I'd like to ask the developers to include some exact computation for
ties into wilcox.test(). Just try
wilcox.test(c(1,1,5),c(10,11))
wilcox.test(c(1,2,5),c(10,11))
The p-values differ significantly.
But if I try
library(exactRankTests)
wilcox.exact(c(1,1,5),c(10,11))
wilcox.exact(c(1,2,5)
The binary tree algorithm does not need additional scrambling. I have
written the R code for the algorithm in the last answer at:
https://stackoverflow.com/questions/311703/algorithm-for-sampling-without-replacement/46807110#46807110
However, the algorithm will probably be outperformed by hash t
Thank you for your answer. Certainly, hash table must be faster than
binary tree.
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See also:
P. Gupta, G. P. Bhattacharjee. (1984) An efficient algorithm for random
sampling without replacement. International Journal of Computer
Mathematics 16:4, pages 201-209.
http://dx.doi.org/10.1080/00207168408803438
Teuhola, J. and Nevalainen, O. 1982. Two efficient algorithms for random
If somebody is interested I can write the code. But somebody else has to
add the code for handling int / long int / double cases, since I do not
have enough experience in that.
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Let us consider the current uniform sampling without replacement
algorithm. It resides in function do_sample in
https://svn.r-project.org/R/trunk/src/main/random.c
Its complexity is obviously O(n), where the sample is selected from
1...n, since the algorithm has to create a vector of length n. S
