Description Usage Arguments Value References See Also

View source: R/intercorr_cont_pois.R

This function calculates a `k_cont x k_pois`

intermediate matrix of correlations for the `k_cont`

continuous and
`k_pois`

Poisson variables. It extends the method of Amatya & Demirtas (2015, doi: 10.1080/00949655.2014.953534) to continuous
variables generated using Headrick's fifth-order polynomial transformation and zero-inflated Poisson variables. Here, the
intermediate correlation between Z1 and Z2 (where Z1 is the standard normal variable transformed using Headrick's fifth-order or
Fleishman's third-order method to produce a continuous variable Y1, and Z2 is the standard normal variable used to generate a
Poisson variable via the inverse CDF method) is calculated by dividing the target correlation by a correction factor. The
correction factor is the product of the upper Frechet-Hoeffding bound on the correlation between a Poisson variable and the
normal variable used to generate it and the power method correlation (described in Headrick & Kowalchuk, 2007,
doi: 10.1080/10629360600605065) between Y1 and Z1. The function is used in `intercorr`

and
`corrvar`

. This function would not ordinarily be called by the user.

1 2 3 | ```
intercorr_cont_pois(method = c("Fleishman", "Polynomial"), constants = NULL,
rho_cont_pois = NULL, lam = NULL, p_zip = 0, nrand = 100000,
seed = 1234)
``` |

`method` |
the method used to generate the |

`constants` |
a matrix with |

`rho_cont_pois` |
a |

`lam` |
a vector of lambda (mean > 0) constants for the regular and zero-inflated Poisson variables (see |

`p_zip` |
a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the
zero-inflated Poisson variables (see |

`nrand` |
the number of random numbers to generate in calculating the bound (default = 10000) |

`seed` |
the seed used in random number generation (default = 1234) |

a `k_cont x k_pois`

matrix whose rows represent the `k_cont`

continuous variables and columns represent the
`k_pois`

Poisson variables

Amatya A & Demirtas H (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals. Journal of Statistical Computation and Simulation, 85(15):3129-39. doi: 10.1080/00949655.2014.953534.

Demirtas H & Hedeker D (2011). A practical way for computing approximate lower and upper correlation bounds. American Statistician, 65(2):104-109. doi: 10.1198/tast.2011.10090.

Frechet M (1951). Sur les tableaux de correlation dont les marges sont donnees. Ann. l'Univ. Lyon SectA, 14:53-77.

Headrick TC, Kowalchuk RK (2007). The Power Method Transformation: Its Probability Density Function, Distribution Function, and Its Further Use for Fitting Data. Journal of Statistical Computation and Simulation, 77:229-249. doi: 10.1080/10629360600605065.

Hoeffding W. Scale-invariant correlation theory. In: Fisher NI, Sen PK, editors. The collected works of Wassily Hoeffding. New York: Springer-Verlag; 1994. p. 57-107.

Yahav I & Shmueli G (2012). On Generating Multivariate Poisson Data in Management Science Applications. Applied Stochastic Models in Business and Industry, 28(1):91-102. doi: 10.1002/asmb.901.

Yee TW (2018). VGAM: Vector Generalized Linear and Additive Models. R package version 1.0-5. https://CRAN.R-project.org/package=VGAM.

`power_norm_corr`

, `find_constants`

,
`intercorr`

, `corrvar`

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