predict.polymars: Polymars: multivariate adaptive polynomial spline regression

In polspline: Polynomial Spline Routines

Polymars: multivariate adaptive polynomial spline regression

Description

Produces fitted values for a model of class polymars.

Usage

## S3 method for class 'polymars'
predict(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 length of only those predictors that occur in the model, in the same order as they appear in the original data set. Similarly, x can take the form of a matrix with the number of columns equal to the number of predictors in the original data set, or shortened to the number of predictors in the model.
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 in each row is its class for the case when classify = TRUE).
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.

Author(s)

Martin O'Connor.

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, design.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)

polspline documentation built on May 29, 2024, 3:38 a.m.