The TRANSREG Procedure
- Overview
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Getting Started
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Syntax
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DetailsModel Statement UsageBox-Cox TransformationsUsing Splines and KnotsScoring Spline VariablesLinear and Nonlinear Regression FunctionsSimultaneously Fitting Two Regression FunctionsPenalized B-SplinesSmoothing SplinesSmoothing Splines Changes and EnhancementsIteration History Changes and EnhancementsANOVA CodingsMissing ValuesMissing Values, UNTIE, and Hypothesis TestsControlling the Number of IterationsUsing the REITERATE Algorithm OptionAvoiding Constant TransformationsConstant VariablesCharacter OPSCORE VariablesConvergence and DegeneraciesImplicit and Explicit InterceptsPassive ObservationsPoint ModelsRedundancy AnalysisOptimal ScalingOPSCORE, MONOTONE, UNTIE, and LINEAR TransformationsSPLINE and MSPLINE TransformationsSpecifying the Number of KnotsSPLINE, BSPLINE, and PSPLINE ComparisonsHypothesis TestsOutput Data SetOUTTEST= Output Data SetComputational ResourcesUnbalanced ANOVA without CLASS VariablesHypothesis Tests for Simple Univariate ModelsHypothesis Tests with Monotonicity ConstraintsHypothesis Tests with Dependent Variable TransformationsHypothesis Tests with One-Way ANOVAUsing the DESIGN Output OptionDiscrete Choice Experiments: DESIGN, NORESTORE, NOZEROCenteringDisplayed OutputODS Table NamesODS 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:
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ordinary regression and ANOVA
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metric and nonmetric conjoint analysis (Green and Wind 1975; De Leeuw, Young, and Takane 1976)
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linear models with Box-Cox (1964) transformations of the dependent variables
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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
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metric and nonmetric vector and ideal point preference mapping (Carroll 1972)
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simple, multiple, and multivariate regression with variable transformations (Young, de Leeuw, and Takane 1976; Winsberg and Ramsay 1980; Breiman and Friedman 1985)
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redundancy analysis (Stewart and Love 1968) with variable transformations (Israels 1984)
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canonical correlation analysis with variable transformations (Van der Burg and de Leeuw 1983)
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response surface regression (Myers 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:
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transform nominal variables by scoring the categories to minimize squared error (Fisher 1938), or treat nominal variables as classification variables
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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 .
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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); Hastie and Tibshirani (1986). (These are just a few of the many relevant sources.)