Multidimensional Unfolding Options
The Options dialog box allows you to select the initial configuration style, specify iteration and convergence criteria, and set the penalty term for stress.
Initial Configuration. Choose one of the following alternatives:
- Classical. The rectangular proximity matrix is used to supplement the intra-blocks (values between rows and between columns) of the complete symmetrical MDS matrix. Once the complete matrix is formed, a classical scaling solution is used as the initial configuration. The intra-blocks can be filled via imputation using the triangle inequality or Spearman distances.
- Ross-Cliff. The Ross-Cliff start uses the results of a singular value decomposition on the double centered and squared proximity matrix as the initial values for the row and column objects.
- Correspondence. The correspondence start uses the results of a correspondence analysis on the reversed data (similarities instead of dissimilarities), with symmetric normalization of row and column scores.
- Centroids. The procedure starts by positioning the row objects in the configuration using an eigenvalue decomposition. Then the column objects are positioned at the centroid of the specified choices. For the number of choices, specify a positive integer between 1 and the number of proximities variables.
- Multiple random starts. Solutions are computed for several initial configurations chosen at random, and the one with the lowest penalized stress is shown as the best solution.
- Custom. You can select variables that contain the coordinates of your own initial configuration. The number of variables selected should equal the maximum number of dimensions specified, with the first variable corresponding to coordinates on dimension 1, the second variable corresponding to coordinates on dimension 2, and so on. The number of cases in each variable should equal the combined number of row and column objects. The row and column coordinates should be stacked, with the column coordinates following the row coordinates.
Iteration Criteria. Specify the iteration criteria values.
- Stress convergence. The algorithm will stop iterating when the relative difference in consecutive penalized stress values is less than the number specified here, which must be non-negative.
- Minimum stress. The algorithm will stop when the penalized stress falls below the number specified here, which must be non-negative.
- Maximum iterations. The algorithm will perform the number of iterations specified here unless one of the above criteria is satisfied first.
Penalty Term. The algorithm attempts to minimize penalized stress, a goodness-of-fit measure equal to the product of Kruskal's Stress-I and a penalty term based on the coefficient of variation of the transformed proximities. These controls allow you to set the strength and range of the penalty term.
- Strength. The smaller the value of the strength parameter, the stronger the penalty. Specify a value between 0.0 and 1.0.
- Range. This parameter sets the moment at which the penalty becomes active. If set to 0.0, the penalty is inactive. Increasing the value causes the algorithm to search for a solution with greater variation among the transformed proximities. Specify a non-negative value.
To Specify Multidimensional Unfolding Options
This feature requires SPSS® Statistics Professional Edition or the Categories option.
- From the menus choose:
- In the Multidimensional Unfolding dialog box, click Options.