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test_MVHRM.R
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library(matrixcalc)
library(MASS)
library(tidyr)
library(HRM)
pseudorank <- function(liste){
# defining variables und preparing data
a <- length(liste)
g <- list(0)
n <- vapply(liste, FUN=nrow, FUN.VALUE = 0L )
g <- lapply(liste, FUN=vec)
gg <- unlist(g)
t <- ncol(liste[[1]])
# defining normalized ecdf's
F <- function(x, h, n, i){
return( 1/2*1/n[i]*(sum(h[[i]]<x) + sum(h[[i]]<=x) ) )
}
# unweighted average ecdf
G <- function(x, h, n, ngroups) {
return(1/ngroups*sum(vapply(1:ngroups, FUN=F, FUN.VALUE = 1, h=g, n=n, x=x)))
}
# calculate pseudoranks
pseudoranks <- sum(n)*vapply(gg, FUN = G, h = g, n = n, ngroups = a, FUN.VALUE = vector(mode="double", length=1L) ) + 1/2
# put all observations from one subject in one row
result <- NULL
for(i in 1:a){
j <- sum(n[0:(i-1)])*t+1
jj <- sum(n[0:i])*t
result <- rbind(result, matrix(pseudoranks[j:jj], nrow = n[i], ncol=t, byrow = FALSE) )
}
return(result)
}
I = function(size){
return (diag(rep(1,size)))
}
J = function(size){
return (rep(1,size)%*%t(rep(1,size)))
}
P = function(size){
return (I(size)-J(size)*1/size)
}
.E1 = function(n,i, M) {
return ((n[i]*(n[i]-1))/((n[i]-2)*(n[i]+1))*(matrix.trace(M)^2-2/n[i]*matrix.trace(M%*%M)))
}
.E2 = function(n,i, M) {
return ((n[i]-1)^2/((n[i]-2)*(n[i]+1))*(matrix.trace(M%*%M)-1/(n[i]-1)*matrix.trace(M)^2))
}
# .E1 = function(n,i, M) {
# return ((matrix.trace(M)^2))
# }
# .E2 = function(n,i, M) {
# return ((matrix.trace(M%*%M)))
# }
# Setting:
k = 3 # groups
p = 3 # variables
n = c(5,7,5) # sample sizes
N = sum(n)
alpha=0.05
t <- 30
tt = c(2,10,50,100,300,400)
sim = 10*10^3
g11 = rep(0,sim)
alternative = seq(0, 0, 0.2)
reject = rep(0, length(tt))
reject1 = rep(0, length(tt))
reject11 = rep(0, length(tt))
reject2 = rep(0, length(tt))
reject3 = rep(0, length(tt))
reject31 = rep(0, length(tt))
reject32 = rep(0, length(tt))
start = Sys.time()
#set.seed(12345)
for(l in 1:length(tt)){
print(tt[l])
y <- list(reject2, reject3, tt)
set.seed(tt[l])
#save(file="result_normal_ind_10_200.RData", y)
for(j in 1:sim){
if(j%%100==0){
