}
power[j]<-mean((abs(rp)> qnorm(0.975))*1)
}
write(as.vector(power), file="Logormal_MhD_NN.txt", ncolumns=1)
############################################################
d=10
m=1000
n=800
iter=100
h=seq(0, 3, length.out=20)
power=vector(length=20)
rp=vector(length=iter)
mu0=rep(0, d)
sigma0=rep(1, d)%*%t(rep(1, d))+diag(rep(1,d))
#sigma0=as.matrix(read.table(file="Lognormal_Dependent_Sigma10.txt"))
for(j in 1:20)
{
delta1<-h[j]*rep(1, d)/sqrt(m+n)
mu1=delta1
for(i in 1:iter)
{
points1<-exp(rmnorm(m, mu0, sigma0))
points2<-exp(rmnorm(n, mu1, sigma0))
a<-mdepth.MhD(points1, points1, scale=TRUE)$dep
b<-mdepth.MhD(points2, points1, scale=TRUE)$dep
t<-sum(outer(a,b,"<")*1)/(m*n)
rp[i]<-(t-0.5)/(sqrt((1/m+1/n)/12))
}
power[j]<-mean((abs(rp)> qnorm(0.975))*1)
}
write(as.vector(power), file="Lognormal_MhDDependent_NN.txt", ncolumns=1)
#######################################################
a<-read.table("NN_Lognormal_Independent.txt")
b<-read.table("T2Lognormal_NN.txt")
c<-read.table("Lognormal_HD_NN.txt")
d<-read.table("Lognormal_MD_NN.txt")
a<-read.table("NN_Lognormal_Independent.txt")
b<-read.table("T2Lognormal_NN.txt")
c<-read.table("Lognormal_HD_NN.txt")
d<-read.table("Lognormal_MD_NN.txt")
a<-read.table("NN_Lognormal_Independent.txt")
b<-read.table("T2Lognormal_NN.txt")
c<-read.table("Lognormal_HD_NN.txt")
d<-read.table("Lognormal_MhD_NN.txt")
h=seq(0, 3, length.out=20)
a<-as.matrix(read.table("NN_Lognormal_Independent.txt"))
b<-read.table("T2Lognormal_NN.txt")
c<-read.table("Lognormal_HD_NN.txt")
d<-read.table("Lognormal_MhD_NN.txt")
a
scatter.smooth(h[-1], a[,i][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
scatter.smooth(h[-1], a[,1][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
a[,1]
for(i in 2:8)
{
points(loess.smooth(h[-1], a[,i][-1]), type='l', col=2)
}
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=5)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=6)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=8)
points(loess.smooth(h[-1], e[,1][-1]), type='l', col=9)
abline(h=0.05, col=10)
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], e[,1][-1]), type='l', col=8)
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
pdf(file="Lognormal_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main=" Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
a<-as.matrix(read.table("NN_Lognormal_Dedependent_New.txt"))
a<-as.matrix(read.table("NN_LognormalDedependent_New.txt"))
a<-as.matrix(read.table("NN_Lognormal_Dependent_New.txt"))
b<-read.table("T2LognormalDependent_NN.txt")
c<-read.table("LognormalDependent_HD_NN.txt")
c<-read.table("Lognormal_Dependent_HD_NN.txt")
a<-as.matrix(read.table("NN_Lognormal_Dependent_New.txt"))
b<-read.table("T2LognormalDependent_NN.txt")
c<-read.table("Lognormal_HDDependent_NN.txt")
d<-read.table("Lognormal_MhDDependent_NN.txt")
h=seq(0, 3, length.out=20)
pdf(file="Lognormal_Dependent_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main="Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
