plotVariable: Variable representation for Principal Component Analysis

In EMA: Easy Microarray Data 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

Author(s)

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

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
\dontrun{
plotVariable(pca)
}
}

## End(Not run)

EMA documentation built on March 26, 2020, 8:40 p.m.