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R Under development (unstable) (2026-03-05 r89546) -- "Unsuffered Consequences"
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R Under development (unstable) (2026-05-22 r90067) -- "Unsuffered Consequences"
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Copyright (C) 2026 The R Foundation for Statistical Computing
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Copyright (C) 2026 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu
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Platform: x86_64-pc-linux-gnu
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R is free software and comes with ABSOLUTELY NO WARRANTY.
 6 
R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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You are welcome to redistribute it under certain conditions.
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Type 'demo()' for some demos, 'help()' for on-line help, or
 14 
Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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Type 'q()' to quit R.
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18 
> #### Some examples of the KS and Wilcoxon tests
 18 
> #### Some examples of the KS and Wilcoxon tests
19 
>
19 
>
20 
> ### ------ Kolmogorov Smirnov (KS) --------------
 20 
> ### ------ Kolmogorov Smirnov (KS) --------------
21 
>
21 
>
22 
> ## unrealistic one of PR#14561
 22 
> ## unrealistic one of PR#14561
23 
> ds1 <- c(1.7,2,3,3,4,4,5,5,6,6)
 23 
> ds1 <- c(1.7,2,3,3,4,4,5,5,6,6)
24 
> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216)
 24 
> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216)
25 
 
 25 
 
26 
	Asymptotic one-sample Kolmogorov-Smirnov test
 26 
	Asymptotic one-sample Kolmogorov-Smirnov test
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 31 
 
32 
Warning message:
 32 
Warning message:
33 
In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216) :
 33 
In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216) :
34 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
 34 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
35 
> # how on earth can sigma = 1.55216 be known?
 35 
> # how on earth can sigma = 1.55216 be known?
36 
>
36 
>
37 
> # R >= 2.14.0 allows the equally invalid
 37 
> # R >= 2.14.0 allows the equally invalid
38 
> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE)
 38 
> ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE)
39 
 
 39 
 
40 
	Exact one-sample Kolmogorov-Smirnov test
 40 
	Exact one-sample Kolmogorov-Smirnov test
41 
 
 41 
 
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44 
alternative hypothesis: two-sided
 44 
alternative hypothesis: two-sided
45 
 
 45 
 
46 
Warning message:
 46 
Warning message:
47 
In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE) :
 47 
In ks.test.default(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE) :
48 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
 48 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
49 
>
49 
>
50 
> ## Try out the effects of rounding
 50 
> ## Try out the effects of rounding
51 
> set.seed(123)
 51 
> set.seed(123)
52 
> ds2 <- rnorm(1000)
 52 
> ds2 <- rnorm(1000)
53 
> ks.test(ds2, "pnorm") # exact = FALSE is default for n = 1000
 53 
> ks.test(ds2, "pnorm") # exact = FALSE is default for n = 1000
54 
 
 54 
 
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110 
alternative hypothesis: two-sided
 110 
alternative hypothesis: two-sided
111 
 
 111 
 
112 
Warning message:
 112 
Warning message:
113 
In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :
 113 
In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :
114 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
 114 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
- 
 
 115 
> summary(w1 <- warnings())
- 
 
 116 
1 identical warnings:
- 
 
 117 
In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :
- 
 
 118 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
115 
>
119 
>
116 
>
120 
>
117 
> ### ------ Wilkoxon (Mann Whitney) --------------
 121 
> ### ------ Wilcoxon (Mann Whitney) --------------
118 
>
122 
>
119 
> options(nwarnings = 1000)
 123 
> options(nwarnings = 1000)
120 
> (alts <- setNames(, eval(formals(stats:::wilcox.test.default)$alternative)))
 124 
> (alts <- setNames(, eval(formals(stats:::wilcox.test.default)$alternative)))
121 
  two.sided        less     greater
125 
  two.sided        less     greater
122 
"two.sided"      "less"   "greater"
126 
"two.sided"      "less"   "greater"
123 
> x0 <- 0:4
 127 
> x0 <- setNames(0:4, paste0("n_", 1L + 0:4))
124 
> (x.set <- list(s0 = lapply(x0, function(m) 0:m),
 128 
> str(x.set <- list(s0 = lapply(x0, function(m) 0:m),
125 
+                s. = lapply(x0, function(m) c(1e-9, seq_len(m)))))
 129 
+                   s. = lapply(x0, function(m) c(1e-9, seq_len(m)))))
126 
$s0
 - 
 
127 
$s0[[1]]
 130 
List of 2
128 
[1] 0
 - 
 
129 
 
 - 
 
130 
$s0[[2]]
 131 
 $ s0:List of 5
131 
[1] 0 1
 132 
  ..$ n_1: int 0
132 
 
 - 
 
133 
$s0[[3]]
 133 
  ..$ n_2: int [1:2] 0 1
134 
[1] 0 1 2
 134 
  ..$ n_3: int [1:3] 0 1 2
135 
 
 - 
 
136 
$s0[[4]]
 - 
 
137 
[1] 0 1 2 3
 135 
  ..$ n_4: int [1:4] 0 1 2 3
138 
 
 - 
 
139 
$s0[[5]]
 - 
 
140 
[1] 0 1 2 3 4
 136 
  ..$ n_5: int [1:5] 0 1 2 3 4
141 
 
 - 
 
142 
 
 - 
 
143 
$s.
 - 
 
144 
$s.[[1]]
 137 
 $ s.:List of 5
145 
[1] 1e-09
 138 
  ..$ n_1: num 1e-09
146 
 
 - 
 
147 
$s.[[2]]
 - 
 
148 
[1] 1e-09 1e+00
 139 
  ..$ n_2: num [1:2] 1e-09 1e+00
149 
 
 - 
 
150 
$s.[[3]]
 - 
 
151 
[1] 1e-09 1e+00 2e+00
 140 
  ..$ n_3: num [1:3] 1e-09 1e+00 2e+00
152 
 
 - 
 
153 
$s.[[4]]
 - 
 
154 
[1] 1e-09 1e+00 2e+00 3e+00
 141 
  ..$ n_4: num [1:4] 1e-09 1e+00 2e+00 3e+00
155 
 
