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R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
> library("multcomp")
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS
Attaching package: 'TH.data'
The following object is masked from 'package:MASS':
geyser
> set.seed(290875)
>
> testdata <- data.frame(y = rnorm(21),
+ f1 <- factor(c(rep(c("A", "B", "C"), 7))),
+ f2 <- factor(c(rep("D", 10), rep("E", 11))),
+ x <- rnorm(21))
>
> # one-way ANOVA
> coef(amod <- aov(y ~ f1, data = testdata))
(Intercept) f1B f1C
-0.4394751 0.5151680 0.6886101
> glht(amod, linfct = mcp(f1 = "Dunnett"))
General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Linear Hypotheses:
Estimate
B - A == 0 0.5152
C - A == 0 0.6886
>
> # and a continuous covariable: ANCOVA
> coef(lmod <- lm(y ~ f1 + x, data = testdata))
(Intercept) f1B f1C x
-0.434528566 0.509444592 0.686181780 -0.009491201
> glht(lmod, linfct = mcp(f1 = "Dunnett"))
General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Linear Hypotheses:
Estimate
B - A == 0 0.5094
C - A == 0 0.6862
>
> # ANCOVA with an additional factor as covariable
> coef(lmod <- lm(y ~ f1 + f2 + x, data = testdata))
(Intercept) f1B f1C f2E x
-0.40849498 0.51296437 0.69200699 -0.05266965 -0.01613183
> glht(lmod, linfct = mcp(f1 = "Dunnett"))
General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Linear Hypotheses:
Estimate
B - A == 0 0.513
C - A == 0 0.692
>
> # and with interaction terms
> coef(lmod <- lm(y ~ f1 + f2 + f2:f1 + x, data = testdata))
(Intercept) f1B f1C f2E x f1B:f2E
-0.44532319 0.70282663 0.65613337 0.05552324 -0.03443721 -0.37862471
f1C:f2E
0.02753451
> glht(lmod, linfct = mcp(f1 = "Dunnett"))
General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Linear Hypotheses:
Estimate
B - A == 0 0.7028
C - A == 0 0.6561
Warning message:
In mcp2matrix(model, linfct = linfct) :
covariate interactions found -- default contrast might be inappropriate
>
> # with contrasts as expressions
> glht(lmod, linfct = mcp(f1 = c("B - A = 0", "C - A = 0")))
General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Linear Hypotheses:
Estimate
B - A == 0 0.7028
C - A == 0 0.6561
Warning message:
In mcp2matrix(model, linfct = linfct) :
covariate interactions found -- default contrast might be inappropriate
>
> tmp <- multcomp:::chrlinfct2matrix(c(l1 = "x1 - x2 = 2",
+ l2 = "x2 + 3 * x3 = 1"),
+ paste("x", 1:3, sep = ""))
>
> stopifnot(max(abs(tmp$K - rbind(c(1, -1, 0), c(0, 1, 3)))) < sqrt(.Machine$double.eps))
> stopifnot(max(abs(tmp$m - c(2, 1))) < sqrt(.Machine$double.eps))
>
> ### coef.survreg and vcov.survreg need special tuning
> ### thx to Z for pointing this out
> if (require("survival")) {
+ smod <- survreg(Surv(futime, fustat) ~ ecog.ps + rx,
+ data = ovarian, dist = 'weibull')
+ K <- diag(length(coef(smod)))
+ rownames(K) <- names(coef(smod))
+ glht(smod, linfct = K)
+ }
General Linear Hypotheses
Linear Hypotheses:
Estimate
(Intercept) == 0 6.8967
ecog.ps == 0 -0.3850
rx == 0 0.5286
>
> ### new `means' comparisons
> amod <- aov(weight ~ dose + gesttime + number, data = litter)
> confint(glht(amod, linfct = mcp(dose = "Means")))
Simultaneous Confidence Intervals
Multiple Comparisons of Means: Mean Contrasts
Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)
Quantile = 2.5558
95% family-wise confidence level
Linear Hypotheses:
Estimate lwr upr
0 == 0 32.3651 30.0805 34.6498
5 == 0 29.0127 26.6372 31.3883
50 == 0 30.0743 27.5239 32.6246
500 == 0 29.6899 27.1591 32.2207
>
> proc.time()
user system elapsed
0.516 0.012 0.525
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