print(paste(j,"/",sim))
print(reject[l]/j)
print(reject2[l]/j)
print(reject3[l]/j)
}
t = 50 #tt[l]
n <- c(20,20,20)
#C = J(k)/k
C = P(k)/(k-1)
#D = cbind(I(t-1), rep(-1,t-1))
D = t(as.matrix(rep(1,t)))*1/t
#gg <- kronecker(rep(1,n[1]), t(seq(from=1,to=0,length.out = t)))
a = matrix(kronecker( rnorm(n[[1]], sd=1) , t(rep(1,t)) ),ncol=t,nrow=n[1] )
#a <- a*gg
group1p1 = matrix( rnorm(n[[1]]*t,mean = 1, sd=2) , ncol=t,nrow=n[1]) + a
group1p2 = matrix(( rnorm(n[[1]]*t, mean = 0, sd = 1) ), ncol=t,nrow=n[1] ) + a
group1p3 = matrix(( rnorm(n[[1]]*t, mean = 10, sd = 1) ), ncol=t,nrow=n[1] ) + a
#gg <- kronecker(rep(1,n[2]), t(seq(from=1,to=0,length.out = t)))
b = matrix(kronecker(rnorm(n[[2]],sd=1 ) , t(rep(1,t)) ), ncol=t,nrow=n[2])#*gg
group2p1 = matrix(( rnorm(n[[2]]*t, mean = 1, sd=1) ), ncol=t,nrow=n[2]) + b
group2p2 = matrix(( rnorm(n[[2]]*t, mean = 0, sd = 2) ), ncol=t,nrow=n[2]) + b
group2p3 = matrix(( rnorm(n[[2]]*t, mean = 10,sd = 1) ), ncol=t, nrow=n[2]) + b
#gg <- kronecker(rep(1,n[3]), t(seq(from=1,to=0,length.out = t)))
c = matrix(kronecker(rnorm(n[[3]],sd=1) , t(rep(1,t)) ), ncol=t,nrow=n[3])#*gg
group3p1 = matrix(( rnorm(n[[3]]*t, mean = 1, sd=1) ), ncol=t,nrow=n[3]) + c
group3p2 = matrix(( rnorm(n[[3]]*t, mean = 0, sd = 1) ), ncol=t,nrow=n[3]) + c
group3p3 = matrix(( rnorm(n[[3]]*t,mean = 10, sd = 1.4) ), ncol=t, nrow=n[3]) + c
p1=(rbind(group1p1[,],group2p1[,],group3p1[,]))
p2=(rbind(group1p2[,],group2p2[,],group3p2[,]))
p3=(rbind(group1p3[,],group2p3[,],group3p3[,]))
R = data.frame(1:(p*t))
for(i in 1:sum(n)){
R[,i] = vec(rbind(p1[i,],p2[i,],p3[i,]))
}
R = as.matrix(R) # in columns are the Y_ik
Z = kronecker(D, I(p))%*%R
#Z = R
Z = as.matrix(Z)
df <- data.frame(response = vec(as.matrix(R)), subject = gl(sum(n),t*p),
group = as.factor(c(rep(1,n[1]*t*p),rep(2,n[2]*t*p),rep(3,n[3]*p*t))),
time = as.factor(rep(1:t,p*sum(n))),
variable = gl(p,t*sum(n)))
# hrm.mv.internal(df, "group" , "time", "subject", "response", "variable", C, D )
# hrm.mv.1w.1f(df, "group" , "time", "subject", "response", "variable" )
hrm_test(response~group*time, subject=subject, variable=variable, data = df)
N <- sum(n)
S1 = var(t(Z[,1:n[[1]] ]))
