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
| SPLINE and MSPLINE Transformations |
The missing portions of variables subjected to SPLINE or MSPLINE transformations are handled the same way as for OPSCORE, MONOTONE, UNTIE, and LINEAR transformations (see the previous section). The nonmissing partition is handled by first creating a B-spline basis of the specified degree with the specified knots for the nonmissing partition of the initial scaling vector and then regressing the target onto the basis. The optimally scaled vector is a linear combination of the B-spline basis vectors. Ordinary least squares regression coefficients are used. An algorithm for generating the B-spline basis is given in de Boor (1978, pp. 134–135). B-splines are both a computationally accurate and efficient way of constructing a basis for piecewise polynomials; however, they are not the most natural method of describing splines.
Consider an initial scaling vector 
and a degree-three spline with interior knots at 3.5 and 6.5. The B-spline basis for the transformation is the left matrix, and the natural piecewise polynomial spline basis is the right matrix.
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The two matrices span the same column space. The natural basis has an intercept, a linear term, a quadratic term, a cubic term, and two more terms since there are two interior knots. These terms are generated (for knot k and 
element x) by the formula 
. The indicator variable 
evaluates to 1.0 if x is greater than k and to 0.0 otherwise. If knot k had been repeated, there would be a 
term also. Notice that the fifth column makes no contribution to the curve before 3.5, makes zero contribution at 3.5 (the transformation is continuous), and makes an increasing contribution beyond 3.5. The same pattern of results holds for the last term with knot 6.5. The coefficient of the fifth column represents the change in the cubic portion of the curve after 3.5. The coefficient of the sixth column represents the change in the cubic portion of the curve after 6.5.
The numbers in the B-spline basis do not have a simple interpretation like the numbers in the natural piecewise polynomial basis. The B-spline basis has a diagonally banded structure. The band shifts one column to the right after every knot. The number of entries in each row that can potentially be nonzero is one greater than the degree. The elements within a row always sum to one. The B-spline basis is accurate because of the smallness of the numbers and the lack of extreme collinearity inherent in the natural polynomials. B-splines are efficient because PROC TRANSREG can take advantage of the sparseness of the B-spline basis when it accumulates crossproducts. The number of required multiplications and additions to accumulate the crossproduct matrix does not increase with the number of knots but does increase with the degree of the spline, so it is much more computationally efficient to increase the number of knots than to increase the degree of the polynomial.
MSPLINE transformations are handled like SPLINE transformations except that constraints are placed on the coefficients to ensure monotonicity. When the coefficients of the B-spline basis are monotonically increasing, the transformation is monotonically increasing. When the polynomial degree is two or less, monotone coefficient splines, integrated splines (Winsberg and Ramsay; 1980), and the general class of all monotone splines are equivalent.