plot.MCRestimate {MCRestimate} | R Documentation |
plot.MCRestimate visualizes a 'vote matrix'. A 'vote matrix' is the result of a classification procedure. For every sample (=row) i and every class (=column) j the matrix element [i,j] is the probability or frequency the classification method predicts sample i as a member of class j.
plot.MCRestimate(x, class.factor=NULL, rownames.from.object=FALSE, sample.order=TRUE, legend=FALSE, mypalette=NULL, shading=NULL, xlab="Sample ID", ylab="Frequency of correct classification", cex.axis=1,...)
x |
Object of S3 class MCRestimate or a matrix |
class.factor |
Factor. Its length must match the number of rows
in x and the levels must be the same as the colnames in x .
If x is of class MCRestimate this argument will be ignored. |
rownames.from.object |
Logical. If TRUE then the rownames of the
matrix or the sample names of MCRestimate in x are used as labels for the x-axis |
sample.order |
Logical. If TRUE then the samples are ordered by class membership |
legend |
Logical. If TRUE then there will be a small legend in the output |
mypalette |
|
shading |
|
xlab |
Character |
ylab |
Character |
cex.axis |
numeric |
... |
Further arguments that are passed on to plot.default |
The function is called for its side effect, creating a plot on the active graphics device.
Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de
library(MCRestimate) x <- c(0.5, 0.3, 0.7, 0.3, 0.8, 0.2, 0.3) mat2 <- cbind(x, 1-x) fac2 <- factor(c("low", rep("high", 3), rep("low", 3))) colnames(mat2) <- levels(fac2) mat3 <- cbind(x/3, 2*x/3, 1-x) fac3 <- factor(c(rep("high", 3), rep("intermediate", 2), rep("low", 2))) colnames(mat3) <- levels(fac3) if (interactive()) { x11(width=9, height=9) par(mfrow=c(3,1))} plot.MCRestimate(mat2, fac2) plot.MCRestimate(mat2, fac2, sample.order=FALSE) plot.MCRestimate(mat3, fac3)