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
Overview: TRANSREG Procedure
The TRANSREG (transformation regression) procedure fits linear models, optionally with smooth, spline, Box-Cox, and other nonlinear transformations of the variables. You can use PROC TRANSREG to fit a curve through a scatter plot or fit multiple curves, one for each level of a classification variable. You can also constrain the functions to be parallel or monotone or have the same intercept. PROC TRANSREG can be used to code experimental designs and classification variables prior to their use in other analyses.
The TRANSREG procedure fits many types of linear models, including the following:
ordinary regression and ANOVA
metric and nonmetric conjoint analysis (Green and Wind; 1975; de Leeuw, Young, and Takane; 1976)
linear models with Box-Cox (1964) transformations of the dependent variables
regression with a smooth (Reinsch; 1967), spline (de Boor; 1978; van Rijckevorsel; 1982), monotone spline (Winsberg and Ramsay; 1980), or penalized B-spline (Eilers and Marx; 1996) fit function
metric and nonmetric vector and ideal point preference mapping (Carroll; 1972)
simple, multiple, and multivariate regression with variable transformations (Young, de Leeuw, and Takane; 1976; Winsberg and Ramsay; 1980; Breiman and Friedman; 1985)
redundancy analysis (Stewart and Love; 1968) with variable transformations (Israels; 1984)
canonical correlation analysis with variable transformations (van der Burg and de Leeuw; 1983)
response surface regression (Meyers; 1976; Khuri and Cornell; 1987) with variable transformations
The data set can contain variables measured on nominal, ordinal, interval, and ratio scales (Siegel; 1956). You can specify any mix of these variable types for the dependent and independent variables. PROC TRANSREG can do the following:
transform nominal variables by scoring the categories to minimize squared error (Fisher; 1938), or treat nominal variables as classification variables
transform ordinal variables by monotonically scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and squared error is minimized. Ties can be optimally untied or left tied (Kruskal; 1964). Ordinal variables can also be transformed to ranks.
transform interval and ratio scale of measurement variables linearly or nonlinearly with spline (de Boor; 1978; van Rijckevorsel; 1982), monotone spline (Winsberg and Ramsay; 1980), penalized B-spline (Eilers and Marx; 1996), smooth (Reinsch; 1967), or Box-Cox (Box and Cox; 1964) transformations. In addition, logarithmic, exponential, power, logit, and inverse trigonometric sine transformations are available.
Transformations produced by the PROC TRANSREG multiple regression algorithm, requesting spline transformations, are often similar to transformations produced by the ACE smooth regression method of Breiman and Friedman (1985). However, ACE does not explicitly optimize a loss function (de Leeuw; 1986), while PROC TRANSREG explicitly minimizes a squared-error criterion.
PROC TRANSREG extends the ordinary general linear model by providing optimal variable transformations that are iteratively derived. PROC TRANSREG iterates until convergence, alternating two major steps: finding least squares estimates of the model parameters given the current scoring of the data, and finding least squares estimates of the scoring parameters given the current set of model parameters. This is called the method of alternating least squares (Young; 1981).
For more background on alternating least squares optimal scaling methods and transformation regression methods, see Young, de Leeuw, and Takane (1976), Winsberg and Ramsay (1980), Young (1981), Gifi (1990), Schiffman, Reynolds, and Young (1981), van der Burg and de Leeuw (1983), Israels (1984), Breiman and Friedman (1985), and Hastie and Tibshirani (1986). (These are just a few of the many relevant sources.)