R Under development (unstable) (2018-03-15 r74406) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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.
> ### Regression tests for the transformation functions
>
> set.seed(290875)
> library("coin")
Loading required package: survival
> isequal <- coin:::isequal
> options(useFancyQuotes = FALSE)
>
>
> ### NA handling: continuous
> x <- c(1L, 2L, NA, 3L, 3L, NA, 4L, 5L, NA)
> cc <- complete.cases(x)
>
> id_trafo(x)
[1] 1 2 NA 3 3 NA 4 5 NA
> id_trafo(x[cc])
[1] 1 2 3 3 4 5
>
> rank_trafo(x)
[1] 1.0 2.0 NA 3.5 3.5 NA 5.0 6.0 NA
> rank_trafo(x[cc])
[1] 1.0 2.0 3.5 3.5 5.0 6.0
> rank_trafo(x, ties.method = "random")
[1] 1 2 NA 4 3 NA 5 6 NA
> rank_trafo(x[cc], ties.method = "random")
[1] 1 2 3 4 5 6
>
> normal_trafo(x)
[1] -1.0675705 -0.5659488 NA 0.0000000 0.0000000 NA 0.5659488
[8] 1.0675705 NA
> normal_trafo(x[cc])
[1] -1.0675705 -0.5659488 0.0000000 0.0000000 0.5659488 1.0675705
> normal_trafo(x, ties.method = "average-scores")
[1] -1.067571e+00 -5.659488e-01 NA -8.326673e-17 -8.326673e-17
[6] NA 5.659488e-01 1.067571e+00 NA
> normal_trafo(x[cc], ties.method = "average-scores")
[1] -1.067571e+00 -5.659488e-01 -8.326673e-17 -8.326673e-17 5.659488e-01
[6] 1.067571e+00
>
> median_trafo(x)
[1] 0 0 NA 0 0 NA 1 1 NA
> median_trafo(x[cc])
[1] 0 0 0 0 1 1
> median_trafo(x, mid.score = "0.5")
[1] 0.0 0.0 NA 0.5 0.5 NA 1.0 1.0 NA
> median_trafo(x[cc], mid.score = "0.5")
[1] 0.0 0.0 0.5 0.5 1.0 1.0
> median_trafo(x, mid.score = "1")
[1] 0 0 NA 1 1 NA 1 1 NA
> median_trafo(x[cc], mid.score = "1")
[1] 0 0 1 1 1 1
>
> savage_trafo(x)
[1] -0.8333333 -0.6333333 NA -0.1333333 -0.1333333 NA 0.3666667
[8] 1.3666667 NA
> savage_trafo(x[cc])
[1] -0.8333333 -0.6333333 -0.1333333 -0.1333333 0.3666667 1.3666667
> savage_trafo(x, ties.method = "average-scores")
[1] -0.8333333 -0.6333333 NA -0.2166667 -0.2166667 NA 0.4500000
[8] 1.4500000 NA
> savage_trafo(x[cc], ties.method = "average-scores")
[1] -0.8333333 -0.6333333 -0.2166667 -0.2166667 0.4500000 1.4500000
>
> consal_trafo(x)
[1] 0.0004164931 0.0066638900 NA 0.0625000000 0.0625000000
[6] NA 0.2603082049 0.5397750937 NA
> consal_trafo(x[cc])
[1] 0.0004164931 0.0066638900 0.0625000000 0.0625000000 0.2603082049
[6] 0.5397750937
> consal_trafo(x, a = c(2, 5))
a = 2 a = 5
[1,] 0.1428571 0.0004164931
[2,] 0.2857143 0.0066638900
[3,] NA NA
[4,] 0.5000000 0.0625000000
[5,] 0.5000000 0.0625000000
[6,] NA NA
[7,] 0.7142857 0.2603082049
[8,] 0.8571429 0.5397750937
[9,] NA NA
> consal_trafo(x[cc], a = c(2, 5))
a = 2 a = 5
[1,] 0.1428571 0.0004164931
[2,] 0.2857143 0.0066638900
[3,] 0.5000000 0.0625000000
[4,] 0.5000000 0.0625000000
[5,] 0.7142857 0.2603082049
[6,] 0.8571429 0.5397750937
> consal_trafo(x, ties.method = "average-scores")
[1] 0.0004164931 0.0066638900 NA 0.0701790920 0.0701790920
[6] NA 0.2603082049 0.5397750937 NA
> consal_trafo(x[cc], ties.method = "average-scores")
[1] 0.0004164931 0.0066638900 0.0701790920 0.0701790920 0.2603082049
[6] 0.5397750937
