# [R] Tricky vectorization problem

Mike Lawrence Mike.Lawrence at DAL.CA
Sun Oct 7 03:07:32 CEST 2007

```Seems there were some line break issues when pasting the code, trying
again with a different commenting style:

#start a timer
start = proc.time()

#set the true correllation
rho = .5

#set the number of Subjects
Ns = 100

#for each subject, set a number of observations in A
a.No = 1:100
#for each subject, set a number of observations in B
b.No = 1:100

#set the between Ss variance in condition A
v.a = 1
#set the between Ss variance in condition B
v.b = 2

#for each subject, set a standard deviation in A
s.a.w = 1:100
#for each subject, set a standard deviation in B
s.b.w = 1:100

#set the number of monte carlo experiments
mce = 1e3

#set up a collector for the simulated correlations
sim.r = vector(length=mce)

#define a covariance matrix for use in generating the correlated data
Sigma=matrix(c(v.a,sqrt(v.a*v.b)*rho,sqrt(v.a*v.b)*rho,v.b),2,2)

#set up a collector for subject means in A
a = vector(length=Ns)
#set up a collector for subject means in B
b = vector(length=Ns)

#start a monte carlo experiment
for(i in 1:mce){

#generate correlated ideal means for for each subject in each condition
sub.means=mvrnorm(Ns,rep(0,2),Sigma)

#for each subject
for(s in 1:Ns){

#generate some data for A and grab the mean
a[s] = mean(rnorm(a.No[s],sub.means[s,1],s.a.w[s]))
#generate some data for B and grab the mean
b[s] = mean(rnorm(b.No[s],sub.means[s,2],s.b.w[s]))

}

#store the observed correlation between A and B
sim.r[i] = cor(a,b)

}

#show the total time this took
print(proc.time()-start)

```