Description Usage Arguments Value References Examples
An Adaptive Sum of Powered Correlation Test (aSPC) with dcor
1 | aSPC_dcor2(df1, df2, pow = pow, B = B, show_b = FALSE)
|
df1, |
first matrix |
df2, |
second matrix |
pow, |
power integer candidates, default c(1:8, Inf) |
B, |
number of permutations to calculate a P-value |
the P-values of SPC and aSPC tests
Xu Z., Pan W. An adaptive and powerful test for two groups of variables with high dimension
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | library(mvtnorm)
sigma = diag(0.6, 10) + 0.4
n = 200 # sample size
p = 25; q = 25;
Z = rmvnorm(n=n, mean=rep(0,10), sigma=sigma)
X = rmvnorm(n=n, mean=rep(0,p), sigma=diag(1, p))
Y = rmvnorm(n=n, mean=rep(0,q), sigma=diag(1, q))
X = as.data.frame(cbind(Z[,1:5], X))
Y = as.data.frame(cbind(Z[,6:10], Y))
dim(X)
dim(Y)
set.seed(123) # to ensure we can replicate the permutation P-value
B=10
a = proc.time()
aSPC_dcor2(X, Y, pow = c(1:8, Inf), B = B)
proc.time() - a
a = proc.time()
aSPC(X, Y, pow = c(1:8, Inf), B = B, method = "dcor_fast")
proc.time() - a
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.