A bagging wrapper for multivariate adaptive regression
splines (MARS) via the earth
function
1 2 3 4 5 6 7 |
formula |
A formula of the form |
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 |
The function computes a Earth model for each bootstap sample.
A list with elements
fit |
a list of |
B |
the number of bootstrap samples |
call |
the function call |
x |
either |
oob |
a matrix of performance estimates for each bootstrap sample |
Max Kuhn (bagEarth.formula
is based on Ripley's nnet.formula
)
J. Friedman, “Multivariate Adaptive Regression Splines” (with discussion) (1991). Annals of Statistics, 19/1, 1-141.
1 2 3 4 5 6 7 8 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.