 - 
 
156 
$s.[[5]]
 - 
 
157 
[1] 1e-09 1e+00 2e+00 3e+00 4e+00
 142 
  ..$ n_5: num [1:5] 1e-09 1e+00 2e+00 3e+00 4e+00
158 
 
 - 
 
159 
 
 - 
 
160 
> stats <- setNames(nm = c("statistic", "p.value", "conf.int", "estimate"))
 143 
> stats <- setNames(nm = c("statistic", "p.value", "conf.int", "estimate"))
161 
>
- 
 
162 
> ## Even with  conf.int = TRUE, do not want errors :
 144 
> dig.r <- c(Inf, 20, 15, 10, 7, 5); names(dig.r) <- paste0("d.rank=", dig.r)
163 
> RR <-
 145 
> RL <- lapply(dig.r, function(dig.rank)
164 
+     lapply(x.set, ## for all data sets
 146 
+              lapply(x.set, ## for all data sets
165 
+            function(xs)
 147 
+                     function(xs)
166 
+                lapply(alts, ## for all three alternatives
 148 
+                         lapply(alts, ## for all three alternatives
167 
+                       function(alt)
 149 
+                                function(alt)
168 
+                           lapply(xs, function(x)
 150 
+                                    lapply(xs, function(x)
169 
+                               ## try(
 151 
+                                        ## try( nolonger: Even with  conf.int = TRUE, do not want errors :
170 
+                               wilcox.test(x, exact=TRUE, conf.int=TRUE, alternative = alt)
 152 
+                                        wilcox.test(x, exact=TRUE, conf.int=TRUE, alternative = alt,
- 
 
 153 
+                                                    digits.rank = dig.rank)
171 
+                               ## )
 154 
+                                        ## )
172 
+                               )))
 155 
+                                        ))))
173 
> length(ww <- warnings()) # 52 (or 43 for x0 <- 0:3)
 156 
> length(ww <- warnings()) # 1 ; was 52 (or 43 for x0 <- 0:3)
174 
[1] 1
 157 
[1] 1
- 
 
 158 
> if(!identical(w1, ww)) # typically they *are* identical ==> no new warnings
- 
 
 159 
+     summary(ww)
175 
> unique(ww) # 4 different ones
 160 
> ## unique(ww) # (were 4 different ones)
- 
 
 161 
>
- 
 
 162 
> ## RL is very nested tree of "htest" results
- 
 
 163 
> str(RL, max.level = 2)
- 
 
 164 
List of 6
- 
 
 165 
 $ d.rank=Inf:List of 2
- 
 
 166 
  ..$ s0:List of 3
- 
 
 167 
  ..$ s.:List of 3
- 
 
 168 
 $ d.rank=20 :List of 2
- 
 
 169 
  ..$ s0:List of 3
- 
 
 170 
  ..$ s.:List of 3
- 
 
 171 
 $ d.rank=15 :List of 2
- 
 
 172 
  ..$ s0:List of 3
- 
 
 173 
  ..$ s.:List of 3
- 
 
 174 
 $ d.rank=10 :List of 2
- 
 
 175 
  ..$ s0:List of 3
- 
 
 176 
  ..$ s.:List of 3
- 
 
 177 
 $ d.rank=7  :List of 2
- 
 
 178 
  ..$ s0:List of 3
- 
 
 179 
  ..$ s.:List of 3
- 
 
 180 
 $ d.rank=5  :List of 2
- 
 
 181 
  ..$ s0:List of 3
- 
 
 182 
  ..$ s.:List of 3
- 
 
 183 
> str(RL[["d.rank=7"]]$s.$two.sided, max.level = 1) # 5 x {list of 9, class "htest"}
- 
 
 184 
List of 5
- 
 
 185 
 $ n_1:List of 9
- 
 
 186 
  ..- attr(*, "class")= chr "htest"
- 
 
 187 
 $ n_2:List of 9
- 
 
 188 
  ..- attr(*, "class")= chr "htest"
- 
 
 189 
 $ n_3:List of 9
- 
 
 190 
  ..- attr(*, "class")= chr "htest"
- 
 
 191 
 $ n_4:List of 9
- 
 
 192 
  ..- attr(*, "class")= chr "htest"
176 
Warning message:
 193 
 $ n_5:List of 9
- 
 
 194 
  ..- attr(*, "class")= chr "htest"
177 
In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :
 195 
> str(RL[["d.rank=7"]]$s.$two.sided [1:2], give.attr = FALSE)
- 
 
 196 
List of 2
- 
 
 197 
 $ n_1:List of 9
- 
 
 198 
  ..$ statistic  : Named num 1
- 
 
 199 
  ..$ parameter  : NULL
- 
 
 200 
  ..$ p.value    : num 1
- 
 
 201 
  ..$ null.value : Named num 0
- 
 
 202 
  ..$ alternative: chr "two.sided"
- 
 
 203 
  ..$ method     : chr "Wilcoxon signed rank exact test"
- 
 
 204 
  ..$ data.name  : chr "x"
- 
 
 205 
  ..$ conf.int   : num [1:2] -Inf Inf
- 
 
 206 
  ..$ estimate   : Named num 1e-09
- 
 
 207 
 $ n_2:List of 9
- 
 
 208 
  ..$ statistic  : Named num 2
- 
 
 209 
  ..$ parameter  : NULL
- 
 
 210 
  ..$ p.value    : num 1
- 
 
 211 
  ..$ null.value : Named num 0
- 
 
 212 
  ..$ alternative: chr "two.sided"
178 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
 213 
  ..$ method     : chr "Wilcoxon signed rank exact test"
- 
 
