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R Under development (unstable) (2026-05-22 r90067) -- "Unsuffered Consequences"Copyright (C) 2026 The R Foundation for Statistical ComputingPlatform: x86_64-pc-linux-gnuR 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.> #### Some examples of the KS and Wilcoxon tests>> ### ------ Kolmogorov Smirnov (KS) -------------->> ## unrealistic one of PR#14561> ds1 <- c(1.7,2,3,3,4,4,5,5,6,6)> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216)Asymptotic one-sample Kolmogorov-Smirnov testdata: ds1D = 0.274, p-value = 0.4407alternative hypothesis: two-sidedWarning message:In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216) :ties should not be present for the one-sample Kolmogorov-Smirnov test> # how on earth can sigma = 1.55216 be known?>> # R >= 2.14.0 allows the equally invalid> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE)Exact one-sample Kolmogorov-Smirnov testdata: ds1D = 0.274, p-value = 0.3715alternative hypothesis: two-sidedWarning message:In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE) :ties should not be present for the one-sample Kolmogorov-Smirnov test>> ## Try out the effects of rounding> set.seed(123)> ds2 <- rnorm(1000)> ks.test(ds2, "pnorm") # exact = FALSE is default for n = 1000Asymptotic one-sample Kolmogorov-Smirnov testdata: ds2D = 0.019416, p-value = 0.8452alternative hypothesis: two-sided> ks.test(ds2, "pnorm", exact = TRUE)Exact one-sample Kolmogorov-Smirnov testdata: ds2D = 0.019416, p-value = 0.8379alternative hypothesis: two-sided> ## next two are still close> ks.test(round(ds2, 2), "pnorm")Asymptotic one-sample Kolmogorov-Smirnov testdata: round(ds2, 2)D = 0.019169, p-value = 0.856alternative hypothesis: two-sidedWarning message:In ks.test.default(round(ds2, 2), "pnorm") :ties should not be present for the one-sample Kolmogorov-Smirnov test> ks.test(round(ds2, 2), "pnorm", exact = TRUE)Exact one-sample Kolmogorov-Smirnov testdata: round(ds2, 2)D = 0.019169, p-value = 0.8489alternative hypothesis: two-sidedWarning message:In ks.test.default(round(ds2, 2), "pnorm", exact = TRUE) :ties should not be present for the one-sample Kolmogorov-Smirnov test> # now D has doubled, but p-values remain similar (if very different from ds2)> ks.test(round(ds2, 1), "pnorm")Asymptotic one-sample Kolmogorov-Smirnov testdata: round(ds2, 1)D = 0.03674, p-value = 0.1344alternative hypothesis: two-sidedWarning message:In ks.test.default(round(ds2, 1), "pnorm") :ties should not be present for the one-sample Kolmogorov-Smirnov test> ks.test(round(ds2, 1), "pnorm", exact = TRUE)Exact one-sample Kolmogorov-Smirnov testdata: round(ds2, 1)D = 0.03674, p-value = 0.1311alternative hypothesis: two-sidedWarning message:In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :ties should not be present for the one-sample Kolmogorov-Smirnov test> summary(w1 <- warnings())1 identical warnings:In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :ties should not be present for the one-sample Kolmogorov-Smirnov test>>> ### ------ Wilcoxon (Mann Whitney) -------------->> options(nwarnings = 1000)> (alts <- setNames(, eval(formals(stats:::wilcox.test.default)$alternative)))two.sided less greater"two.sided" "less" "greater"> x0 <- setNames(0:4, paste0("n_", 1L + 0:4))> str(x.set <- list(s0 = lapply(x0, function(m) 0:m),+ s. = lapply(x0, function(m) c(1e-9, seq_len(m)))))List of 2$ s0:List of 5..$ n_1: int 0..$ n_2: int [1:2] 0 1..