| BinOrdNonNor-package {BinOrdNonNor} | R Documentation |
Concurrent generation of binary, ordinal and continuous data
Description
This package implements a procedure for generating samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions based on the methodology proposed by Demirtas et al. (2012) and its extensions.
This package consists of nine functions. The function Fleishman.coef.NN computes the Fleishman coefficients for each continuous variable with pre-specified skewness and kurtosis values. The functions LimitforNN and LimitforONN return the lower and upper correlation bounds of a pairwise correlation between two continuous variables, and between a binary/ordinal variable and a continuous variable, respectively. The function valid.limits.BinOrdNN computes the lower and upper bounds for the correlation entries based on the marginal distributions of the variables. The function validate.target.cormat.BinOrdNN checks the validity of the values of pairwise correlations. The function IntermediateNonNor and IntermediateONN compute the intermediate correlations for continuous pairs, and binary/ordinal-continuous pairs, respectively. The function cmat.star.BinOrdNN assembles the intermediate correlation matrix. The engine function genBinOrdNN generates mixed data in accordance with a given correlation matrix and marginal distributions.
The key packages and functions that we call in this package include GenOrd, OrdNor, BBsolve, rmvnorm, and nearPD.
Details
| Package: | BinOrdNonNor |
| Type: | Package |
| Version: | 1.5.2 |
| Date: | 2021-03-21 |
| License: | GPL-2 | GPL-3 |
Author(s)
Hakan Demirtas, Yue Wang, Rawan Allozi, Ran Gao
Maintainer: Ran Gao <rgao8@uic.edu>
References
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. and Hedeker, D. (2016). Computing the point-biserial correlation under any underlying continuous distribution. Communications in Statistics - Simulation and Computation, 45(8), 2744-2751.
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.
Demirtas, H. and Yavuz Y. (2015). Concurrent generation of ordinal and normal data. Journal of Biopharmaceutical Statistics, 25(4), 635-650.
Fleishman, A.I. (1978). A method for simulating non-normal distributions. Psychometrika, 43(4), 521-532.
Vale, C.D., and Maurelli, V.A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48(3), 465-471.