pdf(file="Lognormal_Dependent_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.4), col=1, xlab="Local Separation", ylab="Power", main="Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
pdf(file="Lognormal_Dependent_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.4), col=1, xlab="Local Separation", ylab="Power", main="Dependent Lognormal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
d=c(300, 400)
m=1000
n=500
iter=100
h=2
power=vector(length=2)
rp=rp1=vector(length=iter)
for(j in 1:2)
{
delta1<-h/(m+n)^(1/2)
mu0=rep(0, d[j])
sigma0=diag(rep(1, d[j]))
sigma1=sigma0+delta1*diag(rep(1, d[j]))
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma0)
points<-rbind(points1, points2)
rp1[i]<-(m+n)*log(det((t(points)%*%points)/(m+n)))-m*log(det((t(points1)%*%points1)/m))-n*log(det((t(points2)%*%points2)/n))
}
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
points<-rbind(points1, points2)
rp[i]<-(m+n)*log(det((t(points)%*%points)/(m+n)))-m*log(det((t(points1)%*%points1)/m))-n*log(det((t(points2)%*%points2)/n))
#rpn[i]<-(r-s*(m+n-1))/sqrt(D*(m+n))
}
power[j]<-length(which(rp> quantile(rp1, 0.95)))/iter
#power2[j]<-length(which(rpn< qnorm(0.05)))/iterations
}
power
d=c(300, 400)
m=1000
n=500
iter=100
h=2
power=vector(length=2)
for(j in 1:2)
{
rp=vector(length=iter)
delta1<-h*rep(1, d[j])/sqrt(m+n)
mu0=rep(0, d[j])
mu1=delta1
sigma0=diag(rep(1, d[j]))
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu1, sigma0)
xmean<-apply(points1, 2, mean)
ymean<-apply(points2, 2, mean)
diff<-xmean-ymean
vc<-(1/m+1/n)*(((m-1)*cov(points1)+(n-1)*cov(points2))/(m+n-2))
rp[i]<-t(diff)%*%solve(vc)%*%diff
}
power[j]<-length(which(rp> qchisq(0.95, d[j])))/iter
}
power
d=c(300, 400)
m=1000
n=500
iter=100
h=2
power=vector(length=2)
for(j in 1:2)
{
rp=rpnull=vector(length=iter)
mu0=rep(0, d[j])
delta1<-h/(m+n)^(1/2)
sigma0=diag(rep(1, d[j]))
sigma1=sigma0+delta1*diag(rep(1, d[j]))
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
xmean<-apply(points1, 2, mean)
ymean<-apply(points2, 2, mean)
diff<-xmean-ymean
vc<-(1/m+1/n)*(((m-1)*cov(points1)+(n-1)*cov(points2))/(m+n-2))
rp[i]<-t(diff)%*%solve(vc)%*%diff
}
power[j]<-length(which(rp> qchisq(0.95, d[j])))/iter
}
power
d=c(300, 400)
m=1000
n=500
iter=100
h=2
power=vector(length=2)
for(j in 1:2)
{
rp=rpnull=vector(length=iter)
delta1<-h*rep(1, d[j])/sqrt(m+n)
mu0=rep(0, d[j])
mu1=delta1
sigma0=diag(rep(1, d[j]))
for(i in 1:iter)
{
points1<-exp(rmnorm(m, mu0, sigma0))
points2<-exp(rmnorm(n, mu1, sigma0))
xmean<-apply(points1, 2, mean)
ymean<-apply(points2, 2, mean)
diff<-xmean-ymean
vc<-(1/m+1/n)*(((m-1)*cov(points1)+(n-1)*cov(points2))/(m+n-2))
rp[i]<-t(diff)%*%solve(vc)%*%diff
}
power[j]<-length(which(rp> qchisq(0.95, d[j])))/iter
}
power
d=c(300, 400)
m=1000
n=500
iter=100
h=2
power=vector(length=2)
rp=rp1=vector(length=iter)
for(j in 1:2)
{
delta1<-h/(m+n)^(1/2)
mu0=rep(0, d[j])
sigma0=diag(rep(1, d[j]))
sigma1=sigma0+delta1*diag(rep(1, d[j]))
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma0)
points<-rbind(points1, points2)
