avas(x, y, wt=<<see below>>, monotone=NULL, linear=NULL,
      categorical=NULL, circular=NULL, tolerance=0.01, span.var="cv")

x:

vector or matrix containing the explanatory variables. Columns represent variables and rows represent observations. Missing values are not accepted.

y:

vector containing the response variable. This must have the same number of observations as x. Missing values are not accepted.

wt:

vector of weights. The length of wt should be the same as the length of y. By default an unweighted regression is carried out (all weights are unity).

monotone:

integer vector specifying which variables are to be transformed by monotone transformations. Values in monotone refer to the columns of the x matrix.

linear:

integer vector specifying which variables are to be transformed by linear transformations. Positive values in linear refer to columns of the x matrix and a zero value refers to the y variable.

categorical:

integer vector specifying which variables assume categorical values. Values in categorical refer to columns of the x matrix.

circular:

integer vector specifying which variables assume circular (periodic) values. Values in circular refer to columns of the x matrix. A variable specified as circular that has values outside the range [0, 1] will be transformed using the default (general ordered) transformation.

tolerance:

termination threshold. Iteration stops when the multiple R-squared changes by less than tolerance in 3 consecutive iterations.

span.var:

fraction of the data used for smoothing the variance, span.var must lie between 0 and 1, or can be set to the character string "cv" for cross validation.

tx:

vector or matrix of the transformed x values. This is a vector when there is only one x variable.

ty:

vector like y of the transformed y values.

rsq:

the multiple R-squared value for the transformed values.

iterations:

iteration number.

span.var:

actual span used for smoothing the variance.

The chapter "Regression and Smoothing for Continous Response Data" in the S-PLUS Guide to Statistical and Mathematical Analysis.

x <- runif(200, 0, 2*pi);  y <- exp(sin(x)+rnorm(200)/2)
a <- avas(x, y)
plot(y, a$ty) # view the response transformation
plot(x, a$tx) # view the carrier transformation
plot(a$tx, a$ty) # examine the linearity of the fitted model