R Under development (unstable) (2025-07-15 r88411 ucrt) -- "Unsuffered Consequences"
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> ## VT::15.01.2020 - this will render the output independent
> ##  from the version of the package
> suppressPackageStartupMessages(library(trimcluster))
> 
>   set.seed(10001)
>   n1 <-60
>   n2 <-60
>   n3 <-70
>   n0 <-10
>   nn <- n1+n2+n3+n0
>   pp <- 2
>   X <- matrix(rep(0,nn*pp),nrow=nn)
>   ii <-0
>   for (i in 1:n1){
+     ii <-ii+1
+     X[ii,] <- c(5,-5)+rnorm(2)
+   }
>   for (i in 1:n2){
+     ii <- ii+1
+     X[ii,] <- c(5,5)+rnorm(2)*0.75
+   }
>   for (i in 1:n3){
+     ii <- ii+1
+     X[ii,] <- c(-5,-5)+rnorm(2)*0.75
+   }
>   for (i in 1:n0){
+     ii <- ii+1
+     X[ii,] <- rnorm(2)*8
+   }
> 
>   tkm1 <- trimkmeans(X, k=3, trim=0.1, runs=5, scaling=TRUE)
> # runs=5 is used to save computing time.
>   print(tkm1)
* trimmed k-means *
trim= 0.1 , k= 3 
Classification (trimmed points are indicated by  4 ):
  [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3
 [38] 3 3 3 4 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 4 4 4 4 4 4 4 4 4 4
Means:
          X 1       X 2
C 1 -4.948673 -4.978344
C 2  5.125058  5.040808
C 3  5.139695 -5.083246
Trimmed mean squares:  0.04719378 
>   plot(tkm1, X)
> 
> ##  With fixed initial values
>   set.seed(1)
>   k <- 3
>   means <- X[sample(nrow(X), k), , drop=FALSE]
>   tkm2 <- trimkmeans(X, k=3, trim=0.1, runs=5, points=means)
Warning message:
In tkmeans(data, k = k, alpha = trim, scale = scaling, nstart = runs,  :
  If initial mean vectors are specified, 'nstart' should be 1 (otherwise the same initial means are used for all runs)
> 
> ##  Univariate data
>   set.seed(2)
>   tkm3 <- trimkmeans(X[, 1, drop=FALSE], k=3, trim=0.1, runs=5)
>   plot(tkm3, X[, 1, drop=FALSE])
> 
> ##  Multivariate data (p > 2)
>   set.seed(3)
>   X1 <- as.matrix(iris[, 1:4])
>   tkm4 <- trimkmeans(X1, k=3, trim=0.1, runs=5)
>   plot(tkm4, X1)
> 
> ## use deprecated arguments  
> try(tkm5 <- trimkmeans(iris[, 1:4], k=3, trim=0.1, runs=5, countmode=100, 
+     printcrit=TRUE, maxit=1000))
Warning messages:
1: In trimkmeans(iris[, 1:4], k = 3, trim = 0.1, runs = 5, countmode = 100,  :
  The parameter 'maxit' is deprecated, please read the help and use the combination of 'niter1', 'niter2', 'nkeep'.
2: In trimkmeans(iris[, 1:4], k = 3, trim = 0.1, runs = 5, countmode = 100,  :
  The parameter 'countmode' is deprecated, please use 'trace'.
3: In trimkmeans(iris[, 1:4], k = 3, trim = 0.1, runs = 5, countmode = 100,  :
  The parameter 'printcrit' is deprecated, please use 'trace'.
> 
>   tkmx1 <- trimcluster:::trimkmeans_orig(X, k=3, trim=0.1, runs=3, scaling=TRUE, countmode=10, printcrit=TRUE)
Iteration  1  criterion value  0.04719378 
Iteration  2  criterion value  0.0472178 
Iteration  3  criterion value  0.04719378 
> # runs=3 is used to save computing time.
> 
>   set.seed(1)
>   k <- 3
>   means <- X[sample(nrow(X), k), , drop=FALSE]
>   tkmx2 <- trimcluster:::trimkmeans_orig(X, k=3, trim=0.1, runs=3, points=means)
> 
> proc.time()
   user  system elapsed 
   1.42    0.07    1.48