 214 
  ..$ data.name  : chr "x"
- 
 
 215 
  ..$ conf.int   : num [1:2] 1e-09 Inf
- 
 
 216 
  ..$ estimate   : Named num 0.5
179 
>
217 
>
180 
> cc <- lapply(RR, function(A) lapply(A, function(bb) lapply(bb, class)))
 218 
> cc <- lapply(RL, function(A) lapply(A, function(B) lapply(B, function(bb) lapply(bb, class))))
181 
> table(unlist(cc))
 219 
> ##N table(unlist(cc))
182 
 
 - 
 
183 
htest
- 
 
184 
   30
- 
 
185 
> ## in R <= 3.3.1,  with try( .. ) above, we got
 220 
> ## in R <= 3.3.1,  with try( .. ) above and only digits.rank=Inf, we got
186 
> ## htest try-error
 221 
> ## htest try-error
187 
> ##    23         7
 222 
> ##    23         7
- 
 
 223 
> stopifnot(identical("htest", unique(unlist(cc))))
188 
> uc <- unlist(cc[["s0"]]); noquote(names(uc)[uc != "htest"]) ## these 7 cases :
 224 
> ##N uc <- unlist(cc[["s0"]]); noquote(names(uc)[uc != "htest"]) ## these 7 cases :
189 
character(0)
 - 
 
190 
> ## two.sided1 two.sided2 two.sided3
 225 
> ## two.sided1 two.sided2 two.sided3
191 
> ## less1      less2
 226 
> ## less1      less2
192 
> ## greater1   greater2
 227 
> ## greater1   greater2
193 
>
228 
>
194 
> ##--- How close are the stats of  (0:m)  to those of  (eps, 1:m) ------------
 229 
> ##--- How close are the stats of  (0:m)  to those of  (eps, 1:m) ------------
195 
>
230 
>
196 
> ## a version that still works with above try(.) and errors there:
 231 
> ## Contained errors in older versions:
197 
> getC <- function(L, C) if(inherits(L,"try-error")) c(L) else L[[C]]
 - 
 
198 
> stR <- lapply(stats, function(COMP)
 232 
> stR <- lapply(stats, function(COMP)
199 
+            lapply(RR, function(A)
 233 
+            lapply(RL, function(A)
200 
+                lapply(A, function(bb)
 234 
+                lapply(A, function(B)
- 
 
 235 
+                    lapply(B, function(bb)
201 
+                    lapply(bb, getC, C=COMP) )))
 236 
+                        lapply(bb, `[[`, COMP) ))))
- 
 
 237 
> if(interactive()) {
- 
 
 238 
+     str(stR, max.level = 2)
- 
 
 239 
+     str(stR$p.value$`d.rank=7`, give.attr = FALSE)
- 
 
 240 
+ }
- 
 
 241 
>
- 
 
 242 
> ## short cut & speedup:
- 
 
 243 
> unList <- function(x) .Internal(unlist(x, FALSE,  TRUE))
- 
 
 244 
> ##                                        recurs. use.nms
- 
 
 245 
> ## a) P-value s
- 
 
 246 
> pv <- stR$p.value
- 
 
 247 
>
- 
 
 248 
> str(apv <- simplify2array(
- 
 
 249 
+         lapply(pv, \(L0) simplify2array(
- 
 
 250 
+                              lapply(L0, \(L) simplify2array(lapply(L, unList))))))
- 
 
 251 
+     )
- 
 
 252 
 num [1:5, 1:3, 1:2, 1:6] 1 1 0.5 0.25 0.125 1 1 1 1 1 ...
- 
 
 253 
 - attr(*, "dimnames")=List of 4
- 
 
 254 
  ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...
- 
 
 255 
  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 256 
  ..$ : chr [1:2] "s0" "s."
- 
 
 257 
  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 258 
> ## num [1:5, 1:3, 1:2, 1:6] 1 1 0.5 0.25 0.125 ...
- 
 
 259 
> ## - attr(*, "dimnames")=List of 4
- 
 
 260 
> ##  ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...
- 
 
 261 
> ##  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 262 
> ##  ..$ : chr [1:2] "s0" "s."
- 
 
 263 
> ##  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 264 
> object.size(apv) 				# 3144 bytes
- 
 
 265 
3144 bytes
- 
 
 266 
> object.size(dpv <- as.data.frame.table(apv))	# 8176 bytes
- 
 
 267 
8176 bytes
- 
 
 268 
> str(dpv)
- 
 
 269 
'data.frame':	180 obs. of  5 variables:
- 
 
 270 
 $ Var1: Factor w/ 5 levels "n_1","n_2","n_3",..: 1 2 3 4 5 1 2 3 4 5 ...
- 
 
 271 
 $ Var2: Factor w/ 3 levels "two.sided","less",..: 1 1 1 1 1 2 2 2 2 2 ...
- 
 
 272 
 $ Var3: Factor w/ 2 levels "s0","s.": 1 1 1 1 1 1 1 1 1 1 ...
- 
 
 273 
 $ Var4: Factor w/ 6 levels "d.rank=Inf","d.rank=20",..: 1 1 1 1 1 1 1 1 1 1 ...
- 
 
 274 
 $ Freq: num  1 1 0.5 0.25 0.125 1 1 1 1 1 ...
- 
 
 275 
> str(ps. <- apv[,,"s.",])
- 
 
 276 
 num [1:5, 1:3, 1:6] 1 0.5 0.25 0.125 0.0625 1 1 1 1 1 ...
- 
 
 277 
 - attr(*, "dimnames")=List of 3
- 
 
 278 
  ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...
- 
 
 279 
  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 280 
  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 281 
> str(ps0 <- apv[,,"s0",])
- 
 
 282 
 num [1:5, 1:3, 1:6] 1 1 0.5 0.25 0.125 1 1 1 1 1 ...
- 
 
 283 
 - attr(*, "dimnames")=List of 3
- 
 
 284 
  ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...
- 
 
 285 
  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 286 
  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 287 
> (epsC <- .Machine$double.eps) # 2.2e-16
- 
 