rp1[i]<-(m+n)*log(mean(diag(points%*%t(points))))-m*log(mean(diag(points1%*%t(points1))))-n*log(mean(diag(points2%*%t(points2))))
}
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
points<-rbind(points1, points2)
rp[i]<-(m+n)*log(mean(diag(points%*%t(points))))-m*log(mean(diag(points1%*%t(points1))))-n*log(mean(diag(points2%*%t(points2))))
#rpn[i]<-(r-s*(m+n-1))/sqrt(D*(m+n))
}
power[j]<-length(which(rp> quantile(rp1, 0.95)))/iter
#power2[j]<-length(which(rpn< qnorm(0.05)))/iterations
}
power
library(mnormt)
library(igraph)
library(cccd)
library(RANN)
d=10
m=1000
n=800
s=c(1, 5, 20, 50, 100, 300, 600, 900)
mu0=rep(0, d)
sigma0=diag(rep(1, d))
iterations=100
iter=20
power1=matrix(nrow=iter, ncol=length(s))
rnull=matrix(nrow=iterations, ncol=length(s))
rp1=vector(length=iterations)
h=seq(0, 3, length.out=iter)
for(j in 1:length(s))
{
for(i in 1:iterations)
{
points1<-exp(rmnorm(m, mu0, sigma0))
points2<-exp(rmnorm(n, mu0, sigma0))
pts<-rbind(points1,points2)
aa1<-nn2(pts, points1, s[j]+1)$nn.idx[,-1]
aa2<-nn2(pts, points2, s[j]+1)$nn.idx[,-1]
rnull[i, j]<-length(which(c(aa1)>m))+length(which(c(aa2)<=m))
}
}
# pdf(file="Dense_Lognormal_50_QQ.pdf")
# qqnorm(scale(rnull))
# abline(a=0, b=1)
# dev.off()
#
# write.table(sigma0, file="NN_Lognormal_50_Sigma.txt")
# eigen(sigma0)$values
for(k in 1:length(s))
{
for(j in 1:iter)
{
delta1<-h[j]/(m+n)^(1/2)
sigma1=sigma0+delta1*diag(rep(1, d))
for(i in 1:iterations)
{
points1<-exp(rmnorm(m, mu0, sigma0))
points2<-exp(rmnorm(n, mu0, sigma1))
pts<-rbind(points1,points2)
aa1<-nn2(pts, points1, s[k]+1)$nn.idx[,-1]
aa2<-nn2(pts, points2, s[k]+1)$nn.idx[,-1]
rp1[i]<-length(which(c(aa1)>m))+length(which(c(aa2)<=m))
}
power1[j, k]<-length(which(rp1< quantile(rnull[,k], 0.05)))/iterations
print(power1[j, k])
}
}
write.table(as.matrix(power1, nrow=iter, ncol=length(s)), file="NN_Scale.txt")
#write(as.matrix(sigma0), file="Lognormal_Independent_Sigma.txt")
a<-as.matrix(read.table("NN_Normal.txt"))
b<-read.table("T2Normal_NN.txt")
c<-read.table("Normal_HD_NN.txt")
d<-read.table("Normal_MhD_NN.txt")
h=seq(0, 3, length.out=20)
pdf(file="Normal_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main="Normal in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
######################################################
a<-as.matrix(read.table("NN_Scale.txt"))
b<-read.table("T2Scale_NN.txt")
c<-read.table("Scale_HD_NN.txt")
d<-read.table("Scale_MhD_NN.txt")
h=seq(0, 3, length.out=20)
pdf(file="Scale_NN.pdf")
scatter.smooth(h[-1], a[,2][-1], type='n', ylim=c(0, 0.9), col=1, xlab="Local Separation", ylab="Power", main="Normal Scale in d=10")
points(loess.smooth(h[-1], a[,3][-1]), type='l', col=2)
#points(loess.smooth(h[-1], a[,5][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,6][-1]), type='l', col=3)
points(loess.smooth(h[-1], a[,7][-1]), type='l', col=4)
points(loess.smooth(h[-1], a[,8][-1]), type='l', col=5)
points(loess.smooth(h[-1], b[,1][-1]), type='l', col=6)
points(loess.smooth(h[-1], c[,1][-1]), type='l', col=7)
points(loess.smooth(h[-1], d[,1][-1]), type='l', col=8)
