# [R] help on R for loops

From: song xin <justinxin_at_yahoo.com>
Date: Sat, 26 Jan 2008 21:24:20 -0800 (PST)

Hi, all.
I need help on improving the efficiency of my R simulations. Below is a function that simulates a change point model. It first generates a sequence of three dimensional ARMA(1,1) observations, then calculates the one step ahead prediction errors, some statistic is calcualted and compared with threshold values in the end. As you can see in the function, there are 5 for loops, which makes the simulation long and inefficient. Can anyone help me improve it?

simfun<-function(b){
for(l in 1:20)
{
qqqqqq<-matrix(0,2,40)
for (w in 1:40)
{
x2 <- matrix(rep(0,6000),nr=3)
epsilonzero2<-(mvrnorm(n=1,mu=c(0,0,0),Sigma=residsigma)) x2[,1:2]<-c(0,0,0)
for (i in 3:550)
{
epsilonone<-(mvrnorm(n=1,mu=c(0,0,0),Sigma=residsigma))   x2[,i] <-
phi%*%x2[,i-1]+epsilonone-theta%*%epsilonzero2 epsilonzero2<-epsilonone
}

residsigma3<-matrix(c(0.789,0.2143,0.171,0.2143,1.4394,-0.229,0.171,-0.229,0.6649),nrow=3,byrow=T) epsilonzero3<-epsilonzero2
for (i in 551:2000)
{
epsilonone<-(mvrnorm(n=1,mu=c(0,0,0),Sigma=residsigma3))   x2[,i] <-
phi%*%x2[,(i-1)]+epsilonone-theta%*%epsilonzero3 epsilonzero3<-epsilonone
}

inno2<-matrix(0,3,2000)
inno2[,1]<-c(0,0,1)
for ( i in 2:2000)
{
inno2[,i]<-x2[,i]-phi%*%x2[,(i-1)]+
theta%*%inno2[,(i-1)]
}

sampeigen4<-matrix(0,3,(2000-nnumber[l])) for ( i in (nnumber[l]+1):2000)
{
var1<-matrix(0,3,3)
for ( j in 1:(nnumber[l]))
{
var1<-var1+j^{b}*inno2[,i-nnumber[l]-1+j]%*%t(inno2[,i-nnumber[l]-1+j])/(sum(c(1:nnumber[l])^{b})) var1<-var1
}

sampeigen4[,(i-nnumber[l])]<-(eigen(var1)\$values-eigen(residsigma)\$values)
}

chisqstat<-rep(0,(2000-nnumber[l]))
for(i in 1:(2000-nnumber[l]))
{
chisqstat[i]<-((nnumber[l]-1)/2)*t(sampeigen4[,i])%*%(diag(eigen(residsigma)\$values)^2)%*%(sampeigen4[,i])
}

normstat<-apply((sampeigen4+eigen(residsigma)\$values),2,prod) qqqqqq[1,w]<-vp(chisqstat[(550-nnumber[l]):(2000-nnumber[l])]) qqqqqq[2,w]<-vp1(normstat[(550-nnumber[l]):(2000-nnumber[l])])
}

inarl2[,l]<-apply(qqqqqq,1,mean)
}

inarl2
}

Thanks

XS

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