Litter Weights Data Set
litter.RdDose response of litter weights in rats.
Usage
data("litter")Format
This data frame contains the following variables
- dose
dosages at four levels:
0,5,50,500.- gesttime
gestation time as covariate.
- number
number of animals in litter as covariate.
- weight
response variable: average post-birth weights in the entire litter.
Details
Pregnant mice were divided into four groups and the compound in four different doses was administered during pregnancy. Their litters were evaluated for birth weights, see multcomp::westfall1999, page 109, and multcomp::westfall1997.
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 0.0646 .
#> 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: Completion with error > abseps
#> Warning: 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 0.0322 *
#> 50 - 0 >= 0 -2.2909 1.3384 -1.712 0.2034
#> 500 - 0 >= 0 -2.6752 1.3343 -2.005 0.1191
#> 50 - 5 >= 0 1.0615 1.3973 0.760 0.9999
#> 500 - 5 >= 0 0.6772 1.3394 0.506 0.9987
#> 500 - 50 >= 0 -0.3844 1.4510 -0.265 0.8907
#> otrend >= 0 -3.4821 2.0867 -1.669 0.2183
#> atrend >= 0 -314.7324 408.9901 -0.770 0.6620
#> ltrend >= 0 -1.9400 0.9616 -2.018 0.1162
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>