> consal_trafo(x, ties.method = "average-scores", a = c(2, 5))
a = 2 a = 5
[1,] 0.1428571 0.0004164931
[2,] 0.2857143 0.0066638900
[3,] NA NA
[4,] 0.5000000 0.0701790920
[5,] 0.5000000 0.0701790920
[6,] NA NA
[7,] 0.7142857 0.2603082049
[8,] 0.8571429 0.5397750937
[9,] NA NA
> consal_trafo(x[cc], ties.method = "average-scores", a = c(2, 5))
a = 2 a = 5
[1,] 0.1428571 0.0004164931
[2,] 0.2857143 0.0066638900
[3,] 0.5000000 0.0701790920
[4,] 0.5000000 0.0701790920
[5,] 0.7142857 0.2603082049
[6,] 0.8571429 0.5397750937
>
> koziol_trafo(x)
[1] 1.2741624 0.8817477 NA 0.0000000 0.0000000 NA -0.8817477
[8] -1.2741624 NA
> koziol_trafo(x[cc])
[1] 1.2741624 0.8817477 0.0000000 0.0000000 -0.8817477 -1.2741624
> koziol_trafo(x, j = 2)
[1] 0.8817477 -0.3146921 NA -1.4142136 -1.4142136 NA -0.3146921
[8] 0.8817477 NA
> koziol_trafo(x[cc], j = 2)
[1] 0.8817477 -0.3146921 -1.4142136 -1.4142136 -0.3146921 0.8817477
> koziol_trafo(x, ties.method = "average-scores")
[1] 1.274162e+00 8.817477e-01 NA 5.551115e-17 5.551115e-17
[6] NA -8.817477e-01 -1.274162e+00 NA
> koziol_trafo(x[cc], ties.method = "average-scores")
[1] 1.274162e+00 8.817477e-01 5.551115e-17 5.551115e-17 -8.817477e-01
[6] -1.274162e+00
> koziol_trafo(x, ties.method = "average-scores", j = 2)
[1] 0.8817477 -0.3146921 NA -1.2741624 -1.2741624 NA -0.3146921
[8] 0.8817477 NA
> koziol_trafo(x[cc], ties.method = "average-scores", j = 2)
[1] 0.8817477 -0.3146921 -1.2741624 -1.2741624 -0.3146921 0.8817477
>
> klotz_trafo(x)
[1] 1.1397068 0.3202981 NA 0.0000000 0.0000000 NA 0.3202981
[8] 1.1397068 NA
> klotz_trafo(x[cc])
[1] 1.1397068 0.3202981 0.0000000 0.0000000 0.3202981 1.1397068
> klotz_trafo(x, ties.method = "average-scores")
[1] 1.13970682 0.32029807 NA 0.03240445 0.03240445 NA 0.32029807
[8] 1.13970682 NA
> klotz_trafo(x[cc], ties.method = "average-scores")
[1] 1.13970682 0.32029807 0.03240445 0.03240445 0.32029807 1.13970682
>
> mood_trafo(x)
[1] 6.25 2.25 NA 0.00 0.00 NA 2.25 6.25 NA
> mood_trafo(x[cc])
[1] 6.25 2.25 0.00 0.00 2.25 6.25
> mood_trafo(x, ties.method = "average-scores")
[1] 6.25 2.25 NA 0.25 0.25 NA 2.25 6.25 NA
> mood_trafo(x[cc], ties.method = "average-scores")
[1] 6.25 2.25 0.25 0.25 2.25 6.25
>
> ansari_trafo(x)
[1] 1.0 2.0 NA 3.5 3.5 NA 2.0 1.0 NA
> ansari_trafo(x[cc])
[1] 1.0 2.0 3.5 3.5 2.0 1.0
> ansari_trafo(x, ties.method = "average-scores")
[1] 1 2 NA 3 3 NA 2 1 NA
> ansari_trafo(x[cc], ties.method = "average-scores")
[1] 1 2 3 3 2 1
>
> fligner_trafo(x)
[1] 0.1800124 0.3661064 NA 0.6744898 0.6744898 NA 1.0675705
[8] 1.4652338 NA
> fligner_trafo(x[cc])
[1] 0.1800124 0.3661064 0.6744898 0.6744898 1.0675705 1.4652338
> fligner_trafo(x, ties.method = "average-scores")
[1] 0.1800124 0.3661064 NA 0.6787937 0.6787937 NA 1.0675705
[8] 1.4652338 NA
> fligner_trafo(x[cc], ties.method = "average-scores")
[1] 0.1800124 0.3661064 0.6787937 0.6787937 1.0675705 1.4652338
>
> maxstat_trafo(x)
x <= 1 x <= 2 x <= 3 x <= 4
1 1 1 1 1
2 0 1 1 1
3 NA NA NA NA
4 0 0 1 1
5 0 0 1 1
6 NA NA NA NA
