polspline (version 1.0.4)

predict.polymars: Polymars: multivariate adaptive polynomial spline regression

Description

Produces fitted values for a model of class polymars.

Usage

predict.polymars(object, x, classify = FALSE, intercept, ...)

Arguments

object

object of the class polymars, typically the result of polymars.

x

the predictor values at which the fitted values will be computed. The predictor values can be in a number of formats. It can take the form of a vector of length equal to the number of predictors in the original data set or it can be shortened to the l

classify

if the original call to polymars was for a classification problem and you would like the classifications (class predictions), set this option equal to TRUE. Otherwise the function returns a response column for each class (the highest values

intercept

Setting intercept equal to FALSE evaluates the object without intercept. The intercept may also be given any numerical value which overrides the fitted coefficient from the object. The defualt is TRUE.

...

other arguments are ignored.

Value

• A matrix of fitted values. The number of columns in the returned matrix equals the number of responses in the original call to polymars.

References

Charles Kooperberg, Smarajit Bose, and Charles J. Stone (1997). Polychotomous regression. Journal of the American Statistical Association, 92, 117--127.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371--1470.

See Also

polymars, plot.polymars, summary.polymars.

Examples

data(state)
state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1)
table(predict(state.pm, x = state.x77, classify = TRUE), state.region)