pca.scoreplot: Score plot for Principal Components (objects of class 'Pca')

In rrcov: Scalable Robust Estimators with High Breakdown Point

Score plot for Principal Components (objects of class 'Pca')

Description

Produces a score plot from an object (derived from) Pca-class.

Usage

    pca.scoreplot(obj, i=1, j=2, main, id.n, ...)

Arguments

obj	an object of class (derived from) "Pca".
i	First score coordinate, defaults to i=1.
j	Second score coordinate, defaults to j=2.
main	The main title of the plot.
id.n	Number of observations to identify by a label. If missing and the total number of observations is less or equal to 10, all observations will be labelled.
...	Optional arguments to be passed to the internal graphical functions.

Author(s)

Valentin Todorov [e-mail]

See Also

Pca-class, PcaClassic, PcaRobust-class.

Examples

require(graphics)

## PCA of the Hawkins Bradu Kass's Artificial Data
##  using all 4 variables
data(hbk)
pca <- PcaHubert(hbk)
pca
pca.scoreplot(pca)

rrcov documentation built on July 9, 2023, 6:03 p.m.