7 0 0 0 1
8 0 0 0 0
9 NA NA NA NA
> maxstat_trafo(x[cc])
x <= 1 x <= 2 x <= 3 x <= 4
1 1 1 1 1
2 0 1 1 1
3 0 0 1 1
4 0 0 1 1
5 0 0 0 1
6 0 0 0 0
> maxstat_trafo(x, minprob = 0.3, maxprob = 0.51)
x <= 2
1 1
2 1
3 NA
4 0
5 0
6 NA
7 0
8 0
9 NA
> maxstat_trafo(x[cc], minprob = 0.3, maxprob = 0.51)
x <= 2
1 1
2 1
3 0
4 0
5 0
6 0
>
>
> ### NA handling: survival
> x <- c(1, 2, NA, 3, 3, NA, 4, 5, NA)
> cc <- complete.cases(x)
>
> logrank_trafo(Surv(x))
[1] -0.8333333 -0.6333333 NA -0.1333333 -0.1333333 NA 0.3666667
[8] 1.3666667 NA
> logrank_trafo(Surv(x)[cc])
[1] -0.8333333 -0.6333333 -0.1333333 -0.1333333 0.3666667 1.3666667
> logrank_trafo(Surv(x), ties.method = "Hothorn-Lausen")
[1] -0.83333333 -0.63333333 NA 0.03333333 0.03333333 NA
[7] 0.53333333 1.53333333 NA
> logrank_trafo(Surv(x)[cc], ties.method = "Hothorn-Lausen")
[1] -0.83333333 -0.63333333 0.03333333 0.03333333 0.53333333 1.53333333
> logrank_trafo(Surv(x), ties.method = "average-scores")
[1] -0.8333333 -0.6333333 NA -0.2166667 -0.2166667 NA 0.4500000
[8] 1.4500000 NA
> logrank_trafo(Surv(x)[cc], ties.method = "average-scores")
[1] -0.8333333 -0.6333333 -0.2166667 -0.2166667 0.4500000 1.4500000
>
> x <- c(1, 2, 3, 3, 3, 4, 4, 5, 5)
> e <- rep(c(0, NA, 1, 1), length.out = 9)
> cc <- complete.cases(x, e)
>
> logrank_trafo(Surv(x, e))
[1] 0.0000000 NA -0.6666667 -0.6666667 0.3333333 NA -0.3333333
[8] 0.1666667 1.1666667
> logrank_trafo(Surv(x, e)[cc])
[1] 0.0000000 -0.6666667 -0.6666667 0.3333333 -0.3333333 0.1666667 1.1666667
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen")
[1] 0.0000000 NA -0.5000000 -0.5000000 0.5000000 NA -0.1666667
[8] 0.8333333 1.8333333
> logrank_trafo(Surv(x, e)[cc], ties.method = "Hothorn-Lausen")
[1] 0.0000000 -0.5000000 -0.5000000 0.5000000 -0.1666667 0.8333333 1.8333333
> logrank_trafo(Surv(x, e), ties.method = "average-scores")
[1] 0.0000000 NA -0.7333333 -0.7333333 0.3666667 NA -0.3000000
[8] 0.2000000 1.2000000
> logrank_trafo(Surv(x, e)[cc], ties.method = "average-scores")
[1] 0.0000000 -0.7333333 -0.7333333 0.3666667 -0.3000000 0.2000000 1.2000000
>
> x <- c(1, 2, NA, 3, 3, NA, 4, 5, NA)
> e <- rep(c(0, NA, 1, 1), length.out = 9)
> cc <- complete.cases(x, e)
>
> logrank_trafo(Surv(x, e))
[1] 0.00 NA NA -0.75 0.25 NA -0.25 0.75 NA
> logrank_trafo(Surv(x, e)[cc])
[1] 0.00 -0.75 0.25 -0.25 0.75
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen")
[1] 0.0000000 NA NA -0.6666667 0.3333333 NA -0.1666667
[8] 0.8333333 NA
> logrank_trafo(Surv(x, e)[cc], ties.method = "Hothorn-Lausen")
[1] 0.0000000 -0.6666667 0.3333333 -0.1666667 0.8333333
> logrank_trafo(Surv(x, e), ties.method = "average-scores")
[1] 0.00 NA NA -0.75 0.25 NA -0.25 0.75 NA
> logrank_trafo(Surv(x, e)[cc], ties.method = "average-scores")
[1] 0.00 -0.75 0.25 -0.25 0.75
>
>
> ### NA handling: factor
> x <- factor(c(1, 1, NA, 2, NA, 3, 3, NA, 4), labels = as.roman(1:4))
> ox <- ordered(x)
> cc <- complete.cases(x)
>
> f_trafo(x)
I II III IV
1 1 0 0 0
2 1 0 0 0
3 NA NA NA NA
