bootstrapMI {maigesPack} | R Documentation |
This function takes a numerical matrix (or two vectors) and calculates bootstrapped (by permutation) p-values to test if the mutual information value is equal to zero. If the first argument is a matrix, the p-values are calculated between all pairs of rows of the matrix.
bootstrapMI(x, y=NULL, bRep, ret="p-value")
x |
numerical matrix or vector to be analysed. If a vector, the
argument y must be informed. |
y |
numerical vector. Must be informed if x is a
vector. If x is a matrix, this argument is ignored. Defaults
to NULL. |
bRep |
number of permutation to be done in the test. |
ret |
character string with the value to return. Must be 'p-value' (default) for the usual p-value or 'max', to return the maximum absolute correlation value obtained by the permutation. |
The method implemented in this function is proposed by Butte and
Kohane (2000). The MI value is calculated using the function MI
.
The result of this function is a square matrix (length equal to the
number of rows of x
) if x
is a matrix or a numerical
value if x
and y
are vectors. The result is the p-values
or maximum MI values calculated by permutation tests.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
Butte, A.J. and Kohane, I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In Pacific Symposium on Biocomputing, 5, 415-426, 2000 (http://psb.stanford.edu/psb-online/proceedings/psb00/)
x <- runif(50, 0, 1) y <- rbeta(50, 1, 2) bootstrapMI(x, y, bRep=100) z <- matrix(rnorm(100, 0, 1), 4, 25) bootstrapMI(z, bRep=100)