pcaNNet {caret} | R Documentation

## Neural Networks with a Principal Component Step

### Description

Run PCA on a dataset, then use it in a neural network model

### Usage

    pcaNNet(x, ...)

    ## S3 method for class 'formula'
    pcaNNet(formula, data, weights, ..., thresh = 0.99, subset,
      na.action, contrasts = NULL)

    ## Default S3 method:
    pcaNNet(x, y, thresh = 0.99, ...)

    ## S3 method for class 'pcaNNet'
    print(x, ...)

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

### Arguments

`x` | matrix or data frame of `x` values for examples.
`...` | arguments passed to `nnet`, such as `size`, `decay`, etc.
`formula` | A formula of the form `class ~ x1 + x2 + ...{}`
`data` | Data frame from which variables specified in `formula` are preferentially to be taken.
`weights` | (case) weights for each example – if missing defaults to 1.
`thresh` | a threshold for the cumulative proportion of variance to capture from the PCA analysis. For example, to retain enough PCA components to capture 95 percent of variation, set `thresh = .95`
`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.)
`contrasts` | a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
`y` | matrix or data frame of target values for examples.
`object` | an object of class `pcaNNet` as returned by `pcaNNet`.
`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

### Details

The function first will run principal component analysis on the data. The cumulative percentage of variance is computed for each principal component. The function uses the `thresh` argument to determine how many components must be retained to capture this amount of variance in the predictors.

The principal components are then used in a neural network model.

When predicting samples, the new data are similarly transformed using the information from the PCA analysis on the training data and then predicted.

Because the variance of each predictor is used in the PCA analysis, the code does a quick check to make sure that each predictor has at least two distinct values. If a predictor has one unique value, it is removed prior to the analysis.

### Value

For `pcaNNet`, an object of `"pcaNNet"` or `"pcaNNet.formula"`. Items of interest in the output are:

`pc` | the output from `preProcess`
`model` | the model generated from `nnet`
`names` | if any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of `NULL`

### Author(s)

These are heavily based on the `nnet` code from Brian Ripley.

### References

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

### See Also

`nnet`, `preProcess`

### Examples

    data(BloodBrain)
    modelFit <- pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE)
    modelFit

    predict(modelFit, bbbDescr[, 1:10])

[Package caret version 6.0-76 Index]