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Data simulation for PCA

R codes

a<-rbinom(n=1000, size=1, prob = 1/2)
a[a==0] <- -1

tdata <- array(0, dim=c(200, 1000))

for(i in 1:200){
position<-sample(1:1000, 2)
a[position] <- -1* a[position]
tdata[i,] <- a }

means<- apply(tdata, 2, mean) # means for each position
diffs<-sweep(tdata, 2, means) # centering

res_svd <- svd(diffs)

Left <- res_svd$u # the left singular vectors
Right <- res_svd$v # the right singular vectors
sqL <- diag(res_svd$d) # diagonal matrix of the singular values

### calculatinf of pc's
sPC_sample <- tdata %*% Right/1000^0.5
sPC_nuc <- t(diffs) %*% Left/200^0.5


dir.create("S6")
for(i in 1:10){
png(width=1000, height=1100, pointsize = 40, file=paste("S6/PC", i, ".png", sep=""))
par(lwd=2, mex=0.6, mai=c(2,2,2,0.2))
plot(1:200, sPC_sample [,i], type="l", xlab="round", ylab=paste("sPC", i, sep=""))
dev.off()}