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

Computes the empirical correlation for each predictor
variable (gene) in the `x`

-Matrix with the response `y`

, and
multiplies its values with (-1) if the empirical correlation has a
negative sign. For gene expression data, this amounts to treating
under- and overexpression symmetrically. After the `sign.change`

,
low (expression) values point towards response class 0 and high
(expression) values point towards class 1.

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

`x` |
Numeric matrix of explanatory variables ( |

`y` |
Numeric vector of length |

Returns a list containing:

`x.new` |
The sign-flipped |

`signs` |
Numeric vector of length |

Marcel Dettling, [email protected]

Marcel Dettling (2003)
*Finding Groups of Predictive Genes from Microarray Data*, see
http://stat.ethz.ch/~dettling/supervised.html

Marcel Dettling and Peter B<c3><bc>hlmann (2004).
Finding Predictive Gene Groups from Microarray Data.
To appear in the *Journal of Multivariate Analysis*.

`pelora`

, as well as for older methodology,
`wilma`

and `sign.flip`

.

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.change(leukemia.x, leukemia.y)$x.new
plot(x[,161],leukemia.y)
title(paste("Margin = ", round(margin(x[,161], leukemia.y),2)))
par(op)
``` |

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