Description Usage Arguments Details Value Author(s) References See Also Examples

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

function

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
bagEarth(x, ...)
## Default S3 method:
bagEarth(x, y, weights = NULL, B = 50,
summary = mean, keepX = TRUE, ...)
## S3 method for class 'formula'
bagEarth(formula, data = NULL, B = 50,
summary = mean, keepX = TRUE, ..., subset, weights = NULL,
na.action = na.omit)
## S3 method for class 'bagEarth'
print(x, ...)
``` |

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

`...` |
arguments passed to the |

`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 |

`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.) |

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 9 |

caret documentation built on May 2, 2019, 5:39 a.m.

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