pca | R Documentation |

Calculate the principal component analysis for a data matrix, and also find the squared prediction error (SPE) and Hotelling's T2 test statistic values for each observation in this data matrix.

```
pca(data, var.amnt = 0.9, ...)
```

`data` |
A centred-and-scaled data matrix or xts matrix |

`var.amnt` |
The energy proportion to preserve in the projection, which dictates the number of principal components to keep. Defaults to 0.90. |

`...` |
Lazy dots for additional internal arguments |

This function takes in a training data matrix, without the label column, and the energy preservation proportion, which defaults to 95 percent per Kazor et al (2016). This proportion is the sum of the q largest eigenvalues divided by the sum of all p eigenvalues, where q is the number of columns of the p x q projection matrix P. This function then returns the projection matrix P, a diagonal matrix of the reciprocal eigenvalues (LambdaInv), a vector of the SPE test statistic values corresponding to the rows of the data matrix, and a T2 test statistic vector similar to the SPE vector.

This internal function is called by faultFilter().

A list of class "pca" with the following:

- projectionMatrix –
the q eigenvectors corresponding to the q largest eigenvalues as a p x q projection matrix

- LambdaInv –
the diagonal matrix of inverse eigenvalues

- SPE –
the vector of SPE test statistic values for each of the n observations contained in "data"

- T2 –
the vector of Hotelling's T2 test statistic for each of the same n observations

Called by: `faultFilter`

.

```
nrml <- mspProcessData(faults = "NOC")
scaledData <- scale(nrml[,-1])
pca(scaledData)
```

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