avNNet.default

0th

Percentile

Neural Networks Using Model Averaging

Aggregate several neural network model

Keywords
neural
Usage
## S3 method for class 'default':
avNNet(x, y, repeats = 5, bag = FALSE, allowParallel = TRUE, ...)
## S3 method for class 'formula':
avNNet(formula, data, weights, ..., 
        repeats = 5, bag = FALSE, allowParallel = TRUE,
        subset, na.action, contrasts = NULL)

## S3 method for class 'avNNet': predict(object, newdata, type = c("raw", "class", "prob"), ...)

Arguments
formula
A formula of the form class ~ x1 + x2 + ...
x
matrix or data frame of x values for examples.
y
matrix or data frame of target values for examples.
weights
(case) weights for each example -- if missing defaults to 1.
repeats
the number of neural networks with with different random number seeds
bag
a logical for bagging for each repeat
allowParallel
if a parallel backend is loaded and available, should the function use it?
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 NAs 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: I
contrasts
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
object
an object of class avNNet as returned by avNNet.
newdata
matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case.
type
Type of output, either: raw for the raw outputs, code for the predicted class or prob for the class probabilities.
...
arguments passed to nnet
Details

Following Ripley (1996), the same neural network model is fit using different random number seeds. All of the resulting models are used for prediction. For regression, the output from each network are averaged. For classification, the model scores are first averaged, then translated to predicted classes. Bagging can also be used to create the models.

If a parallel backend is registered, the foreach package is used to train the networks in parallel.

Value

  • For avNNet, an object of "avNNet" or "avNNet.formula". Items of interest in the output are:
  • modela list of the models generated from nnet
  • repeatsan echo of the model input
  • namesif any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of NULL

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

See Also

nnet, preProcess

Aliases
  • avNNet.default
  • predict.avNNet
  • avNNet.formula
  • avNNet
Examples
data(BloodBrain)
modelFit <- avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
modelFit

predict(modelFit, bbbDescr)
Documentation reproduced from package caret, version 5.15-052, License: GPL-2

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