| Title: | New Kernel-Based Test for Differential Association Analysis |
|---|---|
| Description: | A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions. Song, H. and Wu, M.C. (2023) <arXiv:2307.15268>. |
| Authors: | Hoseung Song [aut, cre], Michael C. Wu [aut] |
| Maintainer: | Hoseung Song <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.1 |
| Built: | 2024-12-14 06:25:01 UTC |
| Source: | CRAN |
This function provides the kernel-based differential association test.
kerdaa(X1, Y1, X2, Y2, perm=0)kerdaa(X1, Y1, X2, Y2, perm=0)
X1 |
The first multivariate data in the first condition. |
Y1 |
The second multivariate data in the first condition. |
X2 |
The first multivariate data in the second condition. |
Y2 |
The second multivariate data in the second condition. |
perm |
The number of permutations performed to calculate the p-value of the test. The default value is 0, which means the permutation is not performed and only approximated p-value based on the asymptotic theory is provided. Doing permutation could be time consuming, so be cautious if you want to set this value to be larger than 10,000. |
Returns a list with test statistic values and p-values of the test. See below for more details.
stat_g |
The value of the test statistic using the Gaussian kernel. |
stat_l |
The value of the test statistic using the linear kernel. |
pval |
The omnibus p-value using the approximated p-values of the test statistic based on asymptotic theory. |
pval_perm |
The omnibus p-value using the permutation p-values of the test statistic when argument ‘perm’ is positive. |
# Dimension of variables. d = 100 # The first covariance matrix SIG = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG[i,j] = 0.4^(abs(i-j)) } } # The second covariance matrix SIG1 = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG1[i,j] = (0.4+0.5)^(abs(i-j)) } } set.seed(500) # We use 'rmvnorm' in 'mvtnorm' package to generate multivariate normally distributed samples require(mvtnorm) Z = rmvnorm(100, mean = rep(0,100), sigma = SIG) X1 = Z[,1:50] Y1 = Z[,51:100] Z = rmvnorm(100, mean = rep(0,100), sigma = SIG1) X2 = Z[,1:50] Y2 = Z[,51:100] a = kerdaa(X1, Y1, X2, Y2, perm=1000) # output results based on the permutation and the asymptotic results # the test statistic values can be found in a$stat_g and a$stat_l # p-values can be found in a$pval and a$pval_perm# Dimension of variables. d = 100 # The first covariance matrix SIG = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG[i,j] = 0.4^(abs(i-j)) } } # The second covariance matrix SIG1 = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG1[i,j] = (0.4+0.5)^(abs(i-j)) } } set.seed(500) # We use 'rmvnorm' in 'mvtnorm' package to generate multivariate normally distributed samples require(mvtnorm) Z = rmvnorm(100, mean = rep(0,100), sigma = SIG) X1 = Z[,1:50] Y1 = Z[,51:100] Z = rmvnorm(100, mean = rep(0,100), sigma = SIG1) X2 = Z[,1:50] Y2 = Z[,51:100] a = kerdaa(X1, Y1, X2, Y2, perm=1000) # output results based on the permutation and the asymptotic results # the test statistic values can be found in a$stat_g and a$stat_l # p-values can be found in a$pval and a$pval_perm
This package can be used to determine whether two high-dimensional samples have similar dependence relationships across two conditions.
Hoseung Song and Michael C. Wu
Maintainer: Hoseung Song ([email protected])
Song, H. and Wu, M.C. (2023). Multivariate differential association analysis. arXiv:2307.15268
# Dimension of variables. d = 100 # The first covariance matrix SIG = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG[i,j] = 0.4^(abs(i-j)) } } # The second covariance matrix SIG1 = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG1[i,j] = (0.4+0.5)^(abs(i-j)) } } set.seed(500) # We use 'rmvnorm' in 'mvtnorm' package to generate multivariate normally distributed samples require(mvtnorm) Z = rmvnorm(100, mean = rep(0,100), sigma = SIG) X1 = Z[,1:50] Y1 = Z[,51:100] Z = rmvnorm(100, mean = rep(0,100), sigma = SIG1) X2 = Z[,1:50] Y2 = Z[,51:100] a = kerdaa(X1, Y1, X2, Y2, perm=1000) # output results based on the permutation and the asymptotic results # the test statistic values can be found in a$stat_g and a$stat_l # p-values can be found in a$pval and a$pval_perm# Dimension of variables. d = 100 # The first covariance matrix SIG = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG[i,j] = 0.4^(abs(i-j)) } } # The second covariance matrix SIG1 = matrix(0, d, d) for (i in 1:d) { for (j in 1:d) { SIG1[i,j] = (0.4+0.5)^(abs(i-j)) } } set.seed(500) # We use 'rmvnorm' in 'mvtnorm' package to generate multivariate normally distributed samples require(mvtnorm) Z = rmvnorm(100, mean = rep(0,100), sigma = SIG) X1 = Z[,1:50] Y1 = Z[,51:100] Z = rmvnorm(100, mean = rep(0,100), sigma = SIG1) X2 = Z[,1:50] Y2 = Z[,51:100] a = kerdaa(X1, Y1, X2, Y2, perm=1000) # output results based on the permutation and the asymptotic results # the test statistic values can be found in a$stat_g and a$stat_l # p-values can be found in a$pval and a$pval_perm