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.
numerical matrix or vector to be analysed. If a vector, the
numerical vector. Must be informed if
number of permutation to be done in the test.
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
The result of this function is a square matrix (length equal to the
number of rows of
x is a matrix or a numerical
y are vectors. The result is the p-values
or maximum MI values calculated by permutation tests.
Gustavo H. Esteves <email@example.com>
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/)
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