Polymars: multivariate adaptive polynomial spline regression
Produces a design matrux for a model of class polymars.
design.polymars(object, x)
object |
object of the class |
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, |
The design matrix corresponding to the fitted polymars model.
Charles Kooperberg
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.
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))
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