PCA plot

Description

Generates a Principle Component plot for data.frames, matrices, or a pre-made prcomp object.

Usage

1
2
prcompPlot(object, pc_x = 1, pc_y = 2, scale = FALSE, colFactor = NULL,
  pchFactor = NULL, palette = "rainbow", legend = TRUE, ...)

Arguments

object

data.frame, matrix or prcomp object.

pc_x

integer, principle component for the plot x dimension.

pc_y

integer, principle component for the plot y dimension.

scale

logical, whether to scale to unit variance before PCA.

colFactor

factor or vector, colour the points by this factor, default is NULL.

pchFactor

factor or vector, point-type by this factor, default is NULL.

palette

string, the function to call to create a vector of contiguous colours with levels(colFactor) steps.

legend

logical, whether to display a legend on the plot.

...

further arguments passed to or from other methods.

Details

A data.frame object will be coerced internally to a matrix. Matrices must be of type double or integer. The prcompPlot function will then perform a principle component analysis on the data prior to plotting. The function is call is prcomp(t(object), retx=TRUE, center=TRUE, scale.=scale). Instead of specifying a data.frame or matrix, a pre-made prcomp object can be given to prcompPlot. In this case, care should be taken in setting the appropriate value of scale.. If a vector is given to colFactor or pchFactor, they will be coerced internally to factors.

For the default NULL values of colFactor and pchFactor, all colours will be black and circles the point type, respectively.

Value

None

See Also

prcomp rainbow

Examples

1
2
3
4
5
6
7
8
library(HarmanData)
data(IMR90)
expt <- imr90.info$Treatment
batch <- imr90.info$Batch
prcompPlot(imr90.data, colFactor=expt)
pca <- prcomp(t(imr90.data), scale.=TRUE)
prcompPlot(pca, 1, 3, colFactor=batch, pchFactor=expt, palette='topo.colors',
main='IMR90 PCA plot of Dim 1 and 3')