R Under development (unstable) (2024-08-15 r87022 ucrt) -- "Unsuffered Consequences"
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> 
> library(robustbase)
> ## testing functions:
> source(system.file("xtraR/ex-funs.R", package = "robustbase"))
> 
> x  <- c(0.26, 0.161, 1.33, -0.925, 0.199, -1.476, 0.489)
> iw <- c(5, 4, 4, 1, 5, 1, 5)
> 
> 
> stopifnot(0.26 == (himR <-  weighted.median(rep(x,iw))),
+           himR == wgt.himedian(x, iw), ## (once gave infinite loop)
+           himR == wgt.himedian(x, as.integer(iw)))
> 
> 
> ## same result, but *different wweigted.median() debug output!
> ##-- even when having EXACT data (& exact differences!)
> all.equal(Qn(c(2:1,4:3)), 1.1376128)
[1] "Mean relative difference: 0.001116917"
> 
> ###--- another inifinite loop {solved}:
> 
> (z4 <- round(qnorm(ppoints(4)), 2)) # -1.05 -0.30  0.30  1.05
[1] -1.05 -0.30  0.30  1.05
> 
> ## both the same (also wweigted.median debug output)
> (all.equal(weighted.median(z4, 4:1),
+            print(wgt.himedian (z4, 4:1))))# 3.97e-8
[1] -0.3
[1] TRUE
> 
> (all.equal(weighted.median(z4, c(4,2,3,17)),
+            print(wgt.himedian (z4, c(4,2,3,17)))))# 4.54e-8
[1] 1.05
[1] TRUE
> 
> Sn (z4)## = 0.8533053
[1] 0.8533053
> Sn (z4, const = 1)# = 0.75
[1] 0.75
> 
> ##-- now
> Qn (z4)# --> gave (another) infinite loop
[1] 0.8541636
>        ##--> now "works" after (float) rounding of differences!
>        ##--- DIFFERENT whimed() output!
> stopifnot(all.equal(Qn(z4, const = 1),
+                     print(Qn0R(z4))))
[1] 0.75
> 
> set.seed(1)
> n <- length(x <- round(2048*runif(16)))
> n*(n-1)/2 # 120
[1] 120
> xDiff <- local({y <- sort(x); m <- outer(y,y,"-"); m[lower.tri(m)] })
> xDsrt <- unique(sort(xDiff))
> stopifnot(exprs = {
+     ## so all pairwise differences differ :
+     length(xDsrt) == n*(n-1)/2 # 120
+     Qn0R(x, k=1:120) == xDsrt ## *all* the "quantiles" = order stats  do differ indeed
+     Qn(x, constant=1) == 374
+ })
> 
> ##  Bugs in  Qn(x,  k = <multiple values>)
> sapply(1:120, function(k) Qn(x, 1, k=k)) ## ends with 0 0 0 ... for the last ones
  [1]    9   20   25   51   54   60   65   75   95  115  119  122  131  154  170
 [16]  180  182  218  224  232  234  235  243  257  263  269  283  286  289  295
 [31]  334  340  349  358  365  374  386  388  400  404  411  417  425  433  453
 [46]  475  487  501  507  526  528  552  558  566  572  582  591  597  606  620
 [61]  629  635  645  647  657  660  667  687  744  751  760  762  790  809  811
 [76]  815  821  841  863  866  875  892  916  926  931  940  985  991  994 1033
 [91] 1045 1046 1053 1073 1078 1098 1148 1155 1161 1164 1173 1215 1226 1280 1296
[106] 1316 1391 1418 1427 1438 1447 1450 1478 1498 1513 1522 1573 1713 1733 1808
> if(FALSE) ## e.g.
+   Qn(x, k=1:10) # enters infinite loop after k[4] = 5
> 
> 
> ## yet another problem:
> Sn0R(c(1.1, -0.93, -0.11, -0.74))# 0.82
[1] 0.82
> Sn  (c(1.1, -0.93, -0.11, -0.74))# 0.9329471
[1] 0.9329471
> ## gave segmentation fault at Sat Mar 16 23:54:30 2002
> ## not anymore but 0.9329471
> 
> ### Check validity of basic algorithm few times
> set.seed(471)
> for(sim in 1:100) { # had '500'
+     cat(".")
+     x <- rnorm(rpois(1, lam=80))# not too large the *n0R() use time!
+     ##--> Sn0R() "fails" for  odd n
+     stopifnot(all.equal(Sn(x, const = 1), Sn0R(x)),
+               all.equal(Qn(x, const = 1), Qn0R(x), tolerance = 7e-8))
+     x <- round(x,2)
+     stopifnot(all.equal(Sn(x, const = 1), Sn0R(x)),
+               all.equal(Qn(x, const = 1), Qn0R(x), tolerance = 7e-8))
+     if(sim %% 50 == 0) cat(sim, "\n")
+ }
..................................................50 
..................................................100 
> 
> 
> 
> ###---- Last series of problems: when  n^2 > max.integer:
> 
> ## Large x with 1% outliers
> N <- 1e5
> n.o <- round(0.01 * N)
> nSim <- 24## interesting
> nSim <- 4 ## for package testing
> estim.lst <- c("mad", "Sn", "Qn")
> Res <- array(NA, dim = c(nSim, length(estim.lst), 1 + 2),
+              dimnames= list(NULL,estim.lst, c("Tx","cpu1", "cpu3")))
> set.seed(101)
> for(i in 1:nSim) {
+     x <- sample(c(rnorm(N), 10*min(1, abs(rt(1, df=2))) + rnorm(n.o)))
+     cat(i)
+     for(S in estim.lst) {
+         cpu <- system.time(Tx <- get(S)(x))[1:3]
+         Res[i, S,] <- c(Tx, cpu[c(1,3)])
+     }
+     cat(" ")
+ };  cat("\n")
1 2 3 4 
> 
> options(digits = 5)
> 
> (Tx <- Res[,, "Tx"])
        mad     Sn     Qn
[1,] 1.0091 1.0128 1.0165
[2,] 1.0082 1.0101 1.0156
[3,] 1.0088 1.0150 1.0198
[4,] 1.0083 1.0123 1.0177
> stopifnot(abs(range(Tx - 1)) < 0.03)
> 
> q()##============================================================================
> proc.time()
   user  system elapsed 
   1.57    0.12    1.68