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

Computes the `score`

for each predictor variable
(gene) in the `x`

-Matrix, and multiplies its values with (-1) if
its `score`

is greater or equal than half of the maximal
`score`

. For gene expression data, this amounts to treating
under- and overexpression symmetrically. After the sign-flip
procedure, low (expression) values point towards response class 0 and
high (expression) values point towards class 1.

1 | ```
sign.flip(x, y)
``` |

`x` |
Numeric matrix of explanatory variables ( |

`y` |
Numeric vector of length |

Returns a list containing:

`flipped.matrix` |
The sign-flipped |

`signs` |
Numeric vector of length |

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`

and `score`

, as well as for a
newer methodology, `pelora`

and `sign.change`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(leukemia, package="supclust")
op <- par(mfrow=c(1,3))
plot(leukemia.x[,69],leukemia.y)
title(paste("Margin = ", round(margin(leukemia.x[,69], leukemia.y),2)))
## Sign-flipping is very important
plot(leukemia.x[,161],leukemia.y)
title(paste("Margin = ", round(margin(leukemia.x[,161], leukemia.y),2)))
x <- sign.flip(leukemia.x, leukemia.y)$flipped.matrix
plot(x[,161],leukemia.y)
title(paste("Margin = ", round(margin(x[,161], leukemia.y),2)))
par(op)# reset
``` |

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