[R] Speed up code with for() loop

[hck]

Barth sent me a very good code and I modified it a bit. Have a look:

Error<-rnorm(10000000, mean=0, sd=0.05)
estimate<-(log(1+0.10)+Error)

DCF_korrigiert<-(1/(exp(1/(exp(0.5*(-estimate)^2/(0.05^2))*sqrt(2*pi/(0.05^2
))*(1-pnorm(0,((-estimate)/(0.05^2)),sqrt(1/(0.05^2))))))-1))
DCF_verzerrt<-(1/(exp(estimate)-1))

S <- 10000000                                   # total sample size
D <- 10000                                      # number of subsamples
Subset <- 10000                                  # number in each subsample
Select <- matrix(sample(S,D*Subset,replace=TRUE),nrow=Subset,ncol=D)

DCF_korrigiert_select <- matrix(DCF_korrigiert[Select],nrow=Subset,ncol=D)
Delta_ln <-(log(colMeans(DCF_korrigiert_select, na.rm=T)/(1/0.10)))



The only problem I discovered is that R cannot handle more than
2.147.483.647 integers, thus the cells in the matrix are bounded by this
condition. (R shows the max by typing: .Machine$integer.max). And if you
want to safe the workspace, the file with 10.000 times 10.000 becomes round
2 GB. Compared to the original of "just" 300 MB. 

So I cannot perform my previous bootstrap with 1.000.000 times 100.000. But
nevertheless 10.000 times 10.000 seems to be sufficiently; I have to say its
amazing, how fast the idea works.

Has anybody a suggestion how to make it work for the 1.000.000 times 100.000
bootstrap???


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