Displaying 4 results from an estimated 4 matches for "209410".
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209.10
2017 Aug 24
3
Are r2dtable and C_r2dtable behaving correctly?
..., very small numbers. This, at first, seemed strange to
me. So I decided to do some simulations myself, and started playing around
with the r2dtable() function. Problem is, using my row and column
marginals, r2dtable() always returns the same matrix. Let's provide a
minimal example:
rr <- c(209410, 276167)
cc <- c(25000, 460577)
ms <- r2dtable(3, rr, cc)
I have tested this code in two machines and it always returned the same
list of length three containing the same matrix three times. The repeated
matrix is the following:
[[1]]
[,1] [,2]
[1,] 10782 198628
[2,] 14218 261949
[...
2017 Aug 25
2
Are r2dtable and C_r2dtable behaving correctly?
...strange to
> me. So I decided to do some simulations myself, and started playing around
> with the r2dtable() function. Problem is, using my row and column
> marginals, r2dtable() always returns the same matrix. Let's provide a
> minimal example:
> rr <- c(209410, 276167)
> cc <- c(25000, 460577)
> ms <- r2dtable(3, rr, cc)
> I have tested this code in two machines and it always returned the same
> list of length three containing the same matrix three times. The repeated
> matrix is the following:
> [[1]]...
2017 Aug 25
0
Are r2dtable and C_r2dtable behaving correctly?
...strange to
> me. So I decided to do some simulations myself, and started playing around
> with the r2dtable() function. Problem is, using my row and column
> marginals, r2dtable() always returns the same matrix. Let's provide a
> minimal example:
> rr <- c(209410, 276167)
> cc <- c(25000, 460577)
> ms <- r2dtable(3, rr, cc)
> I have tested this code in two machines and it always returned the same
> list of length three containing the same matrix three times. The repeated
> matrix is the following:
> [[1]]...
2017 Aug 25
0
Are r2dtable and C_r2dtable behaving correctly?
...e possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being
> most common.
... and
> dhyper(0:2,2,2,2)
[1] 0.1666667 0.6666667 0.1666667
> dhyper(0:2,2,2,2) *6
[1] 1 4 1
so that is exactly what you would expect. However,
> dhyper(10782,209410, 276167, 25000)
[1] 0.005230889
so you wouldn't expect 10782 to recur. It is close to the mean of the hypergeometric distribution, but
> sim <- rhyper(1e6,209410, 276167, 25000)
> mean(sim)
[1] 10781.53
> sd(sim)
[1] 76.14209
(and incidentally, rhyper is plenty fast enough that...