PoisBinOrd-package {PoisBinOrd}

Data Generation with Count, Binary and Ordinal Components

Description

Provides R functions for generation of multiple count, binary and ordinal variables simultaneously given the marginal characteristics and association structure.

Details

Package:	PoisBinOrd
Type:	Package
Version:	1.4.3
Date:	2021-03-21
License:	GPL-2 | GPL-3

PoisBinOrd package consists of ten functions. The functions validation.bin, validation.ord, and validation.corr validate the specified quantities to prevent users from committing obvious specification errors. correlation.limits returns the lower and upper bounds of the pairwise correlation of Poisson-Poisson, Poisson-binary, Poisson-ordinal, binary-binary, binary-ordinal, and ordinal-ordinal combinations given their marginal distributions, i.e. returns the range of feasible pairwise correlations. The function correlation.bound.check checks the validity of the values of pairwise correlations. The functions intermediate.corr.PP, intermediate.corr.BO, and intermediate.corr.PBO computes intermediate correlation matrix for Poisson-Poisson combinations, binary/ordinal and binary/ordinal combinations, and Poisson and binary/ordinal combinations, respectively. The function overall.corr.mat assembles the final correlation matrix. The engine function gen.PoisBinOrd generates mixed data in accordance with the specified marginal and correlational quantities. Throughout the package, variables are supposed to be inputted in a certain order, namely, first count variables, next binary variables, and then ordinal variables should be placed.

Author(s)

Gul Inan, Hakan Demirtas, Ran Gao

Maintainer: Ran Gao <[e-mail]>

References

Amatya, A. and Demirtas, H. (2015). Simultaneous generation of multivariate mixed data with Poisson and normal marginals. Journal of Statistical Computation and Simulation, (85)15, 3129-3139.

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.

Demirtas, H., Hedeker, D., and Mermelstein, R.J. (2012). Simulation of massive public health data by power polynomials. Statistics in Medicine, 31(27), 3337-3346.

Ferrari, P.A. and Barbiero, A. (2012). Simulating ordinal data. Multivariate Behavioral Research, 47(4), 566-589.