Polymars: multivariate adaptive polynomial spline regression
Produces fitted values for a model of class polymars.
## S3 method for class 'polymars' predict(object, x, classify = FALSE, intercept, ...)
object |
object of the class |
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, |
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 |
intercept |
Setting intercept equal to |
... |
other arguments are ignored. |
A matrix of fitted values.
The number of columns in the
returned matrix equals the number of responses in the original call to polymars.
Martin O'Connor.
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) table(predict(state.pm, x = state.x77, classify = TRUE), state.region)
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