caret (version 7.0-1)

bagEarth: Bagged Earth

Description

A bagging wrapper for multivariate adaptive regression splines (MARS) via the earth function

Usage

bagEarth(x, ...)
# S3 method for default
bagEarth(x, y, weights = NULL, B = 50, summary = mean, keepX = TRUE, ...)
# S3 method for formula
bagEarth(
  formula,
  data = NULL,
  B = 50,
  summary = mean,
  keepX = TRUE,
  ...,
  subset,
  weights = NULL,
  na.action = na.omit
)
# S3 method for bagEarth
print(x, ...)

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

Arguments

x

matrix or data frame of 'x' values for examples.

...

arguments passed to the earth function

y

matrix or data frame of numeric values outcomes.

weights

(case) weights for each example - if missing defaults to 1.

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?

formula

A formula of the form y ~ x1 + x2 + ...

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.)

Author

Max Kuhn (bagEarth.formula is based on Ripley's nnet.formula)

Details

The function computes a Earth model for each bootstap sample.

References

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

See Also

earth, predict.bagEarth

Examples

if (FALSE) {
library(mda)
library(earth)
data(trees)
fit1 <- earth(x = trees[,-3], y = trees[,3])
set.seed(2189)
fit2 <- bagEarth(x = trees[,-3], y = trees[,3], B = 10)
}