cor.LRtest: Maximum Likelihood Ratio Test for Multivariate Correlation...
In CORREP: Multivariate Correlation Estimator and Statistical Inference Procedures.

Description Usage Arguments Details Value Author(s) References See Also Examples

Performs LRT to test if multivariate correlation vanishes. Note this code will return NaN's if the matrix determinant is negative (see below).

1	cor.LRtest(x, m1, m2)

`x`	data matrix, column represents samples (conditions), and row represents variables (genes), see example below for format information
`m1`	number of replicates for gene X
`m2`	number of replicates for gene Y

Under the multivaraite normal distribution assumption, the column vectors of the data are iid samples. We test the followiing hypothesis: H0: Z ~ N(mu, Sigma0), H1 ~ N(mu, Sigma1). Let M = Inverse(Sigma0)*Sigma1, the likelihood ratio test statistic $G^2$, is, n*[trace(M)-log(det(M))-(m1+m2)]. Under the H0, $G^2$ follows a chi-square distribution with $2m1*m2$ degree of freedom. In some case, the determinant of M is negative so that the log(det(M)) returns NaN. There are two ways to deal with this problem, one, those M's whose determinants are negative tend to consist of very small correlations that are biological irrelevant. Therefore, we can simply ignore those gene pairs that the determinants of correlation matrices M's are negative. Second, we can ‘standardize’ the M to make the determinant positive (implemented in function cor.LRtest.std). In most of cases, we recommend using function cor.LRtest. Use cor.LRtest.std only you know what you are doing.

p.value

The p-value of the LRT

Youjuan Li and Dongxiao Zhu

Zhu, D and Li Y. 2007. Multivariate Correlation Estimator for Inferring Functional Relationships from Replicated 'OMICS' data. Submitted.

cor.LRtest.std, cor.test

library("CORREP")
library("MASS")
Sigma <- matrix(c(1, 0.8, .5,.5, 0.8, 1,
0.5,0.5,0.5,0.5,1,0.6,0.5,0.5,0.6,1),4,4)
dat <- mvrnorm(50, mu=c(0,0,0,0), Sigma)
dat.std <- apply(dat,2,function(x) x/sd(x))
cor.LRtest(t(dat.std), m1=2, m2=2)