# [R] Simulate I x J contingency tables using correlation coefficient using bivariate normal distribution

Sri Priya @r|@chocho @end|ng |rom gm@||@com
Mon Aug 19 18:24:26 CEST 2019

```Dear Thierry,

Thank you very much for your answer.  I got some idea about generating
tables from bivariate normal from your code. But still there will be an
argument to choose the lambda value. So, I am thinking whether to generate
count data using margial CDF? Because one may wish to test the independence
from any contingency table. I am planning to test the same for this kind of
table versus multinomial tables.

Once again thank you for your timely response.

Regards
Sripriya

On Mon, Aug 19, 2019 at 2:53 PM Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> Dear Sripriya,
>
> Step 1. generate random values from a multivariate normal distribution
> Step 2. convert the random values into probabilities
> Step 3. convert the probabilities into values from the target distribution
>
> library(mvtnorm)
> n <- 1e3
> correl <- 0.9
> lambda <- c(10, 50)
> sigma <- matrix(correl, ncol = length(lambda), nrow = length(lambda))
> diag(sigma) <- 1
> binorm <- rmvnorm(n, sigma = sigma)
> bip <- apply(binorm, 2, pnorm)
> bipois <- sapply(
>   seq_along(lambda),
>   function(i) {
>     qpois(bip[, i], lambda = lambda[i])
>   }
> )
> plot(bipois)
> table(data.frame(bipois))
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
>
> ///////////////////////////////////////////////////////////////////////////////////////////
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> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op ma 19 aug. 2019 om 10:29 schreef Sri Priya <sri.chocho using gmail.com>:
>
>> Dear R Users,
>>
>> I am interested in generating contingency tables from bivariate normal
>> distribution using different correlation coefficient values.
>>
>> I am experimenting numerous ways of generating contingency tables, and one
>> possible way is to generate from multinomial distribution.
>>
>> I wonder how to generate the count variables from a continuous
>> distribution
>> using correlation structure. Generating bivariate normal variables can be
>> easily done using mvrnorm() in R. I am struggling to write R code for
>> generating count from continuous variables.
>>
>> Any suggestions is very much appreciated.
>>
>> Thanks.
>> Sripriya.
>>
>>         [[alternative HTML version deleted]]
>>
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>

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