SAS/STAT Software

TRANSREG Procedure

The TRANSREG (transformation regression) procedure fits linear models, optionally with smooth, spline, Box-Cox, and other nonlinear transformations of the variables. The following are highlights of the TRANSREG procedure's features:

  • enables you to fit linear models including:
    • 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
  • enables you to use a data set that can contain variables measured on nominal, ordinal, interval, and ratio scales; you can specify any mix of these variable types for the dependent and independent variables
  • transform nominal variables by scoring the categories to minimize squared error (Fisher 1938), or treat nominal variables as classification variables
  • enables you to 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.
  • enables you to 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.
  • fits a curve through a scatter plot or fit multiple curves, one for each level of a classification variable
  • enables you to constrain the functions to be parallel or monotone or have the same intercept
  • enables you to code experimental designs and classification variables prior to their use in other analyses
  • perform sweighted estimation
  • generates output data sets including
    • ANOVA results
    • regression tables
    • conjoint analysis part-worth utilities
    • coefficients
    • marginal means
    • original and transformed variables, predicted values, residuals, scores, and more
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • automatically creates graphs by using ODS Graphics

For further details see the TRANSREG Procedure