S2 = var(t(Z[,(n[[1]]+1):(n[[1]]+n[[2]]) ]))
S3 = var(t(Z[,((n[[1]]+n[[2]]+1):N) ]))
S = 1/k*(1/n[1]*S1 + 1/n[2]*S2 + 1/n[3]*S3)
Zbar1 = as.matrix(1/n[1]*rowSums(Z[,1:n[1]]))
Zbar2 = as.matrix(1/n[2]*rowSums(Z[,(n[1]+1):(n[1]+n[2])]))
Zbar3 = as.matrix(1/n[3]*rowSums(Z[,(n[1]+n[2]+1):N]))
Zbar = as.matrix(cbind(Zbar1, Zbar2, Zbar3))
Q <- data.frame(Q1 = rep(0,k), Q2 = rep(0,k))
Q[1,] <- calcUCpp(t(Z[,1:(n[1])]),n[1],I(dim(S1)[1]),colMeans(t(Z[,1:(n[1])])))
Q[2,] <- calcUCpp(t(Z[,(n[1]+1):(n[1]+n[2])]),n[2],I(dim(S1)[1]),colMeans(t(Z[,(n[1]+1):(n[1]+n[2])])))
Q[3,] <- calcUCpp(t(Z[,(n[1]+n[2]+1):(n[1]+n[2]+n[3])]),n[3],I(dim(S1)[1]),colMeans(t(Z[,(n[1]+n[2]+1):(n[1]+n[2]+n[3])])))
# .E1 = function(n,i, M) {
# return ((n[i]*(n[i]-1))/((n[i]-2)*(n[i]+1))*(matrix.trace(M)^2-2/n[i]*matrix.trace(M%*%M)))
# }
# .E2 = function(n,i, M) {
# return ((n[i]-1)^2/((n[i]-2)*(n[i]+1))*(matrix.trace(M%*%M)-1/(n[i]-1)*matrix.trace(M)^2))
# }
.E1 = function(n,i, M) {
return (Q[i,1])
}
.E2 = function(n,i, M) {
return (Q[i,2])
}
#numerator = 1/n[1]^2*(.E2(n,1,S1) + .E1(n,1,S1))+1/n[2]^2*(.E2(n,2,S2) + .E1(n,2,S2))+2*1/n[1]*1/n[2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2))
#C = diag(c(1,1,1))
fHdenominator = 1/n[1]^2*C[1,1]^2*(.E2(n,1,S1)+.E1(n,1,S1))+
1/n[2]^2*C[2,2]^2*(.E2(n,2,S2)+.E1(n,2,S2))+
1/n[3]^2*C[3,3]^2*(.E2(n,3,S3)+.E1(n,3,S3))+
2*1/n[1]*1/n[2]*C[1,2]*C[2,1]*(matrix.trace(S1%*%S2)+matrix.trace(S1)*matrix.trace(S2)) +
2*1/n[1]*1/n[3]*C[1,3]*C[3,1]*(matrix.trace(S1%*%S3)+matrix.trace(S1)*matrix.trace(S3)) +
2*1/n[2]*1/n[3]*C[2,3]*C[3,2]*(matrix.trace(S2%*%S3)+matrix.trace(S2)*matrix.trace(S3))
numeratorH = 1/n[1]^2*C[1,1]^2*(.E2(n,1,S1) + .E1(n,1,S1))+
1/n[2]^2*C[2,2]^2*(.E2(n,2,S2) + .E1(n,2,S2))+
1/n[3]^2*C[3,3]^2*(.E2(n,3,S3) + .E1(n,3,S3)) +
2*1/n[1]*1/n[2]*C[1,1]*C[2,2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) +
2*1/n[1]*1/n[3]*C[1,1]*C[3,3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) +
2*1/n[2]*1/n[3]*C[2,2]*C[3,3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
fGdenominator = 1/k^2*(1/n[1]^2*1/(n[1]-1)^2*(n[1]*(1-1/n[1])^2 + n[1]*(n[1]-1)*1/n[1]^2)*(.E2(n,1,S1)+.E1(n,1,S1))+