 288 
[1] 2.220446e-16
- 
 
 289 
> allEQ <- function(x,y) all.equal(x, y, tolerance = 8*epsC)
- 
 
 290 
> stopifnot(exprs = {
- 
 
 291 
+     0 <= unlist(apv) ; unlist(apv) <= 1 # (no NA, ..)
- 
 
 292 
+     ## ps0 does *not* vary with digits.rank :
- 
 
 293 
+     identical(dim(print(ups0 <- drop(unique(ps0, MARGIN = 3)))), c(5L, 3L))
- 
 
 294 
+     is.function(uNam <- function(x) `names<-`(x, NULL))
- 
 
 295 
+     allEQ(uNam(ups0[,"two.sided"]), c(1, 2^(0:-3)))
- 
 
 296 
+     allEQ(uNam(ups0[, "greater" ]), 2^(0:-4))
- 
 
 297 
+     allEQ(uNam(ups0[,  "less"   ]), c(1,1,1,1,1))
- 
 
 298 
+     ## NB: This has changed from R 4.6.0 (now using digits.zap = digits.rank):
- 
 
 299 
+     ## ps., eps=1e-9: two possible results, boundary for 1e-9 between digits.rank = 7 and 10
- 
 
 300 
+     identical(dim(print(ups. <- unique(ps., MARGIN = 3))), c(5L, 3L, 2L))
- 
 
 301 
+     allEQ(ps.[,,"d.rank=Inf"], ps.[,,"d.rank=10"])
- 
 
 302 
+     is.function(uNam <- function(x) `dimnames<-`(x, NULL))
- 
 
 303 
+     allEQ(uNam(ups.[,"two.sided", ]), 2^cbind(0:-4, c(0L,0:-3)))
- 
 
 304 
+     allEQ(uNam(ups.[, "greater" , ]), cbind(2^-(1:5), 2^-c(1,1:4)))
- 
 
 305 
+     allEQ(uNam(ups.[,  "less"   , ]), cbind(1, rep(1, 5)))
- 
 
 306 
+ })
- 
 
 307 
    two.sided less greater
- 
 
 308 
n_1     1.000    1  1.0000
- 
 
 309 
n_2     1.000    1  0.5000
- 
 
 310 
n_3     0.500    1  0.2500
- 
 
 311 
n_4     0.250    1  0.1250
- 
 
 312 
n_5     0.125    1  0.0625
- 
 
 313 
, , d.rank=Inf
- 
 
 314 
 
- 
 
 315 
    two.sided less greater
- 
 
 316 
n_1    1.0000    1 0.50000
- 
 
 317 
n_2    0.5000    1 0.25000
- 
 
 318 
n_3    0.2500    1 0.12500
- 
 
 319 
n_4    0.1250    1 0.06250
- 
 
 320 
n_5    0.0625    1 0.03125
- 
 
 321 
 
- 
 
 322 
, , d.rank=7
- 
 
 323 
 
- 
 
 324 
    two.sided less greater
- 
 
 325 
n_1     1.000    1  0.5000
- 
 
 326 
n_2     1.000    1  0.5000
- 
 
 327 
n_3     0.500    1  0.2500
- 
 
 328 
n_4     0.250    1  0.1250
- 
 
 329 
n_5     0.125    1  0.0625
- 
 
 330 
 
- 
 
 331 
>
- 
 
 332 
>
- 
 
 333 
> ## b) Statistic - Estimate
- 
 
 334 
>
- 
 
 335 
> (astat <- simplify2array(
- 
 
 336 
+      lapply(stR[["statistic"]],
- 
 
 337 
+             \(L0) simplify2array(
- 
 
 338 
+                       lapply(L0, \(L) simplify2array(sapply(L, unList)))))))
- 
 
