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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
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