The TRANSREG Procedure
- Overview
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Getting Started
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Syntax
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Details
Model Statement Usage Box-Cox Transformations Using Splines and Knots Scoring Spline Variables Linear and Nonlinear Regression Functions Simultaneously Fitting Two Regression Functions Penalized B-Splines Smoothing Splines Smoothing Splines Changes and Enhancements Iteration History Changes and Enhancements ANOVA Codings Missing Values Missing Values, UNTIE, and Hypothesis Tests Controlling the Number of Iterations Using the REITERATE Algorithm Option Avoiding Constant Transformations Constant Variables Character OPSCORE Variables Convergence and Degeneracies Implicit and Explicit Intercepts Passive Observations Point Models Redundancy Analysis Optimal Scaling OPSCORE, MONOTONE, UNTIE, and LINEAR Transformations SPLINE and MSPLINE Transformations Specifying the Number of Knots SPLINE, BSPLINE, and PSPLINE Comparisons Hypothesis Tests Output Data Set OUTTEST= Output Data Set Computational Resources Unbalanced ANOVA without CLASS Variables Hypothesis Tests for Simple Univariate Models Hypothesis Tests with Monotonicity Constraints Hypothesis Tests with Dependent Variable Transformations Hypothesis Tests with One-Way ANOVA Using the DESIGN Output Option Discrete Choice Experiments: DESIGN, NORESTORE, NOZERO Centering Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Optimal Scaling |
An alternating least squares optimal scaling algorithm can be divided into two major stages. The first major stage estimates the parameters of the linear model. These parameters are used to create the predicted values or target for each variable that can be transformed. Each target minimizes squared error (as explained in the discussion of the algorithms in SAS Technical Report R-108). The definition of the target depends on many factors, such as whether a variable is independent or dependent, which algorithm is used (for example, regression, redundancy, CANALS, or principal components), and so on. The definition of the target is independent of the transformation family you specify for the variable. However, the target values for a variable typically do not fit the prescribed transformation family for the variable. They might not have the right category structure; they might not have the right order; they might not be a linear combination of the columns of a B-spline basis; and so on.
The second major stage is optimal scaling. Optimal scaling can be defined as a possibly constrained, least squares regression problem. When you specify an optimal transformation, or when missing data are estimated for any variable, the full representation of the variable is not simply a vector; it is a matrix with more than one column. The optimal scaling phase finds the vector that is a linear combination of the columns of this matrix that is closest to the target (in terms of minimum squared error), among those that do not violate any of the constraints imposed by the transformation family. Optimal scaling methods are independent of the data analysis method that generated the target. In all cases, optimal scaling can be accomplished by creating a design matrix based on the original scaling of the variable and the transformation family specified for that variable. The optimally scaled variable is a linear combination of the columns of the design matrix. The coefficients of the linear combination are found by using (possibly constrained) least squares. Many optimal scaling problems are solved without actually constructing design and projection matrices. The next two sections describe the algorithms used by PROC TRANSREG for optimal scaling. The first section discusses optimal scaling for OPSCORE, MONOTONE, UNTIE, and LINEAR transformations, including how missing values are handled. The second section addresses SPLINE and MSPLINE transformations.