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.
| Package: | PoisBinNonNor |
| Type: | Package |
| Version: | 1.3.3 |
| Date: | 2021-03-21 |
| License: | GPL-2 | GPL-3 |
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.
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.