| avas {acepack} | R Documentation |
Estimate transformations of x and y such that
the regression of y on x is approximately linear with
constant variance
avas(x, y, wt, cat, mon, lin, circ, delrsq, yspan)
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). |
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. |
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 avas |
m |
not used in this version of avas |
yspan |
span used for smoothing the variance |
iters |
iteration number and rsq for that iteration |
niters |
number of iterations used |
Rob Tibshirani (1987), ``Estimating optimal transformations for regression''. Journal of the American Statistical Association 83, 394ff.
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