4 0 1 0 0
5 NA NA NA NA
6 0 0 1 0
7 0 0 1 0
8 NA NA NA NA
9 0 0 0 1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
> f_trafo(x[cc])
I II III IV
1 1 0 0 0
2 1 0 0 0
3 0 1 0 0
4 0 0 1 0
5 0 0 1 0
6 0 0 0 1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
>
> of_trafo(x)
[,1]
1 1
2 1
3 NA
4 2
5 NA
6 3
7 3
8 NA
9 4
Warning message:
In of_trafo(x) : 'x' is not an ordered factor
> of_trafo(x[cc])
[,1]
1 1
2 1
3 2
4 3
5 3
6 4
Warning message:
In of_trafo(x[cc]) : 'x[cc]' is not an ordered factor
> of_trafo(x, scores = 5:8)
[,1]
1 5
2 5
3 NA
4 6
5 NA
6 7
7 7
8 NA
9 8
Warning message:
In of_trafo(x, scores = 5:8) : 'x' is not an ordered factor
> of_trafo(x[cc], scores = 5:8)
[,1]
1 5
2 5
3 6
4 7
5 7
6 8
Warning message:
In of_trafo(x[cc], scores = 5:8) : 'x[cc]' is not an ordered factor
> of_trafo(x, scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 NA NA
4 6 10
5 NA NA
6 7 11
7 7 11
8 NA NA
9 8 12
Warning message:
In of_trafo(x, scores = list(s1 = 5:8, s2 = 9:12)) :
'x' is not an ordered factor
> of_trafo(x[cc], scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 6 10
4 7 11
5 7 11
6 8 12
Warning message:
In of_trafo(x[cc], scores = list(s1 = 5:8, s2 = 9:12)) :
'x[cc]' is not an ordered factor
>
> zheng_trafo(x, increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.0
5 NA NA
6 0 0.5
7 0 0.5
8 NA NA
9 1 1.0
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.5
5 NA NA
6 1 0.5
7 1 0.5
8 NA NA
9 1 1.0
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 NA NA
4 0.5 1
5 NA NA
6 1.0 1
7 1.0 1
8 NA NA
9 1.0 1
Warning message:
In zheng_trafo(x, increment = 0.5) : 'x' is not an ordered factor
> zheng_trafo(x[cc], increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.0
4 0 0.5
5 0 0.5
6 1 1.0
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.5
4 1 0.5
5 1 0.5
6 1 1.0
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 0.5 1
4 1.0 1
5 1.0 1
6 1.0 1
Warning message:
In zheng_trafo(x[cc], increment = 0.5) : 'x[cc]' is not an ordered factor
>
> of_trafo(ox)
[,1]
1 1
2 1
3 NA
4 2
5 NA
6 3
7 3
8 NA
9 4
> of_trafo(ox[cc])
[,1]
1 1
2 1
3 2
4 3
5 3
6 4
> of_trafo(ox, scores = 5:8)
[,1]
1 5
2 5
3 NA
4 6
5 NA
6 7
7 7
8 NA
9 8
> of_trafo(ox[cc], scores = 5:8)
[,1]
1 5
2 5
3 6
4 7
5 7
6 8
> of_trafo(ox, scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 NA NA
4 6 10
5 NA NA
6 7 11
7 7 11
8 NA NA
9 8 12
> of_trafo(ox[cc], scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 6 10
4 7 11
5 7 11
6 8 12
>
> zheng_trafo(ox, increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.0
5 NA NA
6 0 0.5
7 0 0.5
8 NA NA
9 1 1.0
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.5
5 NA NA
6 1 0.5
7 1 0.5
8 NA NA
9 1 1.0
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 NA NA
4 0.5 1
5 NA NA
6 1.0 1
7 1.0 1
8 NA NA
9 1.0 1
> zheng_trafo(ox[cc], increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.0
4 0 0.5
5 0 0.5
6 1 1.0
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.5
4 1 0.5
5 1 0.5
6 1 1.0