$ n_3: int [1:3] 0 1 2..$ n_4: int [1:4] 0 1 2 3..$ n_5: int [1:5] 0 1 2 3 4$ s.:List of 5..$ n_1: num 1e-09..$ n_2: num [1:2] 1e-09 1e+00..$ n_3: num [1:3] 1e-09 1e+00 2e+00..$ n_4: num [1:4] 1e-09 1e+00 2e+00 3e+00..$ n_5: num [1:5] 1e-09 1e+00 2e+00 3e+00 4e+00> stats <- setNames(nm = c("statistic", "p.value", "conf.int", "estimate"))> dig.r <- c(Inf, 20, 15, 10, 7, 5); names(dig.r) <- paste0("d.rank=", dig.r)> RL <- lapply(dig.r, function(dig.rank)+ lapply(x.set, ## for all data sets+ function(xs)+ lapply(alts, ## for all three alternatives+ function(alt)+ lapply(xs, function(x)+ ## try( nolonger: Even with conf.int = TRUE, do not want errors :+ wilcox.test(x, exact=TRUE, conf.int=TRUE, alternative = alt,+ digits.rank = dig.rank)+ ## )+ ))))> length(ww <- warnings()) # 1 ; was 52 (or 43 for x0 <- 0:3)[1] 1> if(!identical(w1, ww)) # typically they *are* identical ==> no new warnings+ summary(ww)> ## unique(ww) # (were 4 different ones)>> ## RL is very nested tree of "htest" results> str(RL, max.level = 2)List of 6$ d.rank=Inf:List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=20 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=15 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=10 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=7 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=5 :List of 2..$ s0:List of 3..$ s.:List of 3> str(RL[["d.rank=7"]]$s.$two.sided, max.level = 1) # 5 x {list of 9, class "htest"}List of 5$ n_1:List of 9..- attr(*, "class")= chr "htest"$ n_2:List of 9..- attr(*, "class")= chr "htest"$ n_3:List of 9..- attr(*, "class")= chr "htest"$ n_4:List of 9..- attr(*, "class")= chr "htest"$ n_5:List of 9..- attr(*, "class")= chr "htest"> str(RL[["d.rank=7"]]$s.$two.sided [1:2], give.attr = FALSE)List of 2$ n_1:List of 9..$ statistic : Named num 1..$ parameter : NULL..$ p.value : num 1..$ null.value : Named num 0..$ alternative: chr "two.sided"..$ method : chr "Wilcoxon signed rank exact test"..$ data.name : chr "x"..$ conf.int : num [1:2] -Inf Inf..$ estimate : Named num 1e-09$ n_2:List of 9..$ statistic : Named num 2..$ parameter : NULL..$ p.value : num 1..$ null.value : Named num 0..$ alternative: chr "two.sided"..$ method : chr "Wilcoxon signed rank exact test"..$ data.name : chr "x"..$ conf.int : num [1:2] 1e-09 Inf..$ estimate : Named num 0.5>> cc <- lapply(RL, function(A) lapply(A, function(B) lapply(B, function(bb) lapply(bb, class))))> ##N table(unlist(cc))> ## in R <= 3.3.1, with try( .. ) above and only digits.rank=Inf, we got> ## htest try-error> ## 23 7> stopifnot(identical("htest", unique(unlist(cc))))> ##N uc <- unlist(cc[["s0"]]); noquote(names(uc)[uc != "htest"]) ## these 7 cases :> ## two.sided1 two.sided2 two.sided3> ## less1 less2> ## greater1 greater2>> ##--- How close are the stats of (0:m) to those of (eps, 1:m) ------------>> ## Contained errors in older versions:> stR <- lapply(stats, function(COMP)+ lapply(RL, function(A)+ lapply(A, function(B)+ lapply(B, function(bb)+ lapply(bb, `[[`, COMP) ))))> if(interactive()) {+ str(stR, max.level = 2)+ str(stR$p.value$`d.rank=7`, give.attr = FALSE)+ }>> ## short cut & speedup:> unList <- function(x) .Internal(unlist(x, FALSE, TRUE))> ## recurs. use.nms> ## a) P-value s> pv <- stR$p.value>> str(apv <- simplify2array(+ lapply(pv, \(L0) simplify2array(+ lapply(L0, \(L) simplify2array(lapply(L, unList))))))+ )num [1:5, 1:3, 1:2, 1:6] 1 1 0.5 0.25 0.125 1 1 1 1 1 ...- attr(*, "dimnames")=List of 4..