Plot.pca: principal component analysis plotting

Description Usage Arguments Details Author(s) Examples

View source: R/Plot.pca.R

Description

Visualize PCA score and loading plots.

Usage

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

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

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## 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)
    }
  }


muma documentation built on May 19, 2017, 11:28 p.m.
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