| corPlot | R Documentation |
Plot of similarity matrix. This function is a slight modification of function
plot.cor of the archived package "sma".
corPlot(x, new = FALSE, col, minCor,
labels = FALSE, lab.both.axes = FALSE, labcols = "black",
title = "", cex.title = 1.2,
protocol = FALSE, cex.axis = 0.8,
cex.axis.bar = 1, signifBar = 2, ...)
x |
data or correlation matrix, respectively |
new |
If |
col |
colors palette for image. If missing, the |
minCor |
numeric value in [-1,1], used to adjust |
labels |
vector of character strings to be placed at the tickpoints,
labels for the columns of |
lab.both.axes |
logical, display labels on both axes |
labcols |
colors to be used for the labels of the columns of |
title |
character string, overall title for the plot. |
cex.title |
A numerical value giving the amount by which plotting text
and symbols should be magnified relative to the default;
cf. |
protocol |
logical, display color bar without numbers |
cex.axis |
The magnification to be used for axis annotation relative to the
current setting of 'cex'; cf. |
cex.axis.bar |
The magnification to be used for axis annotation of the color
bar relative to the current setting of 'cex'; cf.
|
signifBar |
integer indicating the precision to be used for the bar. |
... |
graphical parameters may also be supplied as arguments to the
function (see |
This functions generates the so called similarity matrix (based on correlation) for a microarray experiment.
If min(x), respectively min(cor(x)) is smaller than minCor,
the colors in col are adjusted such that the minimum correlation value
which is color coded is equal to minCor.
invisible()
A first version of this function appeared in package SLmisc.
Matthias Kohl Matthias.Kohl@stamats.de
Sandrine Dudoit, Yee Hwa (Jean) Yang, Benjamin Milo Bolstad and with
contributions from Natalie Thorne, Ingrid Loennstedt and Jessica Mar.
sma: Statistical Microarray Analysis.
http://www.stat.berkeley.edu/users/terry/zarray/Software/smacode.html
## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
colnames(M) <- paste("Sample", 1:20)
M.cor <- cor(M)
corPlot(M.cor, minCor = min(M.cor))
corPlot(M.cor, minCor = min(M.cor), lab.both.axes = TRUE)
corPlot(M.cor, minCor = min(M.cor), protocol = TRUE)
corPlot(M.cor, minCor = min(M.cor), signifBar = 1)
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