Description Usage Arguments Value Author(s) References See Also Examples

For a set of *n* observations grouped into two classes (for
example *n* expression values of a gene), the `score`

function measures the separation of the classes. It can be interpreted
as counting for each observation having response zero, the number of
individuals of response class one that are smaller, and summing up
these quantities.

1 | ```
score(x, resp)
``` |

`x` |
Numeric vector of length |

`resp` |
Numeric vector of length |

A numeric value, the `score`

. The minimal `score`

is
zero, the maximal `score`

is the product of the number of samples
in class 0 and class 1. Values near the minimal or maximal
`score`

indicate good separation, whereas intermediate
`score`

means poor separation.

Marcel Dettling, [email protected]

Marcel Dettling (2002)
*Supervised Clustering of Genes*, see
http://stat.ethz.ch/~dettling/supercluster.html

Marcel Dettling and Peter B<c3><bc>hlmann (2002).
Supervised Clustering of Genes.
*Genome Biology*, **3**(12): research0069.1-0069.15.

`wilma`

, `margin`

is the second statistic
that is used there.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(leukemia, package="supclust")
op <- par(mfrow=c(1,3))
plot(leukemia.x[,69],leukemia.y)
title(paste("Score = ", score(leukemia.x[,69], leukemia.y)))
## Sign-flipping is very important
plot(leukemia.x[,161],leukemia.y)
title(paste("Score = ", score(leukemia.x[,161], leukemia.y),2))
x <- sign.flip(leukemia.x, leukemia.y)$flipped.matrix
plot(x[,161],leukemia.y)
title(paste("Score = ", score(x[,161], leukemia.y),2))
par(op)
``` |

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