ACE and AVAS for Selecting Multiple Regression Transformations

Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. ]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. ]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.


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install.packages("acepack")

1.4.1 by Shawn Garbett, 2 years ago


Browse source code at https://github.com/cran/acepack


Authors: Phil Spector , Jerome Friedman , Robert Tibshirani , Thomas Lumley , Shawn Garbett , Jonathan Baron


Documentation:   PDF Manual  


Task views: Statistics for the Social Sciences


MIT + file LICENSE license


Suggests testthat


Imported by Hmisc.

Depended on by nlts.


See at CRAN