abline(h=0.05, col=9)
legend("topleft", c("5NN", "20NN", "100NN", "600NN", "900NN", "T2", "HD", "MD"), col=c(1,2,3,4,5,6,7,8),
lty = c(1,1,1,1,1,1,1,1), bg = 'gray90',text.width = strwidth("1,000,000"))
dev.off()
library(cluster)
library(mnormt)
library(localdepth)
library(fda.usc)
d=300
m=1000
n=500
iterations=1000
iter=1000
h=2
rp=vector(length=iter)
rp1=vector(length=iterations)
mu0=rep(0, d)
delta1<-h/(m+n)^(1/2)
sigma0=diag(rep(1, d))
sigma1=sigma0+delta1*diag(rep(1, d))
d=300
m=1000
n=500
iterations=1000
iter=100
h=2
rp=vector(length=iter)
rp1=vector(length=iterations)
mu0=rep(0, d)
delta1<-h/(m+n)^(1/2)
sigma0=diag(rep(1, d))
sigma1=sigma0+delta1*diag(rep(1, d))
for(i in 1:iterations)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma0)
aa<-mdepth.TD(points1, points1, scale=TRUE)$dep
bb<-mdepth.TD(points2, points1, scale=TRUE)$dep
rp1[i]<-sum(outer(aa,bb,"<")*1)/(m*n)
}
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
a<-mdepth.TD(points1, points1, scale=TRUE)$dep
b<-mdepth.TD(points2, points1, scale=TRUE)$dep
rp[i]<-sum(outer(a,b,"<")*1)/(m*n)
}
power<-(length(which(rp> quantile(rp1, 0.975)))+length(which(rp < quantile(rp1, 0.025))))/iter
power
rp
rp1
library(cluster)
library(mnormt)
library(localdepth)
library(fda.usc)
d=300
m=1000
n=500
iterations=500
iter=100
h=2
rp=vector(length=iter)
rp1=vector(length=iterations)
mu0=rep(0, d)
delta1<-h/(m+n)^(1/2)
sigma0=diag(rep(1, d))
sigma1=sigma0+delta1*diag(rep(1, d))
for(i in 1:iterations)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma0)
aa<-mdepth.TD(points1, points1, scale=TRUE)$dep
bb<-mdepth.TD(points2, points1, scale=TRUE)$dep
rp1[i]<-sum(outer(aa,bb,"<")*1)/(m*n)
}
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
a<-mdepth.TD(points1, points1, scale=TRUE)$dep
b<-mdepth.TD(points2, points1, scale=TRUE)$dep
rp[i]<-sum(outer(a,b,"<")*1)/(m*n)
}
power<-(length(which(rp> quantile(rp1, 0.975)))+length(which(rp < quantile(rp1, 0.025))))/iter
#write(as.vector(power), file="Scale_TD_NN.txt", ncolumns=1)
############################################################
power
library(cluster)
library(mnormt)
library(localdepth)
library(fda.usc)
d=300
m=1000
n=500
iterations=1000
iter=100
h=2
rp=vector(length=iter)
rp1=vector(length=iterations)
mu0=rep(0, d)
delta1<-h/(m+n)^(1/2)
sigma0=diag(rep(1, d))
sigma1=sigma0+delta1*diag(rep(1, d))
for(i in 1:iterations)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma0)
aa<-mdepth.MhD(points1, points1, scale=TRUE)$dep
bb<-mdepth.MhD(points2, points1, scale=TRUE)$dep
rp1[i]<-sum(outer(aa,bb,"<")*1)/(m*n)
}
for(i in 1:iter)
{
points1<-rmnorm(m, mu0, sigma0)
points2<-rmnorm(n, mu0, sigma1)
a<-mdepth.MhD(points1, points1, scale=TRUE)$dep
b<-mdepth.MhD(points2, points1, scale=TRUE)$dep
rp[i]<-sum(outer(a,b,"<")*1)/(m*n)
}
power<-(length(which(rp> quantile(rp1, 0.975)))+length(which(rp < quantile(rp1, 0.025))))/iter
#write(as.vector(power), file="Scale_TD_NN.txt", ncolumns=1)
############################################################
power
