Introduction to Regression Procedures

Regression with Transformations: The TRANSREG Procedure

PROC TRANSREG fits linear models to data. In addition, PROC TRANSREG can find nonlinear transformations of the data and fit a linear model to the transformed data. This is in contrast to PROC REG and PROC GLM, which fit linear models to data, and PROC NLIN, which fits nonlinear models to data. PROC TRANSREG fits a variety of models, including the following:

  • ordinary regression and ANOVA

  • metric and nonmetric conjoint analysis

  • metric and nonmetric vector and ideal point preference mapping

  • simple, multiple, and multivariate regression with optional variable transformations

  • redundancy analysis with optional variable transformations

  • canonical correlation analysis with optional variable transformations

  • simple and multiple regression models with a Box-Cox (1964) transformation of the dependent variable

  • regression models with penalized B-splines (Eilers and Marx, 1996)