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