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 0.5 1
4 1.0 1
5 1.0 1
6 1.0 1
>
> fmaxstat_trafo(x)
{I} vs. {II, III, IV} {I, II} vs. {III, IV} {I, III} vs. {II, IV}
1 1 1 1
2 1 1 1
3 NA NA NA
4 0 1 0
5 NA NA NA
6 0 0 1
7 0 0 1
8 NA NA NA
9 0 0 0
{I, II, III} vs. {IV} {I, IV} vs. {II, III} {I, II, IV} vs. {III}
1 1 1 1
2 1 1 1
3 NA NA NA
4 1 0 1
5 NA NA NA
6 1 0 0
7 1 0 0
8 NA NA NA
9 0 1 1
{I, III, IV} vs. {II}
1 1
2 1
3 NA
4 0
5 NA
6 1
7 1
8 NA
9 1
> fmaxstat_trafo(x[cc])
{I} vs. {II, III, IV} {I, II} vs. {III, IV} {I, III} vs. {II, IV}
1 1 1 1
2 1 1 1
3 0 1 0
4 0 0 1
5 0 0 1
6 0 0 0
{I, II, III} vs. {IV} {I, IV} vs. {II, III} {I, II, IV} vs. {III}
1 1 1 1
2 1 1 1
3 1 0 1
4 1 0 0
5 1 0 0
6 0 1 1
{I, III, IV} vs. {II}
1 1
2 1
3 0
4 1
5 1
6 1
> fmaxstat_trafo(x, minprob = 0.49)
{I, II} vs. {III, IV} {I, IV} vs. {II, III}
1 1 1
2 1 1
3 NA NA
4 1 0
5 NA NA
6 0 0
7 0 0
8 NA NA
9 0 1
> fmaxstat_trafo(x[cc], minprob = 0.49)
{I, II} vs. {III, IV} {I, IV} vs. {II, III}
1 1 1
2 1 1
3 1 0
4 0 0
5 0 0
6 0 1
>
> ofmaxstat_trafo(ox)
{I} vs. {II, III, IV} {I, II} vs. {III, IV} {I, II, III} vs. {IV}
1 1 1 1
2 1 1 1
3 NA NA NA
4 0 1 1
5 NA NA NA
6 0 0 1
7 0 0 1
8 NA NA NA
9 0 0 0
> ofmaxstat_trafo(ox[cc])
{I} vs. {II, III, IV} {I, II} vs. {III, IV} {I, II, III} vs. {IV}
1 1 1 1
2 1 1 1
3 0 1 1
4 0 0 1
5 0 0 1
6 0 0 0
> ofmaxstat_trafo(ox, minprob = 0.49)
{I, II} vs. {III, IV}
1 1
2 1
3 NA
4 1
5 NA
6 0
7 0
8 NA
9 0
> ofmaxstat_trafo(ox[cc], minprob = 0.49)
{I, II} vs. {III, IV}
1 1
2 1
3 1
4 0
5 0
6 0
>
> mcp_trafo(x = "Tukey")(data.frame(x))
II - I III - I IV - I III - II IV - II IV - III
1 -1 -1 -1 0 0 0
2 -1 -1 -1 0 0 0
3 NA NA NA NA NA NA
4 1 0 0 -1 -1 0
5 NA NA NA NA NA NA
6 0 1 0 1 0 -1
7 0 1 0 1 0 -1
8 NA NA NA NA NA NA
9 0 0 1 0 1 1
attr(,"assign")
[1] 1 1 1 1 1 1
attr(,"contrast")
Multiple Comparisons of Means: Tukey Contrasts
I II III IV
II - I -1 1 0 0
III - I -1 0 1 0
IV - I -1 0 0 1
III - II 0 -1 1 0
IV - II 0 -1 0 1
IV - III 0 0 -1 1
> mcp_trafo(x = "Tukey")(data.frame(x = x[cc]))
II - I III - I IV - I III - II IV - II IV - III
1 -1 -1 -1 0 0 0
2 -1 -1 -1 0 0 0
3 1 0 0 -1 -1 0
4 0 1 0 1 0 -1
5 0 1 0 1 0 -1
6 0 0 1 0 1 1
attr(,"assign")
[1] 1 1 1 1 1 1
attr(,"contrast")
Multiple Comparisons of Means: Tukey Contrasts
I II III IV
II - I -1 1 0 0
III - I -1 0 1 0
IV - I -1 0 0 1
III - II 0 -1 1 0
IV - II 0 -1 0 1
IV - III 0 0 -1 1
>
> x[9] <- NA
> ox[9] <- NA
> cc <- complete.cases(x)
>
> f_trafo(x)
I II III
1 1 0 0
2 1 0 0
3 NA NA NA
4 0 1 0
5 NA NA NA
6 0 0 1
7 0 0 1
8 NA NA NA
9 NA NA NA
attr(,"assign")
[1] 1 1 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
> f_trafo(x[cc])
I II III
1 1 0 0
2 1 0 0
3 0 1 0
4 0 0 1
5 0 0 1
attr(,"assign")
[1] 1 1 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
>
> of_trafo(x)
[,1]
1 1
2 1
3 NA
4 2
5 NA
6 3
7 3
8 NA
9 NA
Warning message:
In of_trafo(x) : 'x' is not an ordered factor
> of_trafo(x[cc])
[,1]
1 1
2 1
3 2
4 3
5 3
Warning message:
In of_trafo(x[cc]) : 'x[cc]' is not an ordered factor
> of_trafo(x, scores = 5:8)
