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.
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.
<doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of
Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.
Functions in acepack
|ace||Alternating Conditional Expectations|
|avas||Additivity and variance stabilization for regression|
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