1/n[2]^2*1/(n[2]-1)^2*(n[2]*(1-1/n[2])^2 + n[2]*(n[2]-1)*1/n[2]^2)*(.E2(n,2,S2)+.E1(n,2,S2)) +
1/n[3]^2*1/(n[3]-1)^2*(n[3]*(1-1/n[3])^2 + n[3]*(n[3]-1)*1/n[3]^2)*(.E2(n,3,S3)+.E1(n,3,S3)) )
# fGdenominator = 1/k^2*(1/n[1]^2*1/(n[1]-1)*(.E2(n,1,S1)+.E1(n,1,S1))+
# 1/n[2]^2*1/(n[2]-1)*(.E2(n,2,S2)+.E1(n,2,S2)) +
# 1/n[3]^2*1/(n[3]-1)*(.E2(n,3,S3)+.E1(n,3,S3)) )
numerator = 1/n[1]^2*(.E2(n,1,S1)+.E1(n,1,S1))+
1/n[2]^2*(.E2(n,2,S2)+.E1(n,2,S2))+
1/n[3]^2*(.E2(n,3,S3)+.E1(n,3,S3))+
2*1/n[1]*1/n[2]*(matrix.trace(S1)*matrix.trace(S2)+matrix.trace(S1%*%S2))+
2*1/n[1]*1/n[3]*(matrix.trace(S1)*matrix.trace(S3)+matrix.trace(S1%*%S3))+
2*1/n[2]*1/n[3]*(matrix.trace(S2)*matrix.trace(S3)+matrix.trace(S2%*%S3))
numerator <- numerator*1/k^2
# numerator = 1/n[1]^2*(.E2(n,1,S1) + .E1(n,1,S1))+1/n[2]^2*(.E2(n,2,S2) + .E1(n,2,S2))+1/n[3]^2*(.E2(n,3,S3) + .E1(n,3,S3)) +2*1/n[1]*1/n[2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
fH = numeratorH/fHdenominator
fG <- numerator/fGdenominator
#C = P(k)*1/(k-1)
H = fH*Zbar%*%C%*%t(Zbar)
G = fG*S
d = p*matrix.rank(D%*%t(D))
TD = sqrt(d)*fG*matrix.trace(H)/matrix.trace(G) - sqrt(d)*fH
#TDsd1 = sqrt(2*(fH*fG+fH^2)*matrix.trace(S%*%S)/d)/(matrix.trace(S)/d*sqrt(fG))
TDsd1 = (2*(fH)*matrix.trace(S%*%S)/d-matrix.trace(S)^2/(fG*d))/(matrix.trace(S)^2/d^2)*(1+fH/fG)*fG^2/(fG^2+fG-2)
TDsdalt = (2*(fH)*matrix.trace(S%*%S)/d-matrix.trace(S)^2/(fG*d))/(matrix.trace(S)^2/d^2)*fG^2/(fG^2+fG-2)
TDsrv = TD/sqrt(TDsd1)
if(abs(TDsrv)>=qnorm(1-0.05/2)){reject3[l] = reject3[l] + 1}
TDsrvalt = TD/sqrt(TDsdalt)
if(abs(TDsrvalt)>=qnorm(1-0.05/2)){reject[l] = reject[l] + 1}
TDsdN <- 1/n[1]^2*.E2(n,1,S1)+
1/n[2]^2*.E2(n,2,S2)+
1/n[3]^2*.E2(n,3,S3)+
2*1/n[1]*1/n[2]*(matrix.trace(S1%*%S2)) +
2*1/n[1]*1/n[3]*(matrix.trace(S1%*%S3)) +
2*1/n[2]*1/n[3]*(matrix.trace(S2%*%S3))
TDsdN <- sqrt(2*fH*TDsdN*d)
TDsdD <- 1/n[1]^2*.E1(n,1,S1)+
1/n[2]^2*.E1(n,2,S2)+
1/n[3]^2*.E1(n,3,S3)+
2*1/n[1]*1/n[2]*matrix.trace(S1)*matrix.trace(S2)+
2*1/n[1]*1/n[3]*matrix.trace(S1)*matrix.trace(S3)+
2*1/n[2]*1/n[3]*matrix.trace(S2)*matrix.trace(S3)
TDsdD <- sqrt(TDsdD)
TDsd <- TDsdN/(matrix.trace(S)/d*k)*sqrt(1+fH/fG)
TDsd2 <- TDsdN/TDsdD*sqrt(1+fH/fG)
