intercorr_pois: Calculate Intermediate MVN Correlation for Poisson Variables:...

In SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

Description

This function calculates a k_pois x k_pois intermediate matrix of correlations for the Poisson variables using the method of Yahav & Shmueli (2012, doi: 10.1002/asmb.901). The intermediate correlation between Z1 and Z2 (the standard normal variables used to generate the Poisson variables Y1 and Y2 via the inverse CDF method) is calculated using a logarithmic transformation of the target correlation. First, the upper and lower Frechet-Hoeffding bounds (mincor, maxcor) on ρ_{Y1, Y2} are simulated. Then the intermediate correlation is found as follows:

ρ_{Z1, Z2} = \frac{1}{b} * log(\frac{ρ_{Y1, Y2} - c}{a}),

where a = -(maxcor * mincor)/(maxcor + mincor), b = log((maxcor + a)/a), and c = -a. The function adapts code from Amatya & Demirtas' (2016) package PoisNor-package by:

1) allowing specifications for the number of random variates and the seed for reproducibility

2) providing the following checks: if Sigma_(Z1, Z2) > 1, Sigma_(Z1, Z2) is set to 1; if Sigma_(Z1, Z2) < -1, Sigma_(Z1, Z2) is set to -1

3) simulating regular and zero-inflated Poisson variables.

The function is used in intercorr and corrvar and would not ordinarily be called by the user.

Usage

intercorr_pois(rho_pois = NULL, lam = NULL, p_zip = 0, nrand = 100000,
  seed = 1234)

Arguments

rho_pois	a k_pois x k_pois matrix of target correlations ordered 1st regular and 2nd zero-inflated
lam	a vector of lambda (mean > 0) constants for the regular and zero-inflated Poisson variables (see stats::dpois); the order should be 1st regular Poisson variables, 2nd zero-inflated Poisson variables
p_zip	a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the zero-inflated Poisson variables (see VGAM::dzipois); if p_zip = 0, Y_{pois} has a regular Poisson distribution; if p_zip is in (0, 1), Y_{pois} has a zero-inflated Poisson distribution; if p_zip is in (-(exp(lam) - 1)^(-1), 0), Y_{pois} has a zero-deflated Poisson distribution and p_zip is not a probability; if p_zip = -(exp(lam) - 1)^(-1), Y_{pois} has a positive-Poisson distribution (see VGAM::dpospois); if length(p_zip) < length(lam), the missing values are set to 0 (and ordered 1st)
nrand	the number of random numbers to generate in calculating the bound (default = 10000)
seed	the seed used in random number generation (default = 1234)

Value

the k_pois x k_pois intermediate correlation matrix for the Poisson variables

References

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.

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

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.

See Also

intercorr_nb, intercorr_pois_nb, intercorr, corrvar

SimCorrMix documentation built on May 2, 2019, 1:24 p.m.