# principal component analysis plotting

### Description

Visualize PCA score and loading plots.

### Usage

1 |

### Arguments

`pcx` |
an integer indicating the principal component to be plotted in x |

`pcy` |
an integer indicating the principal component to be plotted in y |

`scaling` |
a character string indicating the name of the scaling previously specified in the function 'explore.data' |

`test.outlier` |
logical, indicating whether the geometric outlier testing has to be performed. By default is 'TRUE'. |

### Details

'test.outlier' results in a printed string indicating whether outliers were detected or not; if detected, the samples (rownames) identified as outliers are printed. Principal components to be plotted can be identified through the function 'explore.data'.

A directory called 'PCA-Data' is automatically created in the working directory. Within this directory are written PCA score and loading matrix and every PCA plot generated with the function 'plot.pca'.

### Author(s)

Edoardo Gaude, Dimitrios Spiliotopoulos, Francesca Chignola, Silvia Mari, Andrea Spitaleri and Michela Ghitti

### Examples

1 2 3 4 5 6 7 8 9 | ```
## The function is currently defined as
function (pcx, pcy, scaling, test.outlier = TRUE)
{
Plot.pca.score(pcx, pcy, scaling)
Plot.pca.loading(pcx, pcy, scaling)
if (test.outlier) {
outlier(pcx, pcy, scaling)
}
}
``` |