R help - Apr 2019 - Why is it not possible to cut a tree returned by Agnes or Diana by height?

> asdf = data.frame(x=c(1,2,3), y=c(4,5,6), z=c(7,8,9))
    > cutree(agnes(asdf), h=100)
    Error in cutree(agnes(asdf), h = 100) :
      the 'height' component of 'tree' is not sorted
(increasingly)
    > cutree(diana(asdf), h=100)
    Error in cutree(diana(asdf), h = 100) :
      the 'height' component of 'tree' is not sorted
(increasingly)

I'm not sure if I understand why this is the case.

This is what I want: Cluster stuff by the //distances//, **not** by
how many clusters I want to have.

If two things are further from each other than X, they should go to
different clusters. Otherwise, the same cluster.

Is it unreasonable what I'm asking for? I image if I was to manually
implement Agnes or Diana this would go like that: stop joining
clusters if the smallest distance between any pair of clusters is
larger than X (Agnes) or stop dividing clusters if the largest cluster
has a diameter of X (Diana); but since both methods always join/divide
to the very end I thought using cutree with a height parameter would
give me what I need. It won't.

Am I missing something?

Inline.

Bert Gunter


On Sun, Apr 14, 2019 at 4:12 PM Leszek Nowina <leekoinan at gmail.com>
wrote:
>     > asdf = data.frame(x=c(1,2,3), y=c(4,5,6), z=c(7,8,9))
>     > cutree(agnes(asdf), h=100)
>     Error in cutree(agnes(asdf), h = 100) :
>       the 'height' component of 'tree' is not sorted
(increasingly)
>     > cutree(diana(asdf), h=100)
>     Error in cutree(diana(asdf), h = 100) :
>       the 'height' component of 'tree' is not sorted
(increasingly)
>
> I'm not sure if I understand why this is the case.
>
> This is what I want: Cluster stuff by the //distances//, **not** by
> how many clusters I want to have.
>
> If two things are further from each other than X, they should go to
> different clusters. Otherwise, the same cluster.
>
> Is it unreasonable what I'm asking for?
Yes.

X and Y are at a distance 2. Y and Z are at a distance 2. X and Z are at a
distance 4. Your idea cannot be consistently applied if 3 is the cutoff for
clustering: Xand Z would have to go in different clusters but both be in
the same cluster as Y.

Maybe you need to spend some time with the literature before trying to cook
up your own notions.

Cheers,
Bert


> I image if I was to manually
> implement Agnes or Diana this would go like that: stop joining
> clusters if the smallest distance between any pair of clusters is
> larger than X (Agnes) or stop dividing clusters if the largest cluster
> has a diameter of X (Diana); but since both methods always join/divide
> to the very end I thought using cutree with a height parameter would
> give me what I need. It won't.
>
> Am I missing something?
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

I think cutree() only works on things inheriting from class 'hclust' and
agnes, et al do not produce such things.  There are as.hclust methods for
the output of agnes so you might try
    cutree( as.hclust( agnes(...)), h)
instead of
    cutree( agnes(...), h)

Bill Dunlap
TIBCO Software
wdunlap tibco.com


On Sun, Apr 14, 2019 at 4:12 PM Leszek Nowina <leekoinan at gmail.com>
wrote:
>     > asdf = data.frame(x=c(1,2,3), y=c(4,5,6), z=c(7,8,9))
>     > cutree(agnes(asdf), h=100)
>     Error in cutree(agnes(asdf), h = 100) :
>       the 'height' component of 'tree' is not sorted
(increasingly)
>     > cutree(diana(asdf), h=100)
>     Error in cutree(diana(asdf), h = 100) :
>       the 'height' component of 'tree' is not sorted
(increasingly)
>
> I'm not sure if I understand why this is the case.
>
> This is what I want: Cluster stuff by the //distances//, **not** by
> how many clusters I want to have.
>
> If two things are further from each other than X, they should go to
> different clusters. Otherwise, the same cluster.
>
> Is it unreasonable what I'm asking for? I image if I was to manually
> implement Agnes or Diana this would go like that: stop joining
> clusters if the smallest distance between any pair of clusters is
> larger than X (Agnes) or stop dividing clusters if the largest cluster
> has a diameter of X (Diana); but since both methods always join/divide
> to the very end I thought using cutree with a height parameter would
> give me what I need. It won't.
>
> Am I missing something?
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

Either way, it would seem to me that cutree(tree, h=height) could be
easily implemented as cutree(tree, k=sum(tree$height>height)+1) - why
isn't it?

Or is this not really the same, despite what seems to me?

pon., 15 kwi 2019 o 01:30 Bert Gunter <bgunter.4567 at gmail.com>
napisa?(a):
>
> Inline.
>
> Bert Gunter
>
>
> On Sun, Apr 14, 2019 at 4:12 PM Leszek Nowina <leekoinan at
gmail.com> wrote:
>>
>>     > asdf = data.frame(x=c(1,2,3), y=c(4,5,6), z=c(7,8,9))
>>     > cutree(agnes(asdf), h=100)
>>     Error in cutree(agnes(asdf), h = 100) :
>>       the 'height' component of 'tree' is not sorted
(increasingly)
>>     > cutree(diana(asdf), h=100)
>>     Error in cutree(diana(asdf), h = 100) :
>>       the 'height' component of 'tree' is not sorted
(increasingly)
>>
>> I'm not sure if I understand why this is the case.
>>
>> This is what I want: Cluster stuff by the //distances//, **not** by
>> how many clusters I want to have.
>>
>> If two things are further from each other than X, they should go to
>> different clusters. Otherwise, the same cluster.
>>
>> Is it unreasonable what I'm asking for?
>
> Yes.
>
> X and Y are at a distance 2. Y and Z are at a distance 2. X and Z are at a
distance 4. Your idea cannot be consistently applied if 3 is the cutoff for
clustering: Xand Z would have to go in different clusters but both be in the
same cluster as Y.
>
> Maybe you need to spend some time with the literature before trying to cook
up your own notions.
>
> Cheers,
> Bert
>
>
>>
>> I image if I was to manually
>> implement Agnes or Diana this would go like that: stop joining
>> clusters if the smallest distance between any pair of clusters is
>> larger than X (Agnes) or stop dividing clusters if the largest cluster
>> has a diameter of X (Diana); but since both methods always join/divide
>> to the very end I thought using cutree with a height parameter would
>> give me what I need. It won't.
>>
>> Am I missing something?
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.