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

[Thierry Onkelinx]

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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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.
>
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>
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