1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
|
R version 2.4.0 (2006-10-03)
Copyright (C) 2006 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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
> 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 0.5135
C - A == 0 0.6699
>
> # 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 0.5135
C - A == 0 0.6699
>
> 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)
+ }
Loading required package: survival
Loading required package: splines
General Linear Hypotheses
Linear Hypotheses:
Estimate
(Intercept) == 0 6.8967
ecog.ps == 0 -0.3850
rx == 0 0.5286
>
|
