| BinNonNor-package {BinNonNor} | R Documentation |
Data Generation with Binary and Continuous Non-Normal Components
Description
Provides R functions for generation of multiple binary and continuous non-normal variables simultaneously given the marginal characteristics and association structure based on the methodology proposed by Demirtas et al. (2012).
Details
| Package: | BinNonNor |
| Type: | Package |
| Version: | 1.5.3 |
| Date: | 2021-03-21 |
| License: | GPL-2 | GPL-3 |
This package consists of eleven functions. The functions validation.bin,
validation.corr, and validation.skewness.kurtosis validate the specified quantities to avoid obvious specification errors. The function
fleishman.coef computes the coefficients of the third order Fleishman polynomials that are used to simulate the continuous non-normal variables.
correlation.limits returns the lower and upper bounds of the pairwise correlation of binary and binary
and binary and continuous non-normal, and continuous non-normal and continuous non-normal pairs 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 Int.Corr.NN, Tetra.Corr.BB, and Biserial.Corr.BN
computes intermediate correlation matrix for continuous non-normal and continuous non-normal combinations,
tetrachoric correlations for binary and binary combinations, and biserial correlations for binary and continuous non-normal combinations, respectively.
The function overall.corr.mat assembles the final correlation matrix. The engine function gen.Bin.NonNor
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 binary variables, and then continuous variables should be placed.
Author(s)
Gul Inan, Hakan Demirtas, Ran Gao
Maintainer: Ran Gao <rgao8@uic.edu>
References
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.