MCRestimate (version 2.26.0)

plot.MCRestimate: Plot method for a objects of class MCRestimate

Description

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.

Usage

"plot"(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,...)

Arguments

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
vector with length equal to the number of classes. The vector specifies the color for the bar representing the classes. If 'NULL' colors chosen by the author are used.
shading
the density of shading lines for the rectangles that indicate the groups, in lines per inch. The default value of 'NULL' means that no shading lines are drawn.
xlab
Character
ylab
Character
cex.axis
numeric
...
Further arguments that are passed on to plot.default

Value

active graphics device.

See Also

MCRestimate

Examples

Run this code
  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)

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