A bagging wrapper for multivariate adaptive regression
splines (MARS) via the earth function
Usage
## S3 method for class 'formula'
bagEarth(formula, data = NULL, B = 50,
summary = mean, keepX = TRUE,
..., subset, weights, na.action = na.omit)
## Default S3 method:
bagEarth(x, y, weights = NULL, B = 50,
summary = mean, keepX = TRUE, ...)
Arguments
formula
A formula of the form y ~ x1 + x2 + ...
x
matrix or data frame of 'x' values for examples.
y
matrix or data frame of numeric values outcomes.
weights
(case) weights for each example - if missing defaults to 1.
data
Data frame from which variables specified in 'formula' are
preferentially to be taken.
subset
An index vector specifying the cases to be used in the
training sample. (NOTE: If given, this argument must be
named.)
na.action
A function to specify the action to be taken if 'NA's are
found. The default action is for the procedure to fail. An
alternative is na.omit, which leads to rejection of cases
with missing values on any required variable. (NOTE: If
given, this argument must be named.)
B
the number of bootstrap samples
summary
a function with a single argument specifying how the bagged predictions should be summarized
keepX
a logical: should the original training data be kept?
...
arguments passed to the earth function
Details
The function computes a Earth model for each bootstap sample.
Value
A list with elements
fit
a list of B Earth fits
B
the number of bootstrap samples
call
the function call
x
either NULL or the value of x, depending on the
value of keepX
oob
a matrix of performance estimates for each bootstrap sample
Author(s)
Max Kuhn (bagEarth.formula is based on Ripley's nnet.formula)
References
J. Friedman, “Multivariate Adaptive Regression Splines” (with
discussion) (1991). Annals of Statistics, 19/1, 1-141.
See Also
earth, predict.bagEarth
Examples
## Not run:
library(mda)
library(earth)
data(trees)
fit1 <- earth(trees[,-3], trees[,3])
fit2 <- bagEarth(trees[,-3], trees[,3], B = 10)
## End(Not run)