# preProcess

From caret v4.25
by Max Kuhn

##### Pre-Processing of Predictors

Pre-processing transformation (centering, scaling etc) can be estimated from the training data and applied to any data set with the same variables.

- Keywords
- utilities

##### Usage

`preProcess(x, ...)`## S3 method for class 'default':
preProcess(x, method = c("center", "scale"),
thresh = 0.95, na.remove = TRUE, ...)

## S3 method for class 'preProcess':
predict(object, newdata, ...)

##### Arguments

- x
- a matrix or data frame
- method
- a character vector specifying the type of processing. Possible values are "center", "scale", "pca" and "spartialSign"
- thresh
- a cutoff for the cumulative percent of variance to be retained by PCA
- na.remove
- a logical; should missing values be removed from the calculations?
- object
- an object of class
`preProcess`

- newdata
- a matrix or data frame of new data to be pre-processed
- ...
- Additional arguments (currently this argument is not used)

##### Details

The operations are applied in this order: centering, scaling, PCA and spatial sign. If PCA is requested but scaling is not, the values will still be scaled.

The function will throw an error of any variables in `x`

has less than two unique values.

##### Value

`preProcess`

results in a list with elementscall the function call dim the dimensions of `x`

mean a vector of means (if centering was requested) std a vector of standard deviations (if scaling or PCA was requested) rotation a matrix of eigenvectors if PCA was requested method the value of `method`

thresh the value of `thresh`

numComp the number of principal components required of capture the specified amount of variance

##### References

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (

##### See Also

##### Examples

```
data(BloodBrain)
# one variable has one unique value
preProc <- preProcess(bbbDescr[1:100,])
preProc <- preProcess(bbbDescr[1:100,-3])
training <- predict(preProc, bbbDescr[1:100,-3])
test <- predict(preProc, bbbDescr[101:208,-3])
```

*Documentation reproduced from package caret, version 4.25, License: GPL-2*

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