Enlightening. Thanks.
Joh
Gabor Grothendieck wrote:
> If you want indexes, i.e. 1, 2, 3, ... instead of the values in v you
> can still use split -- just split on seq_along(v) instead of v (or if
> v had names you might want to split along names(v)):
>
> split(seq_along(v), ct)
>
> and if you
If you want indexes, i.e. 1, 2, 3, ... instead of the values in v you
can still use split -- just split on seq_along(v) instead of v (or if
v had names you might want to split along names(v)):
split(seq_along(v), ct)
and if you only want to retain groups with 2+ elements then
you can just Filter
Johannes Graumann <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> But cutree does away with the indexes from the original input, which
> rect.hclust retains.
> I will have no other choice and match that input with the 'values'
> contained in the clusters ...
If you want to retain the ori
Here's what I finally came up with. Thanks for your help!
Joh
MQUSpotOverlapClusters <- function(
Series,# Vector of data to be evaluated
distance=0.5,# Maximum distance of clustered data points
minSize=2# Minimum size of clusters returned
){
But cutree does away with the indexes from the original input, which
rect.hclust retains.
I will have no other choice and match that input with the 'values' contained
in the clusters ...
Joh
Gabor Grothendieck wrote:
> If we don't need any plotting we don't really need rect.hclust at
> all. Sp
If we don't need any plotting we don't really need rect.hclust at
all. Split the output of cutree, instead. Continuing from the
prior code:
> for(el in split(unname(vv), names(vv))) print(el)
[1] 0.00 0.45
[1] 1
[1] 2
[1] 3.00 3.25 3.33 3.75 4.10
[1] 5
[1] 6.00 6.45
[1] 7.0 7.1
[1] 8
On Dec 21,
Hm, hm, rect.hclust doesn't accept "plot=FALSE" and cutree doesn't retain
the indexes of membership ... anyway short of ripping out the guts of
rect.hclust to achieve the same result without an active graphics device?
Joh
>> # cluster and plot
>> hc <- hclust(dist(v), method = "single")
>> plot(h
Jim,
Although I can't find the post this code stems from, I had come across it on
my prowling the NG. It's not the one you had shared with me to eliminate
overlaps (and which I referenced below:
http://tolstoy.newcastle.edu.au/R/e2/help/07/07/21286.html). That
particular solution you had come up w
Thank you very much for this elegant solution to the problem. The reason I
still hope for an extension of Jim's code (not the one re responded with in
this thread, but the one I actually reference) is that windows of overlap
can be asymetric with that: one can check e.g. whether values overlap give
On Fri, 21 Dec 2007, Johannes Graumann wrote:
>
>
> Dear all,
>
> I'm trying to solve the problem, of how to find clusters of values in a
> vector that are closer than a given value. Illustrated this might look as
> follows:
>
> vector <- c(0,0.45,1,2,3,3.25,3.33,3.75,4.1,5,6,6.45,7,7.1,8)
>
> Wh
This may not be as direct as Jim's in terms of specifying granularity but
will uses conventional hierarchical clustering to create the clusters and also
draws a nice dendrogram for you. I have split the dendrogram at a
height of 0.5
to define the clusters but you can change that to whatever granu
Here is a modification of the algorithm to use a specified value for
the overlap:
> vector <- c(0,0.45,1,2,3,3.25,3.33,3.75,4.1,5,6,6.45,7,7.1,8)
> # following add 0.5 as the overlap detection -- can be changed
> x <- rbind(cbind(value=vector, oper=1, id=seq_along(vector)),
+cbind(valu
Dear all,
I'm trying to solve the problem, of how to find clusters of values in a
vector that are closer than a given value. Illustrated this might look as
follows:
vector <- c(0,0.45,1,2,3,3.25,3.33,3.75,4.1,5,6,6.45,7,7.1,8)
When using '0.5' as the proximity requirement, the following groups
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