Dear List: I have a question regarding an MDS procedure that I am accustomed to using. I have searched around the archives a bit and the help doc and still need a little assistance. The package isoMDS is what I need to perform the non-metric scaling, but I am working with similarity matrices, not dissimilarities. The question may end up being resolved simply.
Here is a bit of substantive background. I am working on a technique where individuals organize items based on how similar they perceive the items to be. For example, assume there are 10 items. Person 1 might group items 1,2,3,4,5 in group 1 and the others in group 2. I then turn this grouping into a binomial similarity matrix. The following is a sample matrix for Person 1 based on this hypothetical grouping. The off diagonals are the similar items with the 1's representing similarities. a b c d e f g h i j a 1 1 1 1 1 0 0 0 0 0 b 1 1 1 1 1 0 0 0 0 0 c 1 1 1 1 1 0 0 0 0 0 d 1 1 1 1 1 0 0 0 0 0 e 1 1 1 1 1 0 0 0 0 0 f 0 0 0 0 0 1 1 1 1 1 g 0 0 0 0 0 1 1 1 1 1 h 0 0 0 0 0 1 1 1 1 1 i 0 0 0 0 0 1 1 1 1 1 j 0 0 0 0 0 1 1 1 1 1 Each of these individual matrices are summed over individuals. So, in this summed matrix diagonal elements represent the total number of participants and the off-diagonals represent the number of times an item was viewed as being similar by members of the group (obviously the matrix is symmetric below the diagonal). So, a "4" in row 'a' column 'c' means that these items were viewed as being similar by 4 people. A sample total matrix is at the bottom of this email describing the perceived similarities of 10 items across 4 individuals. It is this total matrix that I end up working with in the MDS. I have previously worked in systat where I run the MDS and specify the matrix as a similarity matrix. I then take the resulting data from the MDS and perform a k-means cluster analysis to identify which items belong to a particular cluster, centroids, etc. So, here are my questions. 1) Can isoMDS work only with dissimilarities? Or, is there a way that it can perform the analysis on the similarity matrix as I have described it? 2) If I cannot perform the analysis on the similarity matrix, how can I turn this matrix into a dissimilarity matrix necessary? I am less familiar with this matrix and how it would be constructed? Thanks for any help offered, Harold a b c d e f g h i j a 4 2 4 3 3 2 0 0 0 0 b 2 4 2 3 1 0 2 2 2 2 c 4 2 4 3 3 2 0 0 0 0 d 3 3 3 4 2 1 1 1 1 1 e 3 1 3 2 4 3 1 1 1 1 f 2 0 2 1 3 4 2 2 2 2 g 0 2 0 1 1 2 4 4 4 4 h 0 2 0 1 1 2 4 4 4 4 i 0 2 0 1 1 2 4 4 4 4 j 0 2 0 1 1 2 4 4 4 4 [[alternative HTML version deleted]] ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html