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.

1 2 3 4 5 6 7 8 9 10 11 |

`x` |
Object of S3 class |

`class.factor` |
Factor. Its length must match the number of rows
in |

`rownames.from.object` |
Logical. If TRUE then the rownames of the
matrix or the sample names of |

`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 |

The function is called for its side effect, creating a plot on the active graphics device.

Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de

`MCRestimate`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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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