bagFDA
From caret v4.75
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, ...)
## 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, ...)
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. (N
- B
- the numebr 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
- A list with elements
fit a list of B
FDA fitsB the number of bootstrap samples call the function call x either NULL
or the value ofx
, depending on the value ofkeepX
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.
See Also
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)
Community examples
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