avas
From acepack v1.4.1
by Shawn Garbett
Additivity and variance stabilization for regression
Estimate transformations of x
and y
such that
the regression of y
on x
is approximately linear with
constant variance
 Keywords
 models
Usage
avas(x, y, wt = rep(1, nrow(x)), cat = NULL, mon = NULL, lin = NULL, circ = NULL, delrsq = 0.01, yspan = 0)
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 thex
matrix and zero to the response variable. Variables must be numeric, so a character variable should first be transformed with as.numeric() and then specified as categorical.  mon
 an optional integer vector specifying which variables are
to be transformed by monotone transformations. Positive values
in
mon
refer to columns of thex
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 thex
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 thex
matrix and zero to the response variable.  delrsq
 termination threshold. Iteration stops when Rsquared
changes by less than
delrsq
in 3 consecutive iterations (default 0.01).  yspan
 Optional window size parameter for smoothing the variance. Range is $[0,1]$. Default is 0 (cross validated choice). .5 is a reasonable alternative to try.
Value

A structure with the following components:
References
Rob Tibshirani (1987), ``Estimating optimal transformations for regression''. Journal of the American Statistical Association 83, 394ff.
Examples
library(acepack)
TWOPI < 8*atan(1)
x < runif(200,0,TWOPI)
y < exp(sin(x)+rnorm(200)/2)
a < avas(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
# From D. Wang and M. Murphy (2005), Identifying nonlinear relationships
# regression using the ACE algorithm. Journal of Applied Statistics,
# 32, 243258, adapted for avas.
X1 < runif(100)*21
X2 < runif(100)*21
X3 < runif(100)*21
X4 < runif(100)*21
# Original equation of Y:
Y < log(4 + sin(3*X1) + abs(X2) + X3^2 + X4 + .1*rnorm(100))
# Transformed version so that Y, after transformation, is a
# linear function of transforms of the X variables:
# exp(Y) = 4 + sin(3*X1) + abs(X2) + X3^2 + X4
a1 < avas(cbind(X1,X2,X3,X4),Y)
par(mfrow=c(2,1))
# For each variable, show its transform as a function of
# the original variable and the of the transform that created it,
# showing that the transform is recovered.
plot(X1,a1$tx[,1])
plot(sin(3*X1),a1$tx[,1])
plot(X2,a1$tx[,2])
plot(abs(X2),a1$tx[,2])
plot(X3,a1$tx[,3])
plot(X3^2,a1$tx[,3])
plot(X4,a1$tx[,4])
plot(X4,a1$tx[,4])
plot(Y,a1$ty)
plot(exp(Y),a1$ty)
Community examples
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