 339 
, , s0, d.rank=Inf
- 
 
 340 
 
- 
 
 341 
      two.sided less greater
- 
 
 342 
n_1.V         0    0       0
- 
 
 343 
n_2.V         2    2       2
- 
 
 344 
n_3.V         5    5       5
- 
 
 345 
n_4.V         9    9       9
- 
 
 346 
n_5.V        14   14      14
- 
 
 347 
 
- 
 
 348 
, , s., d.rank=Inf
- 
 
 349 
 
- 
 
 350 
      two.sided less greater
- 
 
 351 
n_1.V         1    1       1
- 
 
 352 
n_2.V         3    3       3
- 
 
 353 
n_3.V         6    6       6
- 
 
 354 
n_4.V        10   10      10
- 
 
 355 
n_5.V        15   15      15
- 
 
 356 
 
- 
 
 357 
, , s0, d.rank=20
- 
 
 358 
 
- 
 
 359 
      two.sided less greater
- 
 
 360 
n_1.V         0    0       0
- 
 
 361 
n_2.V         2    2       2
- 
 
 362 
n_3.V         5    5       5
- 
 
 363 
n_4.V         9    9       9
- 
 
 364 
n_5.V        14   14      14
- 
 
 365 
 
- 
 
 366 
, , s., d.rank=20
- 
 
 367 
 
- 
 
 368 
      two.sided less greater
- 
 
 369 
n_1.V         1    1       1
- 
 
 370 
n_2.V         3    3       3
- 
 
 371 
n_3.V         6    6       6
- 
 
 372 
n_4.V        10   10      10
- 
 
 373 
n_5.V        15   15      15
- 
 
 374 
 
- 
 
 375 
, , s0, d.rank=15
- 
 
 376 
 
- 
 
 377 
      two.sided less greater
- 
 
 378 
n_1.V         0    0       0
- 
 
 379 
n_2.V         2    2       2
- 
 
 380 
n_3.V         5    5       5
- 
 
 381 
n_4.V         9    9       9
- 
 
 382 
n_5.V        14   14      14
- 
 
 383 
 
- 
 
 384 
, , s., d.rank=15
- 
 
 385 
 
- 
 
 386 
      two.sided less greater
- 
 
 387 
n_1.V         1    1       1
- 
 
 388 
n_2.V         3    3       3
- 
 
 389 
n_3.V         6    6       6
- 
 
 390 
n_4.V        10   10      10
- 
 
 391 
n_5.V        15   15      15
- 
 
 392 
 
- 
 
 393 
, , s0, d.rank=10
- 
 
 394 
 
- 
 
 395 
      two.sided less greater
- 
 
 396 
n_1.V         0    0       0
- 
 
 397 
n_2.V         2    2       2
- 
 
 398 
n_3.V         5    5       5
- 
 
 399 
n_4.V         9    9       9
- 
 
 400 
n_5.V        14   14      14
- 
 
 401 
 
- 
 
 402 
, , s., d.rank=10
- 
 
 403 
 
- 
 
 404 
      two.sided less greater
- 
 
 405 
n_1.V         1    1       1
- 
 
 406 
n_2.V         3    3       3
- 
 
 407 
n_3.V         6    6       6
- 
 
 408 
n_4.V        10   10      10
- 
 
 409 
n_5.V        15   15      15
- 
 
 410 
 
- 
 
 411 
, , s0, d.rank=7
- 
 
 412 
 
- 
 
 413 
      two.sided less greater
- 
 
 414 
n_1.V         0    0       0
- 
 
 415 
n_2.V         2    2       2
- 
 
 416 
n_3.V         5    5       5
- 
 
 417 
n_4.V         9    9       9
- 
 
 418 
n_5.V        14   14      14
- 
 
 419 
 
- 
 
 420 
, , s., d.rank=7
- 
 
 421 
 
- 
 
 422 
      two.sided less greater
- 
 
 423 
n_1.V         1    1       1
- 
 
 424 
n_2.V         2    2       2
- 
 
 425 
n_3.V         5    5       5
- 
 
 426 
n_4.V         9    9       9
- 
 
 427 
n_5.V        14   14      14
- 
 
 428 
 
- 
 
 429 
, , s0, d.rank=5
- 
 
 430 
 
- 
 
 431 
      two.sided less greater
- 
 
 432 
n_1.V         0    0       0
- 
 
 433 
n_2.V         2    2       2
- 
 
 434 
n_3.V         5    5       5
- 
 
 435 
n_4.V         9    9       9
- 
 
 436 
n_5.V        14   14      14
- 
 
 437 
 
- 
 
 438 
, , s., d.rank=5
- 
 
 439 
 
- 
 
 440 
      two.sided less greater
- 
 
 441 
n_1.V         1    1       1
- 
 
 442 
n_2.V         2    2       2
- 
 
 443 
n_3.V         5    5       5
- 
 
 444 
n_4.V         9    9       9
- 
 
 445 
n_5.V        14   14      14
- 
 
 446 
 
- 
 
 447 
>
- 
 
 448 
> (aEst <- simplify2array(
- 
 
 449 
+      lapply(stR[["estimate"]],
- 
 
 450 
+             \(L0) simplify2array(
- 
 
 451 
+                       lapply(L0, \(L) simplify2array(lapply(L, unList)))))))
- 
 
 452 
, , s0, d.rank=Inf
- 
 
 453 
 
- 
 
 454 
                   two.sided less greater
- 
 
 455 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 456 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 457 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 458 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 459 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 460 
 
- 
 
 461 
, , s., d.rank=Inf
- 
 
 462 
 
- 
 
 463 
                   two.sided    less greater
- 
 
 464 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 465 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 466 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 467 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 468 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 469 
 
- 
 
 470 
, , s0, d.rank=20
- 
 
 471 
 
- 
 
 472 
                   two.sided less greater
- 
 
 473 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 474 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 475 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 476 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 477 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 478 
 
- 
 
 479 
, , s., d.rank=20
- 
 
 480 
 
- 
 
 481 
                   two.sided    less greater
- 
 
 482 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 483 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 484 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 485 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 486 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 487 
 
- 
 
 488 
, , s0, d.rank=15
- 
 
 489 
 
- 
 
 490 
                   two.sided less greater
- 
 
 491 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 492 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 493 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 494 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 495 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 496 
 
- 
 
 497 
, , s., d.rank=15
- 
 
 498 
 
- 
 
 499 
                   two.sided    less greater
- 
 
 500 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 501 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 502 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 503 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 504 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 505 
 
- 
 
 506 
, , s0, d.rank=10
- 
 
 507 
 
- 
 
 508 
                   two.sided less greater
- 
 
 509 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 510 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 511 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 512 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 513 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 514 
 
- 
 
 515 
, , s., d.rank=10
- 
 
 516 
 
- 
 
 517 
                   two.sided    less greater
- 
 
 518 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 519 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 520 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 521 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 522 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 523 
 
- 
 
 524 
, , s0, d.rank=7
- 
 
 525 
 
- 
 
 526 
                   two.sided less greater
- 
 
 527 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 528 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 529 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 530 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 531 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 532 
 
- 
 
 533 
, , s., d.rank=7
- 
 
 534 
 
- 
 
 535 
                   two.sided    less greater
- 
 
 536 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 537 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 538 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 539 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 540 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 541 
 
- 
 
 542 
, , s0, d.rank=5
- 
 
 543 
 
- 
 
 544 
                   two.sided less greater
- 
 
 545 
n_1.(pseudo)median       0.0  0.0     0.0
- 
 
 546 
n_2.(pseudo)median       0.5  0.5     0.5
- 
 
 547 
n_3.(pseudo)median       1.0  1.0     1.0
- 
 
 548 
n_4.(pseudo)median       1.5  1.5     1.5
- 
 
 549 
n_5.(pseudo)median       2.0  2.0     2.0
- 
 
 550 
 
- 
 
 551 
, , s., d.rank=5
- 
 
 552 
 
- 
 
 553 
                   two.sided    less greater
- 
 
 554 
n_1.(pseudo)median   1.0e-09 1.0e-09 1.0e-09
- 
 
 555 
n_2.(pseudo)median   5.0e-01 5.0e-01 5.0e-01
- 
 
 556 
n_3.(pseudo)median   1.0e+00 1.0e+00 1.0e+00
- 
 
 557 
n_4.(pseudo)median   1.5e+00 1.5e+00 1.5e+00
- 
 
 558 
n_5.(pseudo)median   2.0e+00 2.0e+00 2.0e+00
- 
 
 559 
 
- 
 
 560 
> str(aEst)
- 
 
 561 
 num [1:5, 1:3, 1:2, 1:6] 0 0.5 1 1.5 2 0 0.5 1 1.5 2 ...
- 
 
 562 
 - attr(*, "dimnames")=List of 4
- 
 
 563 
  ..$ : chr [1:5] "n_1.(pseudo)median" "n_2.(pseudo)median" "n_3.(pseudo)median" "n_4.(pseudo)median" ...
- 
 
