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TOOLS > DISSIMILARITIES

PURPOSE Compute dissimilarities among rows or columns of a matrix using one of various measures.

DESCRIPTION Given a matrix with n rows and m columns, the program computes either an n-by-n matrix of dissimilarities among the rows, or an m-by-m matrix of dissimilarities among the columns.

PARAMETERS
Input dataset:
Name of file containing matrix to be analyzed. Data type: Matrix.

Measure of profile similarity: (Default = 'EUCLIDEAN')
Choices are:

Euclidean
Euclidean distance: SQRT(S(xi-yi)^2) . When missing values are present, the computed distance is multiplied by n/m where n is the size of the vectors and m is the number of non-missing values.

Manhattan
City-block distance: S abs(xi-yiWhen missing values are present, the computed distance is multiplied by n/m where n is the size of the vectors and m is the number of non-missing values.

Normed SSD
Normed sum of squared differences: S(xi-yi)^2/ Sxi^2Syi^2

Non-Matches
Proportion of cases in which xi does not equal yi for all i.

Positive Non-Matches
Proportion of cases in which xi does not equal yi given that either xi > 0 or yi > 0 or both.

Compute dissimilarities among Rows or Cols (Default = COLUMNS)
If Rows, an n-by-n dissimilarity matrix representing the dissimilarity between each pair of rows is computed. If Columns an m-by-m dissimilarity matrix is computed representing the dissimilarity between each pair of columns.

(For sq. mats) Diagonal valid (Default = YES)
If No, values along the main diagonal are treated as though they were missing.

Output dataset:(Default = Dissimilarities)
Name of dataset to contain output dissimilarity matrix.

LOG FILE Dissimilarity matrix.

TIMING O(N^3).