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" .....$ : chr [1:3] "two.sided" "less" "greater"..$ : chr [1:2] "s0" "s."..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...> ## num [1:5, 1:3, 1:2, 1:6] 1 1 0.5 0.25 0.125 ...> ## - attr(*, "dimnames")=List of 4> ## ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...> ## ..$ : chr [1:3] "two.sided" "less" "greater"> ## ..$ : chr [1:2] "s0" "s."> ## ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...> object.size(apv) # 3144 bytes3144 bytes> object.size(dpv <- as.data.frame.table(apv)) # 8176 bytes8176 bytes> str(dpv)'data.frame': 180 obs. of 5 variables:$ Var1: Factor w/ 5 levels "n_1","n_2","n_3",..: 1 2 3 4 5 1 2 3 4 5 ...$ Var2: Factor w/ 3 levels "two.sided","less",..: 1 1 1 1 1 2 2 2 2 2 ...$ Var3: Factor w/ 2 levels "s0","s.": 1 1 1 1 1 1 1 1 1 1 ...$ Var4: Factor w/ 6 levels "d.rank=Inf","d.rank=20",..: 1 1 1 1 1 1 1 1 1 1 ...$ Freq: num 1 1 0.5 0.25 0.125 1 1 1 1 1 ...> str(ps. <- apv[,,"s.",])num [1:5, 1:3, 1:6] 1 0.5 0.25 0.125 0.0625 1 1 1 1 1 ...- attr(*, "dimnames")=List of 3..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" .....$ : chr [1:3] "two.sided" "less" "greater"..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...> str(ps0 <- apv[,,"s0",])num [1:5, 1:3, 1:6] 1 1 0.5 0.25 0.125 1 1 1 1 1 ...- attr(*, "dimnames")=List of 3..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" .....$ : chr [1:3] "two.sided" "less" "greater"..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...> (epsC <- .Machine$double.eps) # 2.2e-16[1] 2.220446e-16> allEQ <- function(x,y) all.equal(x, y, tolerance = 8*epsC)> stopifnot(exprs = {+ 0 <= unlist(apv) ; unlist(apv) <= 1 # (no NA, ..)+ ## ps0 does *not* vary with digits.rank :+ identical(dim(print(ups0 <- drop(unique(ps0, MARGIN = 3)))), c(5L, 3L))+ is.function(uNam <- function(x) `names<-`(x, NULL))+ allEQ(uNam(ups0[,"two.sided"]), c(1, 2^(0:-3)))+ allEQ(uNam(ups0[, "greater" ]), 2^(0:-4))+ allEQ(uNam(ups0[, "less" ]), c(1,1,1,1,1))+ ## NB: This has changed from R 4.6.0 (now using digits.zap = digits.rank):+ ## ps., eps=1e-9: two possible results, boundary for 1e-9 between digits.rank = 7 and 10+ identical(dim(print(ups. <- unique(ps., MARGIN = 3))), c(5L, 3L, 2L))+ allEQ(ps.[,,"d.rank=Inf"], ps.[,,"d.rank=10"])+ is.function(uNam <- function(x) `dimnames<-`(x, NULL))+ allEQ(uNam(ups.[,"two.sided", ]), 2^cbind(0:-4, c(0L,0:-3)))+ allEQ(uNam(ups.[, "greater" , ]), cbind(2^-(1:5), 2^-c(1,1:4)))+ allEQ(uNam(ups.[, "less" , ]), cbind(1, rep(1, 5)))+ })two.sided less greatern_1 1.000 1 1.0000n_2 1.000 1 0.5000n_3 0.500 1 0.2500n_4 0.250 1 0.1250n_5 0.125 1 0.0625, , d.rank=Inftwo.sided less greatern_1 1.0000 1 0.50000n_2 0.5000 1 0.25000n_3 0.2500 1 0.12500n_4 0.1250 1 0.06250n_5 0.0625 1 0.03125, , d.rank=7two.sided less greatern_1 1.000 1 0.5000n_2 1.000 1 0.5000n_3 0.500 1 0.2500n_4 0.250 1 0.1250n_5 0.125 1 0.0625>>> ## b) Statistic - Estimate>> (astat <- simplify2array(+ lapply(stR[["statistic"]],+ \(L0) simplify2array(+ lapply(L0, \(L) simplify2array(sapply(L, unList))))))), , s0, d.rank=Inftwo.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=Inftwo.sided less greatern_1.V 1 1 1n_2.V 3 3 3n_3.V 6 6 6n_4.V 10 10 10n_5.V 15 15 15, , s0, d.rank=20two.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=20two.sided less greatern_1.V 1 1 1n_2.V 3 3 3n_3.V 6 6 6n_4.V 10 10 10n_5.V 15 15 15, , s0, d.rank=15two.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=15two.sided less greatern_1.V 1 1 1n_2.V 3 3 3n_3.V 6 6 6n_4.V 10 10 10n_5.V 15 15 15, , s0, d.rank=10two.