mutInfTest {sigaR} | R Documentation |
A test evaluates the significance of the mutual information between two (high-dimensional) multivariate random variables. The null distribution is obtained via an efficient permutation resampling algorithm.
mutInfTest(Y, X, nPerm = 1000, method = "normal", k = 1, center = TRUE, rescale = TRUE, lowCiThres=0.10, ncpus=1, verbose=FALSE)
Y |
(High-dimensional) matrix. Columns are assumed to represent the samples, and rows represent the samples' genes or traits. |
X |
(High-dimensional) matrix. Columns are assumed to represent the samples, and rows represent the samples' genes or traits. The number of columns of |
nPerm |
Number of permutations. |
method |
Distributional assumption under which mutual information is to be estimated. |
k |
k-nearest neighbor parameter. |
center |
Logical indicator: should the rows of |
rescale |
Logical indicator: should |
lowCiThres |
A value between 0 and 1. Determines speed of efficient p-value calculation.
If the probability of a p-value being below |
ncpus |
Number of cpus used for the permutations. |
verbose |
Logical indicator: should intermediate output be printed on the screen? |
Object of miTest
-class.
Wessel N. van Wieringen: w.vanwieringen@vumc.nl
Van Wieringen, W.N., Van der Vaart, A.W. (2011), "Statistical analysis of the cancer cell's molecular entropy using high-throughput data", Bioinformatics, 27(4), 556-563.
Van Wieringen, W.N., Van de Wiel, M.A., Van der Vaart, A.W. (2008), "A test for partial differential expression", Journal of the American Statistical Association, 103(483), 1039-1049.
# load data data(pollackCN16) data(pollackGE16) Y <- t(exprs(pollackGE16)) X <- t(copynumber(pollackCN16)) # perform testing and print test results testRes <- mutInfTest(Y, X, nPerm = 1000) summary(testRes)