The MDS Procedure

Displayed Output

• Iteration number

• Type of iteration:

  Initial

  initial configuration

  Monotone

  monotone transformation

  Gau-New

  Gauss-Newton step

  Lev-Mar

  Levenberg-Marquardt step

• Badness-of-Fit Criterion

• Change in Criterion

• Convergence Measures:

  Monotone

  the Euclidean norm of the change in the optimally scaled data divided by the Euclidean norm of the optimally scaled data, averaged across partitions

  Gradient

  the multiple correlation of the Jacobian matrix with the residual vector, uncorrected for the mean

• Data Matrix and possibly Weight Matrix for each subject

• Eigenvalues from the computation of the initial coordinates

• Sum of Data Weights and Pooled Data Matrix computed during initialization with INAV=DATA

• Configuration, the estimated coordinates of the objects

• Dimension Coefficients

• A table of transformation parameters, including one or more of the following:

  • Intercept

  • Slope

  • Power

• A table of fit statistics for each matrix and possibly each row, including the following:

  • Number of Nonmissing Data

  • Weight of the matrix or row, permitting both observation weights and standardization factors

  • Badness-of-Fit Criterion

  • Distance Correlation computed between the distances and data with optimal transformation

  • Uncorrected Distance Correlation not corrected for the mean

  • Fit Correlation computed after applying the FIT= transformation to both distances and data

  • Uncorrected Fit Correlation not corrected for the mean