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The MDS Procedure |

- 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

Depending on what options are specified, PROC MDS may also display the following tables:

- 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
- Number of Nonmissing Data
- Weight of the matrix or row, allowing for 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

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