ace {acepack}R Documentation

Alternating Conditional Expectations

Description

Uses the alternating conditional expectations algorithm to find the transformations of y and x that maximise the proportion of variation in y explained by x.

Usage

ace(x, y, wt, cat, mon, lin, circ, delrsq)

Arguments

x a matrix containing the independent variables.
y a vector containing the response variable.
wt an optional vector of weights.
cat an optional integer vector specifying which variables assume categorical values. Positive values in cat refer to columns of the x matrix and zero to the response variable.
mon an optional integer vector specifying which variables are to be transformed by monotone transformations. Positive values in mon refer to columns of the x matrix and zero to the response variable.
lin an optional integer vector specifying which variables are to be transformed by linear transformations. Positive values in lin refer to columns of the x matrix and zero to the response variable.
circ an integer vector specifying which variables assume circular (periodic) values. Positive values in circ refer to columns of the x matrix and zero to the response variable.
delrsq termination threshold. Iteration stops when R-squared changes by less than delrsq in 3 consecutive iterations (default 0.01).

Value

A structure with the following components:

x the input x matrix.
y the input y vector.
tx the transformed x values.
ty the transformed y values.
rsq the multiple R-squared value for the transformed values.
l not used in this version of ace
m not used in this version of ace

References

Breiman and Friedman, Journal of the American Statistical Association (September, 1985).

The R code is adapted from S code for avas() by Tibshirani, in the Statlib S archive; the FORTRAN is a double-precision version of FORTRAN code by Friedman and Spector in the Statlib general archive.

Examples

TWOPI <- 8*atan(1)
x <- runif(200,0,TWOPI)
y <- exp(sin(x)+rnorm(200)/2)
a <- ace(x,y)
par(mfrow=c(3,1))
plot(a$y,a$ty)  # view the response transformation
plot(a$x,a$tx)  # view the carrier transformation
plot(a$tx,a$ty) # examine the linearity of the fitted model

[Package acepack version 1.3-2.2 Index]