EMA (version 1.4.4)

plotVariable: Variable representation for Principal Component Analysis

Description

Variable representation for Principal Component Analysis (PCA)

Usage

plotVariable(acp, axes = c(1, 2), new.plot = FALSE, lab, lim.cos2.var =
0, palette="rainbow", ...)

Arguments

acp

result from PCA or do.pca function

axes

axes for variable representation, by default 1 and 2

new.plot

if TRUE, a new graphical device is created, by default = FALSE

lab

variable label

palette

character, name of color palette, by default = "rainbow"

lim.cos2.var

keep variables with cos2 >= lim.cos2.var

...

Arguments to be passed to methods, such as graphical parameters (see 'par').

Value

Variable representation on axes axes[1] and axes[2]If PCA is normed, the correlation circle is plotted colored by lab

See Also

runPCA,PCA

Examples

## Not run: 
# data(marty)
# 
# ## PCA on sample on 100 genes
# ## In practice see genes.selection
# ##mvgenes<-genes.selection(marty, thres.num=100)
# 
# pca <- runPCA(t(marty[1:100,]), verbose = FALSE, plotSample = FALSE,
#     plotInertia = FALSE)
# \dontrun{
# ## Variable plot of PCA object
# plotVariable(pca)
# }
# ## End(Not run)