# TDu = TD/TDsd
# if(abs(TDu)>=qnorm(1-0.05/2)){reject[l] = reject[l] + 1}
TDu2 = TD/TDsd2
if(abs(TDu2)>=qnorm(1-0.05/2)){reject2[l] = reject2[l] + 1}
# M <- fG - 0.5*(d+fH+1)
# gamma <- 1/48*d*fH*(d^2+fH^2-5)
# f <- d*fH
# prob <- function(x) {
# pchisq(x, df = f) + gamma/M^2*(pchisq(x,df=f+4) - pchisq(x,df=f))-0.95
# }
# crit <- uniroot(prob, interval = c(0,200))$root
# if(-M*log(Lambda)>=crit){
# reject1 <- reject1 + 1
# }
# Muirhead verwirft immer dann, wenn auch Rao F verwirft
# if(-M*log(Lambda)>=crit | test >= qf(1-alpha, v1, v2)){
# reject <- reject + 1
# }
#
# if(-M*log(Lambda)>=crit & test < qf(1-alpha, v1, v2)){
# print("...")
# }
# large fG, small p , small a
# Bathke Harrar (2008), p.149
# Anderson (2003), p.332
# nu = d*fH
# qWilks = qchisq(1-alpha,nu) + 1/fG*( (d-fH+1)/2 - 0 )*qchisq(1-alpha,nu) + k*(d+fH+1)/(2*(nu+2))*qchisq(1-alpha,nu)^2*1/fG
# if( matrix.trace(1/(2-1)*H%*%solve(G,tol=1e-30)) >= qWilks/(fG+0)){
# reject[l] = reject[l] + 1
# }
# qWilks = qchisq(1-alpha,nu) + 1/fG*( (d-fH+1)/2 + (d-fH+1)/2 )*qchisq(1-alpha,nu) + k*(d+fH+1)/(2*(nu+2))*qchisq(1-alpha,nu)^2*1/fG
# if( -log(det(G%*%solve(G+H,tol=1e-30))) >= qWilks/(fG-(d-fH+1)/2*1/fG)){
# reject1[l] = reject1[l] + 1
# }
# qWilks = qchisq(1-alpha,nu) + 1/fG*( (d-fH+1)/2 - (fH-1) )*qchisq(1-alpha,nu) + k*(d+fH+1)/(2*(nu+2))*qchisq(1-alpha,nu)^2*1/fG
# if( (matrix.trace(H%*%solve(G+H,tol=1e-30))) >= qWilks/(fG +(fH-1)*1/fG)){
# reject11[l] = reject11[l] + 1
# }
#
# alternative degrees of freedom
# fHdenominator = 1/n[1]^2*C[1,1]^2*(.E21(n,1,S1)+.E11(n,1,S1))+1/n[2]^2*C[2,2]^2*(.E21(n,2,S2)+.E11(n,2,S2))+1/n[3]^2*C[3,3]^2*(.E21(n,3,S3)+.E11(n,3,S3))+2*1/n[1]*1/n[2]*C[1,2]*C[2,1]*(matrix.trace(S1%*%S2)+matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*C[1,3]*C[3,1]*(matrix.trace(S1%*%S3)+matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*C[2,3]*C[3,2]*(matrix.trace(S2%*%S3)+matrix.trace(S2)*matrix.trace(S3))
# fGdenominator = (1/n[1]^2*1/(n[1]-1)*(.E21(n,1,S1)+.E11(n,1,S1))+1/n[2]^2*1/(n[2]-1)*(.E21(n,2,S2)+.E11(n,2,S2)) + 1/n[3]^2*1/(n[3]-1)*(.E21(n,3,S3)+.E11(n,3,S3)) )
#
# numeratorH = 1/n[1]^2*C[1,1]^2*(.E21(n,1,S1) + .E11(n,1,S1))+1/n[2]^2*C[2,2]^2*(.E21(n,2,S2) + .E11(n,2,S2))+1/n[3]^2*C[3,3]^2*(.E21(n,3,S3) + .E11(n,3,S3)) +2*1/n[1]*1/n[2]*C[1,1]*C[2,2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*C[1,1]*C[3,3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*C[2,2]*C[3,3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
# numerator = 1/n[1]^2*(.E21(n,1,S1) + .E11(n,1,S1))+1/n[2]^2*(.E21(n,2,S2) + .E11(n,2,S2))+1/n[3]^2*(.E21(n,3,S3) + .E11(n,3,S3)) +2*1/n[1]*1/n[2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
#
# fH = numeratorH/fHdenominator
# fG =numerator/fGdenominator
#
# H = fH*Zbar%*%C%*%t(Zbar)
# G = fG*S
#
# Lambda = 