# aSPC: An adaptive sum of powered correlation test (aSPC) for... In aSPC: An Adaptive Sum of Powered Correlation Test (aSPC) for Global Association Between Two Random Vectors

## Description

An adaptive sum of powered correlation test (aSPC) for association between two random vectors

## Usage

 ```1 2``` ```aSPC(df1, df2, pow = c(1:6, Inf), B = 100, Z.transform = TRUE, method = "pearson") ```

## Arguments

 `df1, ` first sample matrix `df2, ` second sample matrix `pow, ` power integer candidates, default c(1:8, Inf) `B, ` number of permutations to calculate a P-value. Default is 100. `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. 2017. Adaptive testing for association between two random vectors in moderate to high dimensions. Submitted to Genetic Epidemiology

Kim J., Zhang Y., Pan W. Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associa-tions with GWAS and Sequencing Data. Genetics, 2016, 203(2): 715-731.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```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 ```

aSPC documentation built on May 2, 2019, 2:13 p.m.