[,1]
1 5
2 5
3 NA
4 6
5 NA
6 7
7 7
8 NA
9 NA
Warning message:
In of_trafo(x, scores = 5:8) : 'x' is not an ordered factor
> of_trafo(x[cc], scores = 5:8)
[,1]
1 5
2 5
3 6
4 7
5 7
Warning message:
In of_trafo(x[cc], scores = 5:8) : 'x[cc]' is not an ordered factor
> of_trafo(x, scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 NA NA
4 6 10
5 NA NA
6 7 11
7 7 11
8 NA NA
9 NA NA
Warning message:
In of_trafo(x, scores = list(s1 = 5:8, s2 = 9:12)) :
'x' is not an ordered factor
> of_trafo(x[cc], scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 6 10
4 7 11
5 7 11
Warning message:
In of_trafo(x[cc], scores = list(s1 = 5:8, s2 = 9:12)) :
'x[cc]' is not an ordered factor
>
> zheng_trafo(x, increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.0
5 NA NA
6 0 0.5
7 0 0.5
8 NA NA
9 NA NA
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.5
5 NA NA
6 1 0.5
7 1 0.5
8 NA NA
9 NA NA
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 NA NA
4 0.5 1
5 NA NA
6 1.0 1
7 1.0 1
8 NA NA
9 NA NA
Warning message:
In zheng_trafo(x, increment = 0.5) : 'x' is not an ordered factor
> zheng_trafo(x[cc], increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.0
4 0 0.5
5 0 0.5
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.5
4 1 0.5
5 1 0.5
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 0.5 1
4 1.0 1
5 1.0 1
Warning message:
In zheng_trafo(x[cc], increment = 0.5) : 'x[cc]' is not an ordered factor
>
> of_trafo(ox)
[,1]
1 1
2 1
3 NA
4 2
5 NA
6 3
7 3
8 NA
9 NA
> of_trafo(ox[cc])
[,1]
1 1
2 1
3 2
4 3
5 3
> of_trafo(ox, scores = 5:8)
[,1]
1 5
2 5
3 NA
4 6
5 NA
6 7
7 7
8 NA
9 NA
> of_trafo(ox[cc], scores = 5:8)
[,1]
1 5
2 5
3 6
4 7
5 7
> of_trafo(ox, scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 NA NA
4 6 10
5 NA NA
6 7 11
7 7 11
8 NA NA
9 NA NA
> of_trafo(ox[cc], scores = list(s1 = 5:8, s2 = 9:12))
s1 s2
1 5 9
2 5 9
3 6 10
4 7 11
5 7 11
>
> zheng_trafo(ox, increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.0
5 NA NA
6 0 0.5
7 0 0.5
8 NA NA
9 NA NA
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 NA NA
4 0 0.5
5 NA NA
6 1 0.5
7 1 0.5
8 NA NA
9 NA NA
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 NA NA
4 0.5 1
5 NA NA
6 1.0 1
7 1.0 1
8 NA NA
9 NA NA
> zheng_trafo(ox[cc], increment = 0.5)
gamma = (0.0, 0.0, 0.0, 1.0) gamma = (0.0, 0.0, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.0
4 0 0.5
5 0 0.5
gamma = (0.0, 0.0, 1.0, 1.0) gamma = (0.0, 0.5, 0.5, 1.0)
1 0 0.0
2 0 0.0
3 0 0.5
4 1 0.5
5 1 0.5
gamma = (0.0, 0.5, 1.0, 1.0) gamma = (0.0, 1.0, 1.0, 1.0)
1 0.0 0
2 0.0 0
3 0.5 1
4 1.0 1
5 1.0 1
>
> fmaxstat_trafo(x)
{I} vs. {II, III} {I, II} vs. {III} {I, III} vs. {II}
1 1 1 1
2 1 1 1
3 NA NA NA
4 0 1 0
5 NA NA NA
6 0 0 1
7 0 0 1
8 NA NA NA
9 NA NA NA
> fmaxstat_trafo(x[cc])
{I} vs. {II, III} {I, II} vs. {III} {I, III} vs. {II}
1 1 1 1
2 1 1 1
3 0 1 0
4 0 0 1
5 0 0 1
> fmaxstat_trafo(x, minprob = 0.4, maxprob = 0.51)
{I} vs. {II, III}
1 1
2 1
3 NA
4 0
5 NA
6 0
7 0
8 NA
9 NA
