Hello!
I would suggest using some form of imputations, such as MICE package (http://web.inter.nl.net/users/S.van.Buuren/mi/hmtl/mice.htm) or similar (I heard that this can be also done with aregImpute function in the Hmisc package, although I have not tried it) to fill in the NA's. Then you can use k-means or any technique you which, since now you have a complete data-frame. However, for more reliable results, it is best to repeat imputations and analysis several times.
I hope this helps!
Ales Ziberna
----- Original Message ----- From: <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>; <[email protected]>
Sent: Monday, April 04, 2005 5:23 AM
Subject: [R] Using kmeans given cluster centroids and data with NAs
Hello Tom,
Thanks for the reply.
Unfortunately I do have many NAs in my data as not all vertical temperature profiles penetrated to the same depth level. In fact if I simply use na.omit my data matrix is reduced from 4977 to 480 observations, so such a simple solution is not very helpful I'm afraid. Any other ideas?
Cheers, SB
-----Original Message----- From: Mulholland, Tom [mailto:[EMAIL PROTECTED] Sent: Thursday, 31 March 2005 2:15 PM To: Bestley, Sophie (Marine, Hobart); [email protected] Subject: RE: [R] Using kmeans given cluster centroids and data with NAs
Does ?na.omit help
x <- kmeans(na.omit(data),centres)
of course if you have too many NAs you need to be sure that their removal does not unduly influence the results.
Although I am a bit confused as I thought that agnes did not allow NAs. I assume that you are running an alternative clustering method using the results of the first process as the starting point for the partitioning process and are thus using the same initial data.
Tom
-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Sent: Thursday, 31 March 2005 11:33 AM To: [email protected] Subject: [R] Using kmeans given cluster centroids and data with NAs
Hello,
I have used the functions agnes and cutree to cluster my data (4977 objects x 22 variables) into 8 clusters. I would like to refine the solution using a k-means or similar algorithm, setting the initial cluster centres as the group means from agnes. However my data matrix has NA's in it and the function kmeans does not appear to accept this?
> dim(centres) [1] 8 22
> dim(data) [1] 4977 22
> x <- kmeans(data,centres) Error in kmeans(data, centres) : NA/NaN/Inf in foreign function call (arg 1)
I have looked extensively through the mail archives but cannot find if/where someone has provided the answer.
Thanks in advance, SB
Sophie Bestley Pelagic Fisheries and Ecosystems CSIRO Marine Research GPO Box 1538 Hobart, Tasmania 7001 AUSTRALIA
Phone: +61 3 6232 5048 Fax: +61 3 6232 5053 Email: [EMAIL PROTECTED] Website: http://www.marine.csiro.au
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