Nothing
epval_Cai2014_diffcov <- function(sam1, sam2, n.resam = 1000, sam.cov1, sam.cov2, cov1.est, cov2.est, cv.fold = 5, norm = "F", seeds, optim.bandwidth1, optim.bandwidth2, output.opt.bw1, output.opt.bw2){
n1 <- dim(sam1)[1]
n2 <- dim(sam2)[1]
p <- dim(sam1)[2]
T_CLX_0 <- stat_Cai2014(sam1, sam2)
T_CLX_resam <- rep(NA, n.resam)
for(b in 1:n.resam){
if(!is.null(seeds)) set.seed(seeds[b])
sam1.b <- rmnorm(n = n1, mean = rep(0, p), cov1.est)
sam2.b <- rmnorm(n = n2, mean = rep(0, p), cov2.est)
T_CLX_resam[b] <- stat_Cai2014(sam1.b, sam2.b)
}
p.CLX <- (sum(T_CLX_resam > T_CLX_0) + 1)/(n.resam + 1)
pval <- p.CLX
names(pval) <- "Cai2014"
out <- NULL
out$sam.info <- c("n1" = n1, "n2" = n2, "p" = p)
if(output.opt.bw1) out$opt.bw1 <- optim.bandwidth1
if(output.opt.bw2) out$opt.bw2 <- optim.bandwidth2
out$cov.assumption <- "the two groups have different covariances"
out$method <- "parametric bootstrap"
out$pval <- pval
return(out)
}
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