R/SPU.mulT.R

In jasonzyx/APaSPU: Adaptive Sum of Powered Test (aSPU) Based on Approximated Score Vector

#' The function for adaptive sum of powered score (aSPU) test
#'
#' It gives the simulation-based p-values of the sum of powered score (SPU) with different power and aSPU test.
#' @param U, Score vector
#' @param V, variance-covariance matrix of U
#' @param pow, the power vector for aSPU tests.
#' @param B, number of simulations
#' @return  simulation-based p-values of the sum of powered score (SPU) with different power and aSPU test
#' @details the suggested power term is c(1:8, Inf)
#' @author Zhiyuan (Jason) Xu, Yiwei Zhang and Wei Pan
#' @references
#' Wei Pan, Junghi Kim, Yiwei Zhang, Xiaotong Shen and Peng Wei (2014) A powerful and adaptive
#' association test for rare variants, Genetics, 197(4), 1081-95
#'
#' Yiwei Zhang, Zhiyuan Xu, Xiaotong Shen, Wei Pan (2014) Testing for association with multiple
#' traits in generalized estimation equations, with application to neuroimaging data. Neuroimage.
#' 96:309-25
#'
#' @importFrom MASS mvrnorm ginv
#' @importFrom lme4 glmer
SPU.mulT <- function(U, V, pow = c(1:8,Inf), B){


  K = length(U)
  gamma2 = pow
  gamma1 = 1
  weight = F
  spu=spuval(U=U,V=V, gamma1=gamma1,gamma2=gamma2,K,weight=weight)
  p=rep(NA,length(gamma1)*length(gamma2))
  ###### NULL Distribution
  T0s=matrix(NA,nrow=B,ncol=length(gamma1)*length(gamma2))
  u.null<-mvrnorm(B,rep(0,length(U)),V)
  for (b in 1:B) T0s[b,]=spuval(U=u.null[b,],V=V,gamma1=gamma1,gamma2=gamma2,K=K,weight=weight)
  for (g in 1:ncol(T0s))  p[g]<-(1+sum(abs(spu[g])<abs(T0s[,g])))/(1+B)

  ##### p-values
  P0s<-PermPvs(T0s,B=B)
  minp0<-apply(P0s,1,min)
  Paspu<-(sum(minp0<=min(p))+1)/(B+1)
  pvs<-data.frame(matrix(c(p,Paspu),nrow=1))

  names(pvs)=c(paste0("SPU.",pow),"aSPU")
  return(pvs)
}