bagFDA

From caret v6.0-70 by Max Kuhn

Bagged FDA

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

Keywords: regression

Usage

bagFDA(x, ...)
"bagFDA"(formula, data = NULL, B = 50, keepX = TRUE, ..., subset, weights, na.action = na.omit)
"bagFDA"(x, y, weights = NULL, B = 50, 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
keepX: a logical: should the original training data be kept?
...: arguments passed to the mars function

Details

The function computes a FDA model for each bootstap sample.

Value

fit: a list of B FDA 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

References

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

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)
confusionMatrix(predict(baggedFit, testData[, -10]),
                testData[, 10])

Documentation reproduced from package caret, version 6.0-70, License: GPL (>= 2)

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