W dniu 12 sierpnia 2011 05:19 użytkownik Chris Howden <
chris@trickysolutions.com.au> napisał:

> Thanks for the suggestion, I'll look into it
>

It seems to work! :)

library(multiv)
data(iris)
iris <- as.matrix(iris[,1:4])
h <- hierclust(iris, method=2)
d<- dist(iris)
hk<-hclust(d)
>str(hk)
List of 7
$ merge : int [1:149, 1:2] -102 -8 -1 -10 -129 -11 -5 -20 -30 -58 ...
$ height : num [1:149] 0 0.1 0.1 0.1 0.1 ...
$ order : int [1:150] 108 131 103 126 130 119 106 123 118 132 ...
$ labels : NULL
$ method : chr "complete"
$ call : language hclust(d = d)
$ dist.method: chr "euclidean"
- attr(*, "class")= chr "hclust"
> str(h)
List of 3
$ merge : int [1:149, 1:2] -102 -8 -1 -10 -129 -11 -41 -5 -20 7 ...
$ height: num [1:149] 0 0.01 0.01 0.01 0.01 ...
$ order : int [1:150] 42 23 15 16 45 34 33 17 21 32 ...

test.mat<-matrix(rnorm(90523*24),,24)
out<-hierclust(test.mat, method = 1, bign = T)
> print(object.size(out),u="Mb")
1.7 Mb
> str(out)
List of 3
$ merge : int [1:90522, 1:2] -35562 -19476 -60344 -66060 -38949 -14537 -3322
-20248 -19464 -78693 ...
$ height: num [1:90522] 1.93 1.94 1.96 1.98 2 ...
$ order : int [1:90523] 24026 61915 71685 16317 85828 11577 36034 37324
65754 55381 ...
> R.version$os
[1] "mingw32"


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