 564 
  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 565 
  ..$ : chr [1:2] "s0" "s."
- 
 
 566 
  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 567 
>  ## num [1:5, 1:3, 1:2, 1:6] 0 0.5 1 1.5 2 0 0.5 1 1.5 2 ...
- 
 
 568 
>  ## - attr(*, "dimnames")=List of 4
- 
 
 569 
>  ##  ..$ : chr [1:5] "n_1.(pseudo)median" "n_2.(pseudo)median" "n_3.(pseudo)median" "n_4.(pseudo)median" ...
- 
 
 570 
>  ##  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 571 
>  ##  ..$ : chr [1:2] "s0" "s."
- 
 
 572 
>  ##  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 573 
>
- 
 
 574 
> ## Confidence Interval :
- 
 
 575 
> ## c)  C.I. extraction
- 
 
 576 
> CI <- stR[["conf.int"]]
- 
 
 577 
> str(CI, max.level = 2)
- 
 
 578 
List of 6
- 
 
 579 
 $ d.rank=Inf:List of 2
- 
 
 580 
  ..$ s0:List of 3
- 
 
 581 
  ..$ s.:List of 3
- 
 
 582 
 $ d.rank=20 :List of 2
- 
 
 583 
  ..$ s0:List of 3
- 
 
 584 
  ..$ s.:List of 3
- 
 
 585 
 $ d.rank=15 :List of 2
- 
 
 586 
  ..$ s0:List of 3
- 
 
 587 
  ..$ s.:List of 3
- 
 
 588 
 $ d.rank=10 :List of 2
- 
 
 589 
  ..$ s0:List of 3
- 
 
 590 
  ..$ s.:List of 3
- 
 
 591 
 $ d.rank=7  :List of 2
- 
 
 592 
  ..$ s0:List of 3
- 
 
 593 
  ..$ s.:List of 3
- 
 
 594 
 $ d.rank=5  :List of 2
- 
 
 595 
  ..$ s0:List of 3
- 
 
 596 
  ..$ s.:List of 3
- 
 
 597 
>
- 
 
 598 
> if(FALSE) { # printing chCI below is much nicer
- 
 
 599 
+   str(aCI <- simplify2array(lapply(CI,
- 
 
 600 
+                      function(A) simplify2array(lapply(A,
- 
 
 601 
+                              function(B) simplify2array(lapply(B,
- 
 
 602 
+                                         function(C) t(simplify2array(C)))))))))
- 
 
 603 
+   ##  num [1:5, 1:2, 1:3, 1:2, 1:6] 0 0 0 0 0 ...
- 
 
 604 
+   ## - attr(*, "dimnames")=List of 5
- 
 
 605 
+   ##  ..$ : chr [1:5] "n_1" "n_2" "n_3" "n_4" ...
- 
 
 606 
+   ##  ..$ : NULL
- 
 
 607 
+   ##  ..$ : chr [1:3] "two.sided" "less" "greater"
- 
 
 608 
+   ##  ..$ : chr [1:2] "s0" "s."
- 
 
 609 
+   ##  ..$ : chr [1:6] "d.rank=Inf" "d.rank=20" "d.rank=15" "d.rank=10" ...
- 
 
