[Rd] Are r2dtable and C_r2dtable behaving correctly?

[Jari Oksanen]

It is not about "really arge total number of observations", but:

set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11)

A11
     0      1      2 
166483 666853 166664 

There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being
most common.

Cheers, Jari O.
________________________________________
From: R-devel <r-devel-bounces at r-project.org> on behalf of Martin Maechler <maechler at stat.math.ethz.ch>
Sent: 25 August 2017 11:30
To: Gustavo Fernandez Bayon
Cc: r-devel at r-project.org
Subject: Re: [Rd] Are r2dtable and C_r2dtable behaving correctly?

>>>>> Gustavo Fernandez Bayon <gbayon at gmail.com>
>>>>>     on Thu, 24 Aug 2017 16:42:36 +0200 writes:

    > Hello,
    > While doing some enrichment tests using chisq.test() with simulated
    > p-values, I noticed some strange behaviour. The computed p-value was
    > extremely small, so I decided to dig a little deeper and debug
    > chisq.test(). I noticed then that the simulated statistics returned by the
    > following call

    > tmp <- .Call(C_chisq_sim, sr, sc, B, E)

    > were all the same, 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

    > [[2]]
    > [,1]   [,2]
    > [1,] 10782 198628
    > [2,] 14218 261949

    > [[3]]
    > [,1]   [,2]
    > [1,] 10782 198628
    > [2,] 14218 261949

Yes.  You can also do

   unique(r2dtable(100, rr, cc))

and see that the result is constant.

I'm pretty sure this is still due to some integer overflow,

in spite of the fact that I had spent quite some time to fix
such problem in Dec 2003, see the 14 years old bug PR#5701
  https://bugs.r-project.org/bugzilla/show_bug.cgi?id=5701#c2

It has to be said that this is based on an algorithm published
in 1981, specifically - from  help(r2dtable) -

     Patefield, W. M. (1981) Algorithm AS159.  An efficient method of
     generating r x c tables with given row and column totals.
     _Applied Statistics_ *30*, 91-97.

   For those with JSTOR access (typically via your University),
   available at http://www.jstor.org/stable/2346669

When I start reading it, indeed the algorithm seems start from the
expected value of a cell entry and then "explore from there"...
and I do wonder if there is not a flaw somewhere in the
algorithm:

I've now found that a bit more than a year ago, 'paljenczy' found on SO
  https://stackoverflow.com/questions/37309276/r-r2dtable-contingency-tables-are-too-concentrated
that indeed the generated tables seem to be too much around the mean.
Basically his example:

https://stackoverflow.com/questions/37309276/r-r2dtable-contingency-tables-are-too-concentrated


> set.seed(1); system.time(tabs <- r2dtable(1e6, c(100, 100), c(100, 100))); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1))
   user  system elapsed
  0.218   0.025   0.244
> table(A11)

    34     35     36     37     38     39     40     41     42     43
     2     17     40    129    334    883   2026   4522   8766  15786
    44     45     46     47     48     49     50     51     52     53
 26850  42142  59535  78851  96217 107686 112438 108237  95761  78737
    54     55     56     57     58     59     60     61     62     63
 59732  41474  26939  16006   8827   4633   2050    865    340    116
    64     65     66     67
    38     13      7      1
>

For a  2x2  table, there's really only one degree of freedom,
hence the above characterizes the full distribution for that
case.

I would have expected to see all possible values in  0:100
instead of such a "normal like" distribution with carrier only
in [34, 67].

There are newer publications and maybe algorithms.
So maybe the algorithm is "flawed by design" for really large
total number of observations, rather than wrong
Seems interesting ...

Martin Maechler

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