Plot.pca: principal component analysis plotting
In muma: Metabolomics Univariate and Multivariate Analysis

Description Usage Arguments Details Author(s) Examples

Visualize PCA score and loading plots.

1	Plot.pca(pcx, pcy, scaling, test.outlier = TRUE)

`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'.

'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'.

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

## 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)
    }
  }