mars: Multivariate Adaptive Regression Splines

Description

Multivariate adaptive regression splines.

Usage

mars(x, y, w, wp, degree, nk, penalty, thresh, prune, trace.mars,
     forward.step, prevfit, ...)

Value

An object of class "mars", which is a list with the following components:

call: call used to mars.
all.terms: term numbers in full model. 1 is the constant term. Remaining terms are in pairs (2 3, 4 5, and so on). all.terms indicates nonsingular set of terms.
selected.terms: term numbers in selected model.
penalty: the input penalty value.
degree: the input degree value.
thresh: the input threshold value.
gcv: gcv of chosen model.
factor: matrix with $i j$ -th element equal to 1 if term $i$ has a factor of the form $x_{j} > c$ , equal to $- 1$ if term $i$ has a factor of the form $x_{j} \leq c$ , and to 0 if $x j$ is not in term $i$ .
cuts: matrix with $i j$ -th element equal to the cut point $c$ for variable $j$ in term $i$ .
residuals: residuals from fit.
fitted: fitted values from fit.
lenb: length of full model.
coefficients: least squares coefficients for final model.
x: a matrix of basis functions obtained from the input x matrix.

Arguments

x: a matrix containing the independent variables.
y: a vector containing the response variable, or in the case of multiple responses, a matrix whose columns are the response values for each variable.
w: an optional vector of observation weights (currently ignored).
wp: an optional vector of response weights.
degree: an optional integer specifying maximum interaction degree (default is 1).
nk: an optional integer specifying the maximum number of model terms.
penalty: an optional value specifying the cost per degree of freedom charge (default is 2).
thresh: an optional value specifying forward stepwise stopping threshold (default is 0.001).
prune: an optional logical value specifying whether the model should be pruned in a backward stepwise fashion (default is TRUE).
trace.mars: an optional logical value specifying whether info should be printed along the way (default is FALSE).
forward.step: an optional logical value specifying whether forward stepwise process should be carried out (default is TRUE).
prevfit: optional data structure from previous fit. To see the effect of changing the penalty parameter, one can use prevfit with forward.step = FALSE.
...: further arguments to be passed to or from methods.

Author

Trevor Hastie and Robert Tibshirani

References

J. Friedman, ``Multivariate Adaptive Regression Splines'' (with discussion) (1991). Annals of Statistics, 19/1, 1--141.

Examples

Run this code

data(trees)
fit1 <- mars(trees[,-3], trees[3])
showcuts <- function(obj)
{
  tmp <- obj$cuts[obj$sel, ]
  dimnames(tmp) <- list(NULL, names(trees)[-3])
  tmp
}
showcuts(fit1)

## examine the fitted functions
par(mfrow=c(1,2), pty="s")
Xp <- matrix(sapply(trees[1:2], mean), nrow(trees), 2, byrow=TRUE)
for(i in 1:2) {
  xr <- sapply(trees, range)
  Xp1 <- Xp; Xp1[,i] <- seq(xr[1,i], xr[2,i], len=nrow(trees))
  Xf <- predict(fit1, Xp1)
  plot(Xp1[ ,i], Xf, xlab=names(trees)[i], ylab="", type="l")
}

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