bagFDA

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Bagged FDA

A bagging wrapper for flexible discriminant analysis (FDA) using multivariate adaptive regression splines (MARS) basis functions

Keywords
regression
Usage
bagFDA(x, ...)
## S3 method for class 'formula':
bagFDA(formula, data = NULL, B = 50, keepX = TRUE,
       ..., subset, weights, na.action = na.omit)
## S3 method for class 'default':
bagFDA(x, y, weights = NULL, B = 50, keepX = TRUE, ...)
Details

The function computes a FDA model for each bootstap sample.

Value

  • A list with elements
  • fita list of B FDA fits
  • Bthe number of bootstrap samples
  • callthe function call
  • xeither NULL or the value of x, depending on the value of keepX
  • ooba matrix of performance estimates for each bootstrap sample

References

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

See Also

fda, predict.bagFDA

Aliases
Examples
library(mlbench)
library(earth)
data(Glass)

set.seed(36)
inTrain <- sample(1:dim(Glass)[1], 150)

trainData <- Glass[ inTrain, ]
testData  <- Glass[-inTrain, ]


baggedFit <- bagFDA(Type ~ ., trainData)
baggedMat <- table(
                   predict(baggedFit, testData[, -10]),
                   testData[, 10])
   
print(baggedMat)

classAgreement(baggedMat)
Documentation reproduced from package caret, version 5.07-001, License: GPL-2

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