aSPC: An Adaptive Sum of Powered Correlation Test (aSPC) to Test...

In jasonzyx/aSPC: An Adaptive Sum of Powered Correlation Test (aSPC) to Test Global Association Between Two Groups of Variables

Description

An Adaptive Sum of Powered Correlation Test (aSPC) to Test Global Association Between Two Data Matrices

Usage

aSPC(df1, df2, pow = c(1:6, Inf), B = 100, Z.transform = TRUE,
  method = "pearson")

Arguments

df1,	first matrix
df2,	second matrix
pow,	power integer candidates, default c(1:8, Inf)
B,	number of permutations to calculate a P-value
Z.transform,	whether to do Fisher's z-transformation on Pearson correlation, default is TRUE.
method,	one of "pearson", "spearman", or "dcor". Default is "pearson".

Value

the P-values of SPC and aSPC tests

References

Xu Z., Pan W. An adaptive and powerful test for two groups of variables with high dimension

Examples

library(mvtnorm)
sigma = diag(0.9, 10) + 0.1
n = 50 # sample size
Z = rmvnorm(n=n, mean=rep(0,10), sigma=sigma)
X = rmvnorm(n=n, mean=rep(0,15), sigma=diag(1, 15))
Y = rmvnorm(n=n, mean=rep(0,15), sigma=diag(1, 15))
X = as.data.frame(cbind(Z[,1:5], X))
Y = as.data.frame(cbind(Z[,6:10], Y))
set.seed(123) # to ensure we can replicate the permutation P-value
p = 2; q = 2; n=50
X = rmvnorm(n=n, mean=rep(0,p), sigma=diag(1, p))
Y = rmvnorm(n=n, mean=rep(0,q), sigma=diag(1, q))
a = proc.time()
aSPC(X, Y, pow = c(1:8, Inf), B = 1000, method = "pearson")
proc.time() - a

#' a = proc.time()
aSPC(X, Y, pow = c(1:8, Inf), B = 1000, method = "spearman")
proc.time() - a

a = proc.time()
aSPC(X, Y, pow = c(1:8, Inf), B = 500, method = "dcor")
proc.time() - a

jasonzyx/aSPC documentation built on May 18, 2019, 5:55 p.m.