design.polymars: Polymars: multivariate adaptive polynomial spline regression In polspline: Polynomial Spline Routines

 design.polymars R Documentation

Polymars: multivariate adaptive polynomial spline regression

Description

Produces a design matrux for a model of class `polymars`.

Usage

``design.polymars(object, x) ``

Arguments

 `object` object of the class `polymars`, typically the result of `polymars`. `x` the predictor values at which the design matrix 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.

Value

The design matrix corresponding to the fitted `polymars` model.

Author(s)

Charles Kooperberg

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.

`polymars`, `plot.polymars`, `predict.polymars`, `summary.polymars`.

Examples

``````data(state)
state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1)
desmat <- design.polymars(state.pm, state.x77)
# compute traditional summary of the fit for the first class
summary(lm(((state.region=="Northeast")*1) ~ desmat -1))
``````

polspline documentation built on Oct. 27, 2023, 1:07 a.m.