# pcaNNet

##### Neural Networks with a Principal Component Step

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

- Keywords
- neural

##### Usage

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

# S3 method for default
pcaNNet(x, y, thresh = 0.99, ...)

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

# S3 method for 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:

the output from `preProcess`

the model
generated from `nnet`

if 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

##### Examples

```
# NOT RUN {
data(BloodBrain)
modelFit <- pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE)
modelFit
predict(modelFit, bbbDescr[, 1:10])
# }
```

*Documentation reproduced from package caret, version 6.0-80, License: GPL (>= 2)*