Data Generation with Count, Binary and Continuous Components

Description

Provides R functions for generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Continuous variables can be of any nonnormal shape allowed by the Fleishman polynomials, taking the normal distribution as a special case.

Details

The package consists of fourteen functions. The functions validation.bin, validation.corr, and validation.skewness.kurtosis validate the specified quantities. correlation.limits returns the lower and upper bounds of pairwise correlations of Poisson, binary and continuous variables. correlation.bound.check validates pairwise correlation values. intermediate.corr.PP, intermediate.corr.BB, intermediate.corr.CC, intermediate.corr.PB, intermediate.corr.PC, and intermediate.corr.BC compute intermediate correlation matrix for Poisson-Poisson combinations, binary-binary, continuous-continuous, Poisson-binary, Poisson-continuous, binary-continuous combinations, respectively. The function overall.corr.mat assembles the final correlation matrix. The engine function gen.PoisBinNonNor 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 continuous variables should be placed.

Author(s)

Gul Inan, Hakan Demirtas

Maintainer: Gul Inan <inanx002@umn.edu>

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.

Results