entropyTest {sigaR} | R Documentation |
A one-sided two-sample test compares the entropy of a (high-dimensional) multivariate random variable between two groups. The test is one-sided: one group is a priori suspected to have a larger entropy. The null distribution is obtained via an efficient permutation resampling algorithm.
entropyTest(Y, id, nPerm = 1000, method = "normal", k0 = 1, k1 = 1, center = TRUE, lowCiThres=0.10, ncpus=1, verbose=FALSE)
Y |
(High-dimensional) matrix. Rows are assumed to represent the samples, and columns represent the samples' genes or traits. |
id |
An indicator variable for the two groups to be compared. The groups should be coded as |
nPerm |
Number of permutations. |
method |
Distributional assumption under which entropy is to be estimated. |
k0 |
k-nearest neighbor parameter for group comprising of samples indicated by a zero in the indicator variable |
k1 |
k-nearest neighbor parameter for group comprising of samples indicated by a one in the indicator variable |
center |
Logical indicator: should the columns of Y be centered around zero? |
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 entTest
-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(pollackGE16) Y <- exprs(pollackGE16) # assign samples to groups id <- sample(c(0,1), 41, replace=TRUE) # perform testing and print test results testRes <- entropyTest(t(Y), id, nPerm = 5, method="knn") summary(testRes)