Description Usage Arguments Value Author(s) 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 (p variables in columns, n cases in rows). For example, these can be microarray gene expression data which should be sign-flipped and then grouped. |
y |
Numeric vector of length n containing the class labels of the individuals. These labels have to be coded by 0 and 1. |
Returns a list containing:
x.new |
The sign-flipped |
signs |
Numeric vector of length p, which for each predictor variable indicates whether it was sign-flipped (coded by -1) or not (coded by +1). |
Marcel Dettling, dettling@stat.math.ethz.ch
pelora
also for references,
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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