1/det(I(dim(H)[1])+H%*%solve(G,tol=1e-30))
#
# m1 = fH
# m2 = fG
# d = p*matrix.rank(D%*%t(D)) # dim(Zbar)[1]
# if(d>fG){
# d=fG
# print("v2<0")
# print(j)
# }
# a = sqrt((d^2+m1^2-5)/((m1*d)^2-4))
# if((d^2+m1^2-5) < 0){
# stop("<0")
# }
# v1 = m1*d
# v2 = a^(-1)*(m2-1/2*(d-m1+1))-1/2*(m1*d-2)
# # finite sample approximation with an F(v1, v2) distribution
# test = (Lambda^(-a)-1)*v2/v1
# if(v2 < 0){
# print("v2<0")
# }
# if( test >= qf(1-alpha, v1, v2)){
# reject31[l] = reject31[l] + 1
# }
# alternative degrees of freedom 2
# fHdenominator = 1/n[1]^2*C[1,1]^2*(.E22(n,1,S1)+.E12(n,1,S1))+1/n[2]^2*C[2,2]^2*(.E22(n,2,S2)+.E12(n,2,S2))+1/n[3]^2*C[3,3]^2*(.E22(n,3,S3)+.E12(n,3,S3))+2*1/n[1]*1/n[2]*C[1,2]*C[2,1]*(matrix.trace(S1%*%S2)+matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*C[1,3]*C[3,1]*(matrix.trace(S1%*%S3)+matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*C[2,3]*C[3,2]*(matrix.trace(S2%*%S3)+matrix.trace(S2)*matrix.trace(S3))
# fGdenominator = (1/n[1]^2*1/(n[1]-1)*(.E22(n,1,S1)+.E12(n,1,S1))+1/n[2]^2*1/(n[2]-1)*(.E22(n,2,S2)+.E12(n,2,S2)) + 1/n[3]^2*1/(n[3]-1)*(.E22(n,3,S3)+.E12(n,3,S3)) )
#
# numeratorH = 1/n[1]^2*C[1,1]^2*(.E22(n,1,S1) + .E12(n,1,S1))+1/n[2]^2*C[2,2]^2*(.E22(n,2,S2) + .E12(n,2,S2))+1/n[3]^2*C[3,3]^2*(.E22(n,3,S3) + .E12(n,3,S3)) +2*1/n[1]*1/n[2]*C[1,1]*C[2,2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*C[1,1]*C[3,3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*C[2,2]*C[3,3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
# numerator = 1/n[1]^2*(.E22(n,1,S1) + .E12(n,1,S1))+1/n[2]^2*(.E22(n,2,S2) + .E12(n,2,S2))+1/n[3]^2*(.E22(n,3,S3) + .E12(n,3,S3)) +2*1/n[1]*1/n[2]*(matrix.trace(S1%*%S2) + matrix.trace(S1)*matrix.trace(S2)) + 2*1/n[1]*1/n[3]*(matrix.trace(S1%*%S3) + matrix.trace(S1)*matrix.trace(S3)) + 2*1/n[2]*1/n[3]*(matrix.trace(S2%*%S3) + matrix.trace(S2)*matrix.trace(S3))
#
# fH = numeratorH/fHdenominator
# fG =numerator/fGdenominator
#
# H = fH*Zbar%*%C%*%t(Zbar)
# G = fG*S
#
# Lambda = 1/det(I(dim(H)[1])+H%*%solve(G,tol=1e-30))
#
# m1 = fH
# m2 = fG
# d = p*matrix.rank(D%*%t(D)) # dim(Zbar)[1]
# if(d>fG){
# d=fG
# print("v2<0")
# print(j)
# }
# a = sqrt((d^2+m1^2-5)/((m1*d)^2-4))
# v1 = m1*d
# v2 = a^(-1)*(m2-1/2*(d-m1+1))-1/2*(m1*d-2)
# # finite sample approximation with an F(v1, v2) distribution
# test = (Lambda^(-a)-1)*v2/v1
# if(v2 < 0){
# print("v2<0")
# }
# if( test >= qf(1-alpha, v1, v2)){
# reject32[l] = reject32[l] + 1
# }
}
}
Sys.time()-start
#reject/sim # chi^2 Approximation LH
# reject1/sim # chi^2 Approximation LR
# reject11/sim # chi^2 Approximation BNP
reject2/sim # Wilk
reject3/sim # Fuji
#reject31/sim # Rao F, U Sch?tzer
#reject32/sim # Rao F, Plug-In Sch?tzer
n
p*tt
# y <- list(reject, reject2, reject3, tt)
# save(file="result_normal_ind_10_400_101510.RData", y)