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...so we are not # recycling y3<-y2[1:1900] # now y3 contains 100 bootstrap samples of 19 each y4 <- matrix(y3, ncol=19, byrow=T) # actually some of students did # y4 <- t( matrix(y3, ncol=100) ) maxboot <- apply(y4, 1, max) biasboot <- mean(maxboot) - 3.87058501 We did this for more than 100 runs, depend how much memory you have on your machine, you can try put a few zeros to the above code. but you get the idea. The final result, biasboot, is very different when using R or Splus 3.4 Roughly 2 times (biasboot from R) = (b...

...so we are not # recycling y3<-y2[1:1900] # now y3 contains 100 bootstrap samples of 19 each y4 <- matrix(y3, ncol=19, byrow=T) # actually some of students did # y4 <- t( matrix(y3, ncol=100) ) maxboot <- apply(y4, 1, max) biasboot <- mean(maxboot) - 3.87058501 We did this for more than 100 runs, depend how much memory you have on your machine, you can try put a few zeros to the above code. but you get the idea. The final result, biasboot, is very different when using R or Splus 3.4 Roughly 2 times (biasboot from R) = (b...