 610 
+   aCI # has *lost* the conf.level which is varying !!
- 
 
 611 
+ }
202 
>
612 
>
203 
> ## a) P-value
 - 
 
204 
> pv <- stR[["p.value"]]
 - 
 
205 
> ## only the first is NaN, all others in [0,1]:
 - 
 
206 
> sapply(pv$s0, unlist)
 - 
 
207 
     two.sided less greater
 - 
 
208 
[1,]     1.000    1  1.0000
 - 
 
209 
[2,]     1.000    1  0.5000
 - 
 
210 
[3,]     0.500    1  0.2500
 - 
 
211 
[4,]     0.250    1  0.1250
 - 
 
212 
[5,]     0.125    1  0.0625
 - 
 
213 
> sapply(pv$s., unlist) # not really close, but ..
 - 
 
214 
     two.sided less greater
 - 
 
215 
[1,]    1.0000    1 0.50000
 - 
 
216 
[2,]    0.5000    1 0.25000
 - 
 
217 
[3,]    0.2500    1 0.12500
 - 
 
218 
[4,]    0.1250    1 0.06250
 - 
 
219 
[5,]    0.0625    1 0.03125
 - 
 
220 
>
- 
 
221 
> pv$s0$two.sided[1] <-  1 ## artificially
 - 
 
222 
> stopifnot(all.equal(pv$s0, pv$s., tolerance = 0.5 + 1e-6), # seen 0.5
 - 
 
223 
+ 	  ## "less" are close:
 - 
 
224 
+ 	  all.equal(unlist(pv[[c("s0","less")]]),
 - 
 
225 
+ 		    unlist(pv[[c("s.","less")]]),
 - 
 
226 
+                     tolerance = 0.03),
 - 
 
227 
+ 	  0 <= unlist(pv), unlist(pv) <= 1) # <- no further NA ..
 - 
 
228 
> ## b)
 - 
 
229 
> sapply(stR[["statistic"]], unlist)
 - 
 
230 
            s0 s.
 - 
 
231 
two.sided.V  0  1
 - 
 
232 
two.sided.V  2  3
 - 
 
233 
two.sided.V  5  6
 - 
 
234 
two.sided.V  9 10
 - 
 
235 
two.sided.V 14 15
 - 
 
236 
less.V       0  1
 - 
 
237 
less.V       2  3
 - 
 
238 
less.V       5  6
 - 
 
239 
less.V       9 10
 - 
 
240 
less.V      14 15
 - 
 
241 
greater.V    0  1
 - 
 
242 
greater.V    2  3
 - 
 
243 
greater.V    5  6
 - 
 
244 
greater.V    9 10
 - 
 
245 
greater.V   14 15
 - 
 
246 
> ## Conf.int.:
 - 
 
247 
> ## c)
 - 
 
248 
> sapply(stR[["estimate" ]], unlist)
 - 
 
249 
                          s0      s.
 - 
 
250 
two.sided.(pseudo)median 0.0 1.0e-09
 - 
 
251 
two.sided.(pseudo)median 0.5 5.0e-01
 - 
 
252 
two.sided.(pseudo)median 1.0 1.0e+00
 - 
 
253 
two.sided.(pseudo)median 1.5 1.5e+00
 - 
 
254 
two.sided.(pseudo)median 2.0 2.0e+00
 - 
 
255 
less.(pseudo)median      0.0 1.0e-09
 - 
 
256 
less.(pseudo)median      0.5 5.0e-01
 - 
 
257 
less.(pseudo)median      1.0 1.0e+00
 - 
 
258 
less.(pseudo)median      1.5 1.5e+00
 - 
 
259 
less.(pseudo)median      2.0 2.0e+00
 - 
 
260 
greater.(pseudo)median   0.0 1.0e-09
 - 
 
261 
greater.(pseudo)median   0.5 5.0e-01
 - 
 
262 
greater.(pseudo)median   1.0 1.0e+00
 - 
 
263 
greater.(pseudo)median   1.5 1.5e+00
 - 
 
264 
greater.(pseudo)median   2.0 2.0e+00
 - 
 
265 
> ## d) confidence interval
 613 
> ## d) confidence interval __printing__
266 
> formatCI <- function(ci)
 614 
> formatCI <- function(ci)
267 
+     sprintf("[%g, %g] (%g%%)", ci[[1]], ci[[2]],
 615 
+     sprintf("[%g, %g] (%g%%)", ci[[1]], ci[[2]],
268 
+ 	    round(100*attr(ci,"conf.level")))
 616 
+ 	    round(100*attr(ci,"conf.level")))
269 
> nx <- length(x0)
 617 
> nx <- length(x0)
- 
 
 618 
> Fval <- array("", dim = c(nx, length(alts), length(x.set)))
270 
> noquote(vapply(stR[["conf.int"]], function(ss)
 619 
> (chCI <- noquote(vapply(CI, function(D)
- 
 
 620 
+     vapply(D, function(ss)
271 
+     vapply(ss, function(alt) vapply(alt, formatCI, ""), character(nx)),
 621 
+         vapply(ss, function(alt) vapply(alt, formatCI, ""), character(nx)),
272 
+     matrix("", nx, length(alts))))
 622 
+         Fval[,,1]),
- 
 
 623 
+     Fval)
273 
, , s0
 624 
+     ))
- 
 
 625 
, , s0, d.rank=Inf
- 
 
 626 
 
- 
 
 627 
    two.sided       less               greater
- 
 
 628 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 629 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 630 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 631 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 632 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 633 
 
- 
 
 634 
, , s., d.rank=Inf
- 
 
 635 
 
- 
 
 636 
    two.sided          less               greater
- 
 
 637 
n_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 638 
n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 639 
n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 640 
n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 641 
n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%)    [1e-09, Inf] (97%)
- 
 
 642 
 
- 
 
 643 
, , s0, d.rank=20
274 
 
 644 
 
275 
     two.sided       less               greater
645 
    two.sided       less               greater
276 
[1,] [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
646 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
277 
[2,] [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
647 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
278 
[3,] [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
648 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
279 
[4,] [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
649 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
280 
[5,] [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
 650 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
281 
 
 651 
 
282 
, , s.
 652 
, , s., d.rank=20
283 
 
 653 
 
284 
     two.sided          less               greater
654 
    two.sided          less               greater
- 
 
 655 
n_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
285 
[1,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 656 
n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 657 
n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 658 
n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 659 
n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%)    [1e-09, Inf] (97%)
- 
 
 660 
 
- 
 
 661 
, , s0, d.rank=15
- 
 
 662 
 
- 
 
 663 
    two.sided       less               greater
- 
 
 664 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 665 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 666 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 667 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 668 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 669 
 
- 
 
 670 
, , s., d.rank=15
- 
 
 671 
 
- 
 
 672 
    two.sided          less               greater
- 
 
 673 
n_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 674 
n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 675 
n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 676 
n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 677 
n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%)    [1e-09, Inf] (97%)
- 
 
 678 
 
- 
 
 679 
, , s0, d.rank=10
- 
 
 680 
 
- 
 
 681 
    two.sided       less               greater
- 
 
 682 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 683 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 684 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 685 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 686 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 687 
 
- 
 
 688 
, , s., d.rank=10
- 
 
 689 
 
- 
 
 690 
    two.sided          less               greater
- 
 
 691 
n_1 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
286 
[2,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 692 
n_2 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
287 
[3,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 693 
n_3 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
288 
[4,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 694 
n_4 [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
289 
[5,] [-Inf, Inf] (100%) [-Inf, 4] (97%)    [1e-09, Inf] (97%)
 695 
n_5 [-Inf, Inf] (100%) [-Inf, 4] (97%)    [1e-09, Inf] (97%)
- 
 
 696 
 
- 
 
 697 
, , s0, d.rank=7
- 
 
 698 
 
- 
 
 699 
    two.sided       less               greater
- 
 
 700 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 701 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 702 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 703 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 704 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 705 
 