> fmaxstat_trafo(x[cc], minprob = 0.4, maxprob = 0.51)
{I} vs. {II, III}
1 1
2 1
3 0
4 0
5 0
>
> ofmaxstat_trafo(ox)
{I} vs. {II, III} {I, II} vs. {III}
1 1 1
2 1 1
3 NA NA
4 0 1
5 NA NA
6 0 0
7 0 0
8 NA NA
9 NA NA
> ofmaxstat_trafo(ox[cc])
{I} vs. {II, III} {I, II} vs. {III}
1 1 1
2 1 1
3 0 1
4 0 0
5 0 0
> ofmaxstat_trafo(ox, minprob = 0.4, maxprob = 0.51)
{I} vs. {II, III}
1 1
2 1
3 NA
4 0
5 NA
6 0
7 0
8 NA
9 NA
> ofmaxstat_trafo(ox[cc], minprob = 0.4, maxprob = 0.51)
{I} vs. {II, III}
1 1
2 1
3 0
4 0
5 0
>
> mcp_trafo(x = "Tukey")(data.frame(x))
II - I III - I IV - I III - II IV - II IV - III
1 -1 -1 -1 0 0 0
2 -1 -1 -1 0 0 0
3 NA NA NA NA NA NA
4 1 0 0 -1 -1 0
5 NA NA NA NA NA NA
6 0 1 0 1 0 -1
7 0 1 0 1 0 -1
8 NA NA NA NA NA NA
9 NA NA NA NA NA NA
attr(,"assign")
[1] 1 1 1 1 1 1
attr(,"contrast")
Multiple Comparisons of Means: Tukey Contrasts
I II III IV
II - I -1 1 0 0
III - I -1 0 1 0
IV - I -1 0 0 1
III - II 0 -1 1 0
IV - II 0 -1 0 1
IV - III 0 0 -1 1
> mcp_trafo(x = "Tukey")(data.frame(x = x[cc]))
II - I III - I IV - I III - II IV - II IV - III
1 -1 -1 -1 0 0 0
2 -1 -1 -1 0 0 0
3 1 0 0 -1 -1 0
4 0 1 0 1 0 -1
5 0 1 0 1 0 -1
attr(,"assign")
[1] 1 1 1 1 1 1
attr(,"contrast")
Multiple Comparisons of Means: Tukey Contrasts
I II III IV
II - I -1 1 0 0
III - I -1 0 1 0
IV - I -1 0 0 1
III - II 0 -1 1 0
IV - II 0 -1 0 1
IV - III 0 0 -1 1
>
>
> ### Weighted logrank scores
> x <- c(1, 2, 3, 3, 3, 6, 6, 6, 9, 10)
> e <- c(1, 0, 1, 0, 1, 1, 0, 1, 0, 1)
>
> logrank_trafo(Surv(x, e))
[1] -0.90 0.10 -0.65 0.35 -0.65 -0.25 0.75 -0.25 0.75 0.75
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen")
[1] -0.9000000 0.1000000 -0.5666667 0.4333333 -0.5666667 0.1000000
[7] 1.1000000 0.1000000 1.1000000 1.1000000
> logrank_trafo(Surv(x, e), ties.method = "average-scores")
[1] -0.9000000 0.1000000 -0.7035714 0.3678571 -0.7035714 -0.3071429
[7] 0.8178571 -0.3071429 0.8178571 0.8178571
>
> logrank_trafo(Surv(x, e),
+ type = "Gehan-Breslow")
[1] -9 1 -5 3 -5 0 5 0 5 5
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Gehan-Breslow")
[1] -9 1 -3 3 -3 2 5 2 5 5
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Gehan-Breslow")
[1] -9 1 -5 3 -5 0 5 0 5 5
>
> logrank_trafo(Surv(x, e),
+ type = "Tarone-Ware")
[1] -2.8460499 0.3162278 -1.8050926 1.0233345 -1.8050926 -0.3183062
[7] 1.9177617 -0.3183062 1.9177617 1.9177617
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Tarone-Ware")
[1] -2.8460499 0.3162278 -1.3167654 1.1327243 -1.3167654 0.5553741
[7] 2.2874249 0.5553741 2.2874249 2.2874249
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Tarone-Ware")
[1] -2.8460499 0.3162278 -1.8783258 1.0477456 -1.8783258 -0.3730748
[7] 1.9949592 -0.3730748 1.9949592 1.9949592
>
> logrank_trafo(Surv(x, e),
+ type = "Prentice")
[1] -0.81818182 0.09090909 -0.45454545 0.27272727 -0.45454545 -0.03896104
[7] 0.48051948 -0.03896104 0.48051948 0.48051948
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Prentice")
[1] -0.81818182 0.09090909 -0.36363636 0.31818182 -0.36363636 0.18181818
