PLOT.PCA: Plot the results of a principal component analysis

In easyCODA: Compositional Data Analysis in Practice

Description

Various maps and biplots of the results of a principal component analysis using function PCA.

Usage

PLOT.PCA(obj, map="symmetric", rescale=1, dim=c(1,2), axes.inv = c(1,1),
         main="", cols=c("blue","red"), colarrows = "pink", cexs=c(0.8,0.8),
         fonts=c(2,4))

Arguments

obj	An LRA object created using function LRA
map	Choice of scaling of rows and columns: "symmetric" (default), "asymmetric" or "contribution"
rescale	A rescaling factor applied to column coordinates (default is 1 for no rescaling)
dim	Dimensions selected for horizontal and vertical axes of the plot (default is c(1,2))
axes.inv	Option for reversing directions of horizontal and vertical axes (default is c(1,1) for no reversing, change one or both to -1 for reversing)
main	Title for plot
cols	Colours for row and column labels (default is c("blue","red"))
colarrows	Colour for arrows in asymmetric and contribution biplots (default is "pink")
cexs	Character expansion factors for row and column labels (default is c(0.8,0.8))
fonts	Fonts for row and column labels (default is c(2,4))

Details

The function PLOT.PCA makes a scatterplot of the results of a logratio analysis (computed using function PCA), with various options for scaling the results and changing the direction of the axes. By default, dimensions 1 and 2 are plotted on the horizontal and vertical axes, and it is assumed that row points refer to samples and columns to variables.

By default, the symmetric scaling is used, where both rows and columns are in principal coordinates and have the same amount of weighted variance along the two dimensions. The other options are biplots: the asymmetric option, when columns are in standard coordinates, and the contribution option, when columns are in contribution coordinates. In cases where the row and column displays occupy widely different extents, the column coordinates can be rescaled using the rescale option.

Author(s)

Michael Greenacre

References

Greenacre, M. (2013), Contribution biplots, Journal of Computational and Graphical Statistics, 22, 107-122.

See Also

plot.ca

Examples

# perform weighted PCA on the ALRs of the RomanCups data set
# where the first oxide silica is chosen as the denominator
data(cups)
cups.ALR <- ALR(cups, denom=1)
cups.PCA <- PCA(cups.ALR)
PLOT.PCA(cups.PCA, map="contribution", rescale=0.2, axes.inv=c(1,-1))	

Example output

Loading required package: ca
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-7
Loading required package: ellipse

Attaching package: ‘ellipse’

The following object is masked from ‘package:graphics’:

    pairs

easyCODA documentation built on Sept. 20, 2020, 1:07 a.m.