506 0.69260
500 - 50 >= 0 -0.3844 1.4510 -0.265 0.39595
otrend >= 0 -3.4821 2.0867 -1.669 0.04988 *
atrend >= 0 -314.7324 408.9901 -0.770 0.22212
ltrend >= 0 -1.9400 0.9616 -2.018 0.02379 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
summary(Kht, test = adjusted("bonferroni"))
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Linear Hypotheses:
Estimate Std. Error t value Pr(<t)
5 - 0 >= 0 -3.3524 1.2908 -2.597 0.0518 .
50 - 0 >= 0 -2.2909 1.3384 -1.712 0.4118
500 - 0 >= 0 -2.6752 1.3343 -2.005 0.2203
50 - 5 >= 0 1.0615 1.3973 0.760 1.0000
500 - 5 >= 0 0.6772 1.3394 0.506 1.0000
500 - 50 >= 0 -0.3844 1.4510 -0.265 1.0000
otrend >= 0 -3.4821 2.0867 -1.669 0.4490
atrend >= 0 -314.7324 408.9901 -0.770 1.0000
ltrend >= 0 -1.9400 0.9616 -2.018 0.2141
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- bonferroni method)
summary(Kht, test = adjusted("Shaffer"))
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Linear Hypotheses:
Estimate Std. Error t value Pr(<t)
5 - 0 >= 0 -3.3524 1.2908 -2.597 0.0518 .
50 - 0 >= 0 -2.2909 1.3384 -1.712 0.0915 .
500 - 0 >= 0 -2.6752 1.3343 -2.005 0.0734 .
50 - 5 >= 0 1.0615 1.3973 0.760 1.0000
500 - 5 >= 0 0.6772 1.3394 0.506 1.0000
500 - 50 >= 0 -0.3844 1.4510 -0.265 1.0000
otrend >= 0 -3.4821 2.0867 -1.669 0.0998 .
atrend >= 0 -314.7324 408.9901 -0.770 0.4442
ltrend >= 0 -1.9400 0.9616 -2.018 0.0518 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- Shaffer method)
summary(Kht, test = adjusted("Westfall"))
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Linear Hypotheses:
Estimate Std. Error t value Pr(<t)
5 - 0 >= 0 -3.3524 1.2908 -2.597 0.0319 *
50 - 0 >= 0 -2.2909 1.3384 -1.712 0.0893 .
500 - 0 >= 0 -2.6752 1.3343 -2.005 0.0644 .
50 - 5 >= 0 1.0615 1.3973 0.760 0.7750
500 - 5 >= 0 0.6772 1.3394 0.506 0.7271
500 - 50 >= 0 -0.3844 1.4510 -0.265 0.7271
otrend >= 0 -3.4821 2.0867 -1.669 0.0917 .
atrend >= 0 -314.7324 408.9901 -0.770 0.3951
ltrend >= 0 -1.9400 0.9616 -2.018 0.0459 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- Westfall method)
summary(Kht, test = adjusted("single-step"))
Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Linear Hypotheses:
Estimate Std. Error t value Pr(<t)
5 - 0 >= 0 -3.3524 1.2908 -2.597 0.032 *
50 - 0 >= 0 -2.2909 1.3384 -1.712 0.203
500 - 0 >= 0 -2.6752 1.3343 -2.005 0.118
50 - 5 >= 0 1.0615 1.3973 0.760 1.000
500 - 5 >= 0 0.6772 1.3394 0.506 0.999
500 - 50 >= 0 -0.3844 1.4510 -0.265 0.891
otrend >= 0 -3.4821 2.0867 -1.669 0.218
atrend >= 0 -314.7324 408.9901 -0.770 0.662
ltrend >= 0 -1.9400 0.9616 -2.018 0.116
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)