Thank. See below.
Laura
2008/6/14 Christian Hennig <[EMAIL PROTECTED]>:
> What does str(ddata) give?
Class 'dist' atomic [1:130816] 69.2 117.1 145.6 179.9 195.6 ...
>
> dcent doesn't make sense as input for cluster.stats, because you need a
> dissimilarity matrix between all objects.
>
Yes I know ... I simply try to see if something was changing with different
structure of data
>
> Christian
>
> On Sat, 14 Jun 2008, Laura Poggio wrote:
>
> I am sorry I did not provide enough information.
>> I am not using img later, but data that is data.frame.
>> I wrote that img is a "image" just to explain what kind of data is coming
>> from, but the object I am using is data and it is a data.frame (checked
>> many
>> times).
>>
>> I am not using as.dist, but dist in order to calculate the distance matrix
>> among the data I have. Then the whole code I am using is:
>>
>> data <- <- as(img, "data.frame")[1:1] #(where img is an image 256x256
>> px)
>> kl <- kmeans(data, 5)
>> library(fpc)
>> ddata <- dist(data)
>> dcent <- dist(kl$centers)
>>
>> cluster.stats(ddata, kl$cluster)
>> cluster.stats(dcent, kl$cluster)
>>
>> In both cases I got the same error:
>> Error in as.dist(dmat[clustering == i, clustering == i]) : (subscript)
>> logical subscript too long
>>
>> Below the structure of the different objects is detailed below:
>> data is "'data.frame': 262144 obs. of 1 variable"
>> kl$centers is "num [1:5, 1]"
>> kl$cluster is "Named int [1:262144]"
>>
>> I hope it is more informative. I am sorry but I did not find any
>> explanation
>> for the error message I am getting.
>>
>> Thank you very much in advance
>>
>> Laura
>>
>>
>>
>> 2008/6/14 Christian Hennig <[EMAIL PROTECTED]>:
>>
>> The given information is not enough to tell you what's going on. as.dist
>>> doesn't appear in the given code and it's not clear to me what kind of
>>> object img is ("a small image" doesn't tell me what R makes of it).
>>> Also, try to read the help pages first and find out whether img is of the
>>> format that is required by the functions. And check (using str for
>>> example)
>>> whether "data" is what you expect it to be.
>>>
>>> Christian
>>>
>>>
>>> On Sat, 14 Jun 2008, Laura Poggio wrote:
>>>
>>> Thank you very much for your answer.
>>>
>>>> I tried to run the function on my data and now I am getting this message
>>>> of
>>>> error
>>>> Error in as.dist(dmat[clustering == i, clustering == i]) : (subscript)
>>>> logical subscript too long
>>>>
>>>> Below the code I am using (version2.7.0 of R with all packages updated):
>>>>
>>>> data <- <- as(img, "data.frame")[1:1] #(where img is a small image
>>>> 256
>>>> px
>>>> x 256 px)
>>>> kl <- kmeans(data, 5)
>>>> library(fpc)
>>>> cluster.stats(data, kl$cluster)
>>>>
>>>> Thank you for any hints on the reasons and meaning of the error!
>>>>
>>>> Laura
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> 2008/6/13 Christian Hennig <[EMAIL PROTECTED]>:
>>>>
>>>> Dear Laura,
>>>>
>>>>>
>>>>> Dear list,
>>>>>
>>>>> I just tried to use the function cluster.stat in the package fpc.
>>>>>> I just have a couple of questions about the syntax:
>>>>>>
>>>>>> cluster.stats(d,clustering,alt.clustering=NULL,
>>>>>> silhouette=TRUE,G2=FALSE,G3=FALSE)
>>>>>>
>>>>>> 1) the distance object (d) is an object obtained by the function
>>>>>> dist()
>>>>>> on
>>>>>> my own original matrix?
>>>>>>
>>>>>>
>>>>>> d is allowed to be an object of class dist or a dissimilarity matrix.
>>>>> The answer to your question depends on what your "original matrix" is.
>>>>> If
>>>>> it is something on which you can compute a distance by dist(), you're
>>>>> right,
>>>>> at least if dist() delivers the distance you are interested in.
>>>>>
>>>>> 2) clustering is the clusters vector as result of one of the many
>>>>>
>>>>> clustering
>>>>>> methods?
>>>>>>
>>>>>>
>>>>>> The help page tells you what clustering can be. So it could be the
>>>>> clustering/partition vector of a clustering method or it could be
>>>>> something
>>>>> else. Note that cluster.stats doesn't depend on any particular
>>>>> clustering
>>>>> method. It computes the statistics regardless of where the clustering
>>>>> vector
>>>>> comes from.
>>>>>
>>>>> Best regards,
>>>>> Christian
>>>>>
>>>>>
>>>>> Thank you very much in advance and sorry for such basic question, but
>>>>> I
>>>>>
>>>>>> did
>>>>>> not manage to clarify my mind.
>>>>>>
>>>>>> Laura
>>>>>>
>>>>>> [[alternative HTML version deleted]]
>>>>>>
>>>>>> ______________________________________________
>>>>>> R-help@r-project.org mailing list
>>>>>> 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.
>>>>>>
>>>>>>
>>>>>> *** --- ***
>>>>>>
>>>>> Christian Hennig
>>>>> University College London, Department of Statistical Science
>>>>> Gower St., London WC1E 6BT, phone +44 207 679 1698
>>>>> [EMAIL PROTECTED],
>>>>> www.homepages.ucl.ac.uk/~ucakche<http://www.homepages.ucl.ac.uk/%7Eucakche>
>>>>> <http://www.homepages.ucl.ac.uk/%7Eucakche>
>>>>> <http://www.homepages.ucl.ac.uk/%7Eucakche>
>>>>>
>>>>>
>>>>>
>>>> *** --- ***
>>> Christian Hennig
>>> University College London, Department of Statistical Science
>>> Gower St., London WC1E 6BT, phone +44 207 679 1698
>>> [EMAIL PROTECTED],
>>> www.homepages.ucl.ac.uk/~ucakche<http://www.homepages.ucl.ac.uk/%7Eucakche>
>>> <http://www.homepages.ucl.ac.uk/%7Eucakche>
>>>
>>>
>>
> *** --- ***
> Christian Hennig
> University College London, Department of Statistical Science
> Gower St., London WC1E 6BT, phone +44 207 679 1698
> [EMAIL PROTECTED],
> www.homepages.ucl.ac.uk/~ucakche<http://www.homepages.ucl.ac.uk/%7Eucakche>
>
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