# filter.PCA

### Description

The `filter.PCA`

function applies the feature selection Principal Component Analysis (PCA) to a set of physical measures.

### Usage

1 | ```
filter.PCA(X,nbreVarX_,...)
``` |

### Arguments

`X` |
A matrix where each row is a physical measures. |

`nbreVarX_` |
The number of variables which represents each physical measures after the reduction by the PCA. |

`...` |
Currently ignored. |

### Details

The `filter.PCA`

function is the feature selection PCA. It converts a set of physical measures to another one with less components.

### Value

The `filter.PCA`

function returns an object which can be used with the `predict`

function to reduce each physical measure. This physical measure can be the same or another one than contained in *X*.

The value of this function is an object of class `filter.PCA`

, which is a list with the following components:

`mod` |
a model of PCA. |

`nbreVarX` |
number of component to get after the projection by the PCA of a physical measure. |

### Author(s)

Liran Lerman llerman@ulb.ac.be & Gianluca Bontempi gbonte@ulb.ac.be@ulb.ac.be & Olivier Markowitch olivier.markowitch@ulb.ac.be

### References

K. Pearson, (1901), "On Lines and Planes of Closest Fit to Systems of Points in Space", Philosophical Magazine 2 (6), pp. 559-572.

### Examples

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