Hi:
On Tue, Mar 1, 2011 at 8:22 AM, Bodnar Laszlo EB_HU <
laszlo.bod...@erstebank.hu> wrote:
> Hello there,
>
> I have a problem concerning bootstrapping in R - especially focusing on the
> resampling part of it. I try to sum it up in a simplified way so that I
> would not confuse anybody.
>
> I have a small database consisting of 20 observations (basically numbers
> from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20).
>
To check on the probability of this event happening, I ran the following:
bootmat <- matrix(sample(1:20, 200000, replace = TRUE), nrow = 10000)
sum(apply(bootmat, 1, function(x) any(table(x) >= 5)) )
[1] 492
It's about 0.05. A Q& D 'solution' would be to oversample by at least 5%
(let's do 10% just to be on the safe side) and then pick out the first B of
these. In the above example, we could do 11000 samples instead, and pick out
the first 10000 that meet the criterion:
bootmat <- matrix(sample(1:20, 220000, replace = TRUE), nrow = 11000)
badsamps <- apply(bootmat, 1, function(x) any(tabulate(x) >= 5))
bootfin <- bootmat[-badsamps, ][1:10000, ]
Time:
user system elapsed
0.28 0.00 0.28
(Note 1: Using table instead of tabulate took 4.22 seconds on my machine -
tabulate is much faster.)
(Note 2: In the call above, there were 539 bad samples, so the 5% ballpark
estimate seems plausible.)
This is a simple application of the accept-reject criterion. I don't know
how large 'many' is to you, but 10,000 seems to be a reasonable starting
point. I ran it again for 1,000,000 such samples, and the completion time
was
user system elapsed
36.74 0.31 37.15
so the processing time is of an order a bit larger than linear. If your
simulations are of this magnitude and are to be run repeatedly, you probably
need to write a function to improve the speed and to get rid of the waste
produced by a rejection sampling approach. If this is a one-off deal,
perhaps the above is sufficient.
HTH,
Dennis
> I would like to resample this database many times for the bootstrap process
> with the following conditions. Firstly, every resampled database should also
> include 20 observations. Secondly, when selecting a number from the
> above-mentioned 20 numbers, you can do this selection with replacement. The
> difficult part comes now: one number can be selected only maximum 5 times.
> In order to make this clear I show you a couple of examples. So the
> resampled databases might be like the following ones:
>
> (1st database) 1,2,1,2,1,2,1,2,1,2,3,3,3,3,3,4,4,4,4,4
> 4 different numbers are chosen (1, 2, 3, 4), each selected - for the
> maximum possible - 5 times.
>
> (2nd database) 1,8,8,6,8,8,8,2,3,4,5,6,6,6,6,7,19,1,1,1
> Two numbers - 8 and 6 - selected 5 times (the maximum possible times),
> number 1 selected 4 times, the others selected less than 4 times.
>
> (3rd database) 1,1,2,2,3,3,4,4,9,9,9,10,10,13,10,9,3,9,2,1
> Number 9 chosen for the maximum possible 5 times, number 10, 3, 2, 1 chosen
> for 3 times, number 4 selected twice and number 13 selected only once.
>
> ...
>
> Anybody knows how to implement my "tricky" condition into one of the R
> functions - that one number can be selected only 5 times at most? Are 'boot'
> and 'bootstrap' packages capable of managing this? I guess they are, I just
> couldn't figure it out yet...
>
> Thanks very much! Best regards,
> Laszlo Bodnar
>
>
>
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