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=10two.sided less greatern_1.V 1 1 1n_2.V 3 3 3n_3.V 6 6 6n_4.V 10 10 10n_5.V 15 15 15, , s0, d.rank=7two.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=7two.sided less greatern_1.V 1 1 1n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s0, d.rank=5two.sided less greatern_1.V 0 0 0n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14, , s., d.rank=5two.sided less greatern_1.V 1 1 1n_2.V 2 2 2n_3.V 5 5 5n_4.V 9 9 9n_5.V 14 14 14>> (aEst <- simplify2array(+ lapply(stR[["estimate"]],+ \(L0) simplify2array(+ lapply(L0, \(L) simplify2array(lapply(L, unList))))))), , s0, d.rank=Inftwo.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=Inftwo.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00, , s0, d.rank=20two.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=20two.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00, , s0, d.rank=15two.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=15two.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00, , s0, d.rank=10two.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=10two.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00, , s0, d.rank=7two.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=7two.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00, , s0, d.rank=5two.sided less greatern_1.(pseudo)median 0.0 0.0 0.0n_2.(pseudo)median 0.5 0.5 0.5n_3.(pseudo)median 1.0 1.0 1.0n_4.(pseudo)median 1.5 1.5 1.5n_5.(pseudo)median 2.0 2.0 2.0, , s., d.rank=5two.sided less greatern_1.(pseudo)median 1.0e-09 1.0e-09 1.0e-09n_2.(pseudo)median 5.0e-01 5.0e-01 5.0e-01n_3.(pseudo)median 1.0e+00 1.0e+00 1.0e+00n_4.(pseudo)median 1.5e+00 1.5e+00 1.5e+00n_5.(pseudo)median 2.0e+00 2.0e+00 2.0e+00> str(aEst)num [1:5, 1:3, 1:2, 1:6] 0 0.5 1 1.5 2 0 0.5 1 1.5 2 ...- attr(*, "dimnames")=List of 4..$ : chr [1:5] "n_1.(pseudo)median" "n_2.(pseudo)median" "n_3.(pseudo)median" "n_4.(pseudo)median" .....$ : chr [1:3] "two.sided" "less" "greater"..$ : chr [1:2] "s0" "s."..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...> ## num [1:5, 1:3, 1:2, 1:6] 0 0.5 1 1.5 2 0 0.5 1 1.5 2 ...> ## - attr(*, "dimnames")=List of 4> ## ..$ : chr [1:5] "n_1.(pseudo)median" "n_2.(pseudo)median" "n_3.(pseudo)median" "n_4.(pseudo)median" ...> ## ..$ : chr [1:3] "two.sided" "less" "greater"> ## ..$ : chr [1:2] "s0" "s."> ## ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...>> ## Confidence Interval :> ## c) C.I. extraction> CI <- stR[["conf.int"]]> str(CI, max.level = 2)List of 6$ d.rank=Inf:List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=20 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=15 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=10 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=7 :List of 2..$ s0:List of 3..$ s.:List of 3$ d.rank=5 :List of 2..$ s0:List of 3..$ s.:List of 3>> if(FALSE) { # printing chCI below is much nicer+ str(aCI <- simplify2array(lapply(CI,+ function(A) simplify2array(lapply(A,+ function(B) simplify2array(lapply(B,+ function(C) t(simplify2array(C)))))))))+ ## num [1:5, 1:2, 1:3, 1:2, 1:6] 0 0 0 0 0 ...+ ## - attr(*, "dimnames")=List of 5+ ## ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...+ ## ..$ : NULL+ ## ..$ : chr [1:3] "two.sided" "less" "greater"+ ## ..$ : chr [1:2] "s0" "s."+ ## ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...