Spectral Map Plot of Multivariate Data...
Description
Spectral Map Plot of Multivariate Data
Produces a spectral map plot (biplot) of an object of class mpm
Usage
1 2 3 4 5 6 7 8 9  ## S3 method for class 'mpm'
plot(x, scale=c("singul", "eigen", "uvr", "uvc"), dim=c(1, 2), zoom=rep(1,
2), show.row=c("all", "position"), show.col=c("all", "position"),
col.group=rep(1, length(x$col.names)), colors=c("orange1", "red",
rainbow(length(unique(col.group)), start = 2/6, end = 4/6)),
col.areas=TRUE, col.symbols=c(1, rep(2,
length(unique(col.group)))), sampleNames=TRUE, rot=rep(1,
length(dim)), labels, label.tol=1, label.col.tol=1, lab.size=0.725,
col.size=10, row.size=10, do.smoothScatter=FALSE, do.plot=TRUE, ...)

Arguments
x 
object of class 
scale 
optional character string specifying the type of factor scaling of the biplot. This can be either "singul" (singular value scaling), "eigen" (eigenvalue scaling), "uvr" (unit rowvariance scaling), "uvc" (unit columnvariance scaling). The latter is of particular value when analyzing large matrices, such as gene expression data. Singular value scaling "singul" is customary in spectral map analysis. Defaults to "singul". 
dim 
optional principal factors that are plotted along the horizontal
and vertical axis. Defaults to 
zoom 
optional zoom factor for row and column items. Defaults to

show.row 
optional character string indicating whether all rows ("all") are to be plotted or just the positioned rows "position". 
show.col 
optional character string indicating whether all columns ("all") are to be plotted or just the positioned columns "position". 
col.group 
optional vector (character or numeric) indicating the
different groupings of the columns, e.g. 
colors 
vector specifying the colors for the annotation of the plot; the first two elements concern the rows; the third till the last element concern the columns; the first element will be used to color the unlabeled rows; the second element for the labeled rows and the remaining elements to give different colors to different groups of columns. 
col.areas 
logical value indicating whether columns should be plotted
as squares with areas proportional to their marginal mean and colors
representing the different groups ( 
col.symbols 
vector of symbols when 
sampleNames 
Either a logical vector of length one or a character
vector of length equal to the number of samples in the dataset. If a
logical is provided, sample names will be displayed on the plot
( 
rot 
rotation of plot. Defaults to 
labels 
character vector to be used for labeling points on the graph;
if 
label.tol 
numerical value specifying either the percentile
( 
label.col.tol 
numerical value specifying either the percentile
( 
lab.size 
size of identifying labels for row and columnitems as

col.size 
size in mm of the column symbols 
row.size 
size in mm of the row symbols 
do.smoothScatter 
use smoothScatter or not instead of plotting individual points 
do.plot 
produce a plot or not 
... 
further arguments to 
Details
Spectral maps are special types of biplots with the area of the symbols
proportional to some measure, usually the row or column mean value and an
identification of row and columnitems. For large matrices, such as gene
expression data, where there is an abundance of rows, this can obscure the
plot. In this case, the argument label.tol
can be used to select the
most informative rows, i.e. rows that are most distant from the center of
the plot. Only these rowitems are then labeled and represented as circles
with their areas proportional to the marginal mean value. For the
columnitems it can be useful to apply some grouping specified by
col.group
. Examples of groupings are different pathologies, such as
specified in Golub.grp
Value
An object of class plot.mpm
that has the following
components:
Rows 
a data frame with the X and Y coordinates of the
rows and an indication 
Columns 
a data frame with the X and Y coordinates of the columns 
Note
value
is returned invisibly, but is available for further use
when an explicit assignment is made
Author(s)
Luc Wouters
References
Wouters, L., Goehlmann, H., Bijnens, L., Kass, S.U., Molenberghs, G., Lewi, P.J. (2003). Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 59, 11311140.
See Also
mpm
, summary.mpm
Examples
1 2 3 4 5 6 7 8  # Weighted spectral map analysis
data(Golub) # Gene expression data of leukemia patients
data(Golub.grp) # Pathological classes coded as 1, 2, 3
r.sma < mpm(Golub[,1:39], row.weight = "mean", col.weight = "mean")
# Spectral map biplot with result
r < plot(r.sma, label.tol = 20, scale = "uvc",
col.group = (Golub.grp)[1:38], zoom = c(1,1.2), col.size = 5)
Golub[r$Rows$Select, 1] # 20 most extreme genes