- 
 
 706 
, , s., d.rank=7
- 
 
 707 
 
- 
 
 708 
    two.sided           less               greater
- 
 
 709 
n_1 [-Inf, Inf] (100%)  [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 710 
n_2 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 711 
n_3 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 712 
n_4 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 713 
n_5 [1e-09, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 714 
 
- 
 
 715 
, , s0, d.rank=5
- 
 
 716 
 
- 
 
 717 
    two.sided       less               greater
- 
 
 718 
n_1 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 719 
n_2 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 720 
n_3 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 721 
n_4 [0, Inf] (100%) [-Inf, Inf] (100%) [0, Inf] (100%)
- 
 
 722 
n_5 [0, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
- 
 
 723 
 
- 
 
 724 
, , s., d.rank=5
- 
 
 725 
 
- 
 
 726 
    two.sided           less               greater
- 
 
 727 
n_1 [-Inf, Inf] (100%)  [-Inf, Inf] (100%) [-Inf, Inf] (100%)
- 
 
 728 
n_2 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 729 
n_3 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 730 
n_4 [1e-09, Inf] (100%) [-Inf, Inf] (100%) [1e-09, Inf] (100%)
- 
 
 731 
n_5 [1e-09, Inf] (100%) [-Inf, 4] (100%)   [0.5, Inf] (97%)
290 
 
 732 
 
291 
>
733 
>
292 
>
734 
>
293 
> ##-------- 2-sample tests (working unchanged) ------------------
 735 
> ##-------- 2-sample tests (working unchanged) ------------------
294 
>
736 
>
295 
> R2 <- lapply(alts, ## for all three alternatives
 737 
> R2 <- lapply(alts, ## for all three alternatives
296 
+              function(alt)
 738 
+              function(alt)
297 
+                  lapply(seq_along(x0), function(k)
 739 
+                  lapply(seq_along(x0), function(k)
298 
+                          wilcox.test(x = x.set$s0[[k]], y = x.set$s.[[k]],
 740 
+                          wilcox.test(x = x.set$s0[[k]], y = x.set$s.[[k]],
299 
+                                      exact=TRUE, conf.int=TRUE, alternative = alt)))
 741 
+                                      exact=TRUE, conf.int=TRUE, alternative = alt)))
300 
> length(w2 <- warnings()) # 27
 742 
> length(w2 <- warnings()) # 1  (was '27')
301 
[1] 1
 743 
[1] 1
302 
> unique(w2) # 3 different ones
 744 
> if(!identical(w2, w1)) # typically they *are* identical ==> no new warnings
303 
Warning message:
 745 
+     summary(w2)
304 
In ks.test.default(round(ds2, 1), "pnorm", exact = TRUE) :
 - 
 
305 
  ties should not be present for the one-sample Kolmogorov-Smirnov test
 - 
 
306 
>
746 
>
- 
 
 747 
>
307 
> table(uc2 <- unlist(c2 <- lapply(R2, function(A) lapply(A, class))))
 748 
> table(uc2 <- unlist(c2 <- lapply(R2, function(A) lapply(A, class))))
308 
 
 749 
 
309 
htest
750 
htest
310 
   15
751 
   15
311 
> stopifnot(uc2 == "htest")
 752 
> stopifnot(uc2 == "htest")
312 
>
753 
>
313 
> stR2 <- lapply(stats,
 754 
> stR2 <- lapply(stats,
314 
+                function(COMP)
 755 
+                function(COMP)
315 
+                    lapply(R2, function(A) lapply(A, getC, C=COMP)))
 756 
+                    lapply(R2, function(A) lapply(A, `[[`, COMP)))
316 
>
757 
>
317 
> lapply(stats[-3], ## -3: "conf.int" separately
 758 
> lapply(stats[-3], ## -3: "conf.int" separately
318 
+        function(ST) sapply(stR2[[ST]], unlist))
 759 
+        function(ST) sapply(stR2[[ST]], unlist))
319 
$statistic
 760 
$statistic
320 
  two.sided less greater
 761 
  two.sided less greater
321 
W       0.0  0.0     0.0
 762 
W       0.0  0.0     0.0
Line 338... Line 779...
338 
difference in location    -5e-10 -5e-10  -5e-10
 779 
difference in location    -5e-10 -5e-10  -5e-10
339 
difference in location     0e+00  0e+00   0e+00
 780 
difference in location     0e+00  0e+00   0e+00
340 
difference in location     0e+00  0e+00   0e+00
 781 
difference in location     0e+00  0e+00   0e+00
341 
difference in location     0e+00  0e+00   0e+00
 782 
difference in location     0e+00  0e+00   0e+00
342 
 
 783 
 
343 
>
784 
>
344 
> noquote(sapply(stR2[["conf.int"]], function(.) vapply(., formatCI, "")))
 785 
> noquote(sapply(stR2[["conf.int"]], function(.) vapply(., formatCI, "")))
345 
     two.sided          less               greater
786 
     two.sided          less               greater
346 
[1,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 787 
[1,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
347 
[2,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
 788 
[2,] [-Inf, Inf] (100%) [-Inf, Inf] (100%) [-Inf, Inf] (100%)
348 
[3,] [-Inf, Inf] (100%) [-Inf, 2] (100%)   [-2, Inf] (100%)
789 
[3,] [-Inf, Inf] (100%) [-Inf, 2] (100%)   [-2, Inf] (100%)
349 
[4,] [-3, 3] (100%)     [-Inf, 2] (97%)    [-2, Inf] (97%)
790 
[4,] [-3, 3] (100%)     [-Inf, 2] (97%)    [-2, Inf] (97%)
350 
[5,] [-3, 3] (96%)      [-Inf, 2] (95%)    [-2, Inf] (97%)
791 
[5,] [-3, 3] (96%)      [-Inf, 2] (95%)    [-2, Inf] (97%)
351 
>
792 
>
352 
>
793 
>
353 
> proc.time()
 794 
> proc.time()
354 
   user  system elapsed
795 
   user  system elapsed
355 
  0.237   0.012   0.240
796 
  0.544   0.273   0.981