[7] 0.59090909 0.18181818 0.59090909 0.59090909
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Prentice")
[1] -0.81818182 0.09090909 -0.51515152 0.29292929 -0.51515152 -0.06060606
[7] 0.52861953 -0.06060606 0.52861953 0.52861953
>
> logrank_trafo(Surv(x, e),
+ type = "Prentice-Marek")
[1] -0.81818182 0.09090909 -0.43939394 0.26767677 -0.43939394 -0.01515152
[7] 0.45622896 -0.01515152 0.45622896 0.45622896
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Prentice-Marek")
[1] -0.81818182 0.09090909 -0.34199134 0.30735931 -0.34199134 0.19913420
[7] 0.52380952 0.19913420 0.52380952 0.52380952
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Prentice-Marek")
[1] -0.81818182 0.09090909 -0.51515152 0.29292929 -0.51515152 -0.06060606
[7] 0.52861953 -0.06060606 0.52861953 0.52861953
>
> logrank_trafo(Surv(x, e),
+ type = "Andersen-Borgan-Gill-Keiding")
[1] -0.81818182 0.09090909 -0.51515152 0.29292929 -0.51515152 -0.06060606
[7] 0.52861953 -0.06060606 0.52861953 0.52861953
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Andersen-Borgan-Gill-Keiding")
[1] -0.81818182 0.09090909 -0.42857143 0.35064935 -0.42857143 0.18831169
[7] 0.67532468 0.18831169 0.67532468 0.67532468
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Andersen-Borgan-Gill-Keiding")
[1] -0.81818182 0.09090909 -0.51515152 0.29292929 -0.51515152 -0.06060606
[7] 0.52861953 -0.06060606 0.52861953 0.52861953
>
> logrank_trafo(Surv(x, e),
+ type = "Fleming-Harrington")
[1] -0.90 0.10 -0.65 0.35 -0.65 -0.25 0.75 -0.25 0.75 0.75
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Fleming-Harrington")
[1] -0.9000000 0.1000000 -0.5666667 0.4333333 -0.5666667 0.1000000
[7] 1.1000000 0.1000000 1.1000000 1.1000000
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Fleming-Harrington")
[1] -0.9000000 0.1000000 -0.7035714 0.3678571 -0.7035714 -0.3071429
[7] 0.8178571 -0.3071429 0.8178571 0.8178571
>
> logrank_trafo(Surv(x, e),
+ type = "Gaugler-Kim-Liao")
[1] -0.90 0.10 -0.65 0.35 -0.65 -0.25 0.75 -0.25 0.75 0.75
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Gaugler-Kim-Liao")
[1] -0.9000000 0.1000000 -0.5666667 0.4333333 -0.5666667 0.1000000
[7] 1.1000000 0.1000000 1.1000000 1.1000000
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Gaugler-Kim-Liao")
[1] -0.9000000 0.1000000 -0.7035714 0.3678571 -0.7035714 -0.3071429
[7] 0.8178571 -0.3071429 0.8178571 0.8178571
>
> logrank_trafo(Surv(x, e),
+ type = "Self")
[1] -0.90 0.10 -0.65 0.35 -0.65 -0.25 0.75 -0.25 0.75 0.75
> logrank_trafo(Surv(x, e), ties.method = "Hothorn-Lausen",
+ type = "Self")
[1] -0.9000000 0.1000000 -0.5666667 0.4333333 -0.5666667 0.1000000
[7] 1.1000000 0.1000000 1.1000000 1.1000000
> logrank_trafo(Surv(x, e), ties.method = "average-scores",
+ type = "Self")
[1] -0.9000000 0.1000000 -0.7035714 0.3678571 -0.7035714 -0.3071429
[7] 0.8178571 -0.3071429 0.8178571 0.8178571
>
> proc.time()
user system elapsed
0.98 0.07 1.00
Generated by dwww version 1.15 on Sat May 18 13:03:45 CEST 2024.