'rcounts.reg' is used to sample high-dimensional correlated count random variables with approximate prespecified Pearson correlation and exact margins.

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`N` |
number of observations to be generated per margin (should be at least 500). |

`margins` |
Vector of margin tokens. Its length T is the dimension. See details. |

`mu` |
Matrix of dimension N x T of means for the Poisson, GP, ZIP, ZIGP and NB margins. |

`phi` |
Matrix of dimension N x T of dispersion parameters for the GP, and ZIGP margins. For Poisson, ZIP and NB margins, an 'NA' can be provided. |

`omega` |
Matrix of dimension N x T of zero-inflation parameters for the ZIP and ZIGP margins. For Poisson, GP and NB margins, an 'NA' can be provided. |

`psi` |
Matrix of dimension N x T of size parameters for the NB margins. For Poisson, GP, ZIP and ZIGP margins, an 'NA' can be provided. |

`corstr` |
Correlation structure. Can be 'ex' for exchangeable, 'AR1' for AR(1) and 'unstr' for unstructured. |

`corpar` |
Correlation parameter. Scalar correlation for 'ex' and 'AR1' and matrix of dimension TxT for 'unstr'. |

`conv` |
Convergence criterion |

The entries in 'margins' can be specified as 'Poi' for Poisson, 'GP' for generalized Poisson, 'ZIP' for zero-inflated Poisson, 'ZIGP' for zero-inflated generalized Poisson and 'NB' for negative-binomial.

NOTE: there is a tradeoff between too small N (decreasing accuracy of the resulting correlation) and too high N (dramatically increasing computation time).

The function will return a matrix of counts of dimension N x T.

Vinzenz Erhardt

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N <- 500
# bivariate example
margins <- c("ZIGP","GP")
mu <- matrix(runif(N*2,10,20),N,2)
phi <- matrix(runif(N*2,1,3),N,2)
omega <- matrix(c(runif(N,0,.3),rep(NA,N)),N,2)
corstr <- "ex"
corpar <- .5
Y <- rcounts.reg(N=N, margins=margins, mu=mu, phi=phi, omega=omega,
corstr=corstr, corpar=corpar)
cor(Y)
``` |

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