+ aCI # has *lost* the conf.level which is varying !!+ }>> ## d) confidence interval __printing__> formatCI <- function(ci)+ sprintf("[%g, %g] (%g%%)", ci[[1]], ci[[2]],+ round(100*attr(ci,"conf.level")))> nx <- length(x0)> Fval <- array("", dim = c(nx, length(alts), length(x.set)))> (chCI <- noquote(vapply(CI, function(D)+ vapply(D, function(ss)+ vapply(ss, function(alt) vapply(alt, formatCI, ""), character(nx)),+ Fval[,,1]),+ Fval)+ )), , s0, d.rank=Inftwo.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=Inftwo.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%) [1e-09, Inf] (97%), , s0, d.rank=20two.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=20two.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%) [1e-09, Inf] (97%), , s0, d.rank=15two.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=15two.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%) [1e-09, Inf] (97%), , s0, d.rank=10two.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=10two.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%) [1e-09, Inf] (97%), , s0, d.rank=7two.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=7two.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_3 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_4 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_5 [1e-09, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s0, d.rank=5two.sided less greatern_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)n_5 [0, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%), , s., d.rank=5two.sided less greatern_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)n_2 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_3 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_4 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)n_5 [1e-09, Inf] (100%) [-Inf, 4] (100%) [0.5, Inf] (97%)>>> ##-------- 2-sample tests (working unchanged) ------------------>> R2 <- lapply(alts, ## for all three alternatives+ function(alt)+ lapply(seq_along(x0), function(k)+ wilcox.test(x = x.set$s0[[k]], y = x.set$s.[[k]],+ exact=TRUE, conf.int=TRUE, alternative = alt)))> length(w2 <- warnings()) # 1 (was '27')[1] 1> if(!identical(w2, w1)) # typically they *are* identical ==> no new warnings+ summary(w2)>>> table(uc2 <- unlist(c2 <- lapply(R2, function(A) lapply(A, class))))htest15> stopifnot(uc2 == "htest")>> stR2 <- lapply(stats,+ function(COMP)+ lapply(R2, function(A) lapply(A, `[[`, COMP)))>> lapply(stats[-3], ## -3: "conf.int" separately+ function(ST) sapply(stR2[[ST]], unlist))$statistictwo.sided less greaterW 0.0 0.0 0.0W 1.5 1.5 1.5W 4.0 4.0 4.0W 7.5 7.5 7.5W 12.0 12.0 12.0$p.valuetwo.sided less greater[1,] 1.0000000 0.5000000 1.0000000[2,] 1.0000000 0.5000000 0.8333333[3,] 1.0000000 0.5000000 0.7000000[4,] 0.9714286 0.4857143 0.6285714[5,] 0.9682540 0.4841270 0.5873016$estimatetwo.sided less greaterdifference in location -1e-09 -1e-09 -1e-09difference in location -5e-10 -5e-10 -5e-10difference in location 0e+00 0e+00 0e+00difference in location 0e+00 0e+00 0e+00difference in location 0e+00 0e+00 0e+00>> noquote(sapply(stR2[["conf.int"]], function(.) vapply(., formatCI, "")))two.sided less greater[1,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)[2,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)[3,] [-Inf, Inf] (100%) [-Inf, 2] (100%) [-2, Inf] (100%)[4,] [-3, 3] (100%) [-Inf, 2] (97%) [-2, Inf] (97%)[5,] [-3, 3] (96%) [-Inf, 2] (95%) [-2, Inf] (97%)>>> proc.time()user system elapsed0.544 0.273 0.981