R help - Feb 2009 - Simulating contingency table (Basic question, help please)

I'd like to carry out a Monte Carlo simulation test where given data is a
contingency table. I think this is something to do with using rmultinonom(),
but I'm not sure how to code this, to simulate contingency tables. Could
anyone please help with how to use R to simulate contingency tables like
this?
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On Tue, 24 Feb 2009, bbbnc wrote:
> I'd like to carry out a Monte Carlo simulation test where given data is
a
> contingency table. I think this is something to do with using
rmultinonom(),
> but I'm not sure how to code this, to simulate contingency tables.
Could
> anyone please help with how to use R to simulate contingency tables like
> this?
If you want a two-way table with certain fixed margins, you can use 
r2dtable() which implements the Patefield algorithm. For testing 
(conditional) independence of two variables based on r2dtable(), you can 
use coindep_test() in package "vcd".
Z
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________________________________________
From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On
Behalf Of bbbnc [benwong7 at gmail.com]
Sent: Wednesday, February 25, 2009 6:01 AM
To: r-help at r-project.org
Subject: [R]  Simulating contingency table (Basic question, help please)

I'd like to carry out a Monte Carlo simulation test where given data is a
contingency table. I think this is something to do with using rmultinonom(),
but I'm not sure how to code this, to simulate contingency tables. Could
anyone please help with how to use R to simulate contingency tables like
this?


Let m be your matrix (contingency table).

Do something like:
> p <- (rowSums(m)%o%colSums(m))/(sum(m)^2)
> X <- rmultinom(2000,sum(m),p)
and then
> simstat <-
apply(X,2,function(x,k){chisq.test(matrix(x,nrow=k))$statistic},k=nrow(m))
will give you simulated chi-squared statistics.

If you want some other statistic then obviously you need to modify the function
which is applied to the columns of X.

Note that the simulation is done under the usual contingency table null
hypothesis of independence or
homogeneity.  If you have something else in mind you'll need to modify
accordingly.

    cheers,

         Rolf Turner
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