Day 6 - Team G - Vivek Bakshi

Permap

Permap is an interactive computer program.  It offers both metric (ratio and interval) and nonmetric (ordinal, ratio + bounds, interval + bounds) MDS techniques.  It solves problems in up to eight dimensional space and allows boundary conditions to be imposed on the solution.  In the technical jargon, Permap treats "weighted, incomplete, one-mode, two-way" or "weighted, incomplete, two-mode, two-way" data sets.  Other jargon would say it handles weighted, symmetric, incomplete, triangular or rectangular data sets.  The word “weighted” means each data point can have its own multiplier that reflects in some way the importance or reliability of the point.  The word “symmetric” means that Permap assumes that the (i, j) proximity value equals the (j, i) proximity value, and “incomplete” means that it can handle missing data.  The one-mode, two-way and square references indicate that Permap can analyze a matrix of proximity information between several objects, and the two-mode, two-way and rectangular references means it can analyze objects each of which are specified by an array of attributes.

Permap's data files are based on freeform data entry.  This means that keyword identifiers announce the presence of various data elements and that these data types can be present in the file in any order.  Comment lines can be placed freely throughout the data file as long as they are not placed between a keyword and its following data.  All optional information is covered by default values.  This means that if you choose not to use weights then they need not be mentioned in the data file. 

Dissimilarity data can be in either a lower-left half-matrix, as shown above, or in a whole matrix format.  If a whole square matrix is entered, the upper-right triangle is ignored.  Entering a square matrix is allowed simply to facilitate data interchange with other programs such as Excel.   

Badness Functions

 A badness function is simply a definition of what it is that makes the positions of a pair of objects be bad.  Fundamentally, the object pair should be separated by a distance that is consistent with the pair's dissimilarity.  Thus, the badness Bij should involve a measure of the mismatch between dij and Dij.  There are several valid ways of defining this mismatch.

All Available Vectors 

This option includes all attributes, even those that were not used in making the map.  This option facilitates doing "grounded research" where, for instance, you want the best possible synthesis of all available data, and you are willing to give up generalizability in order to squeeze the last bit of information out of your data.  If you have a low stress map with a good interpretation, and you are interested in evaluating additional attributes, then use this option

By:

Vivek Bakshi