Package: |
PoisBinOrdNor |
Type: |
Package |
Version: |
1.3 |
Date: |
2017-01-25 |
License: |
GPL-2 | GPL-3 |
validation_specs
validates the specificed quantities to avoid obvious specification errors.
The functions corr.nn4bb
, corr.nn4bn
, corr.nn4on
, corr.nn4pbo
, corr.nn4pn
, and corr.nn4pp
each computes the intermediate correlation coefficient for binary-binary combinations, binary-normal combinations, ordinal-normal combinations, count-binary/ordinal combinations,
count-normal and count-count combinations, respectively.
The function intermat
assembles the intermediate correlation matrix for the multivaraite data based on input from functions corr.nn4bb
,
corr.nn4bn
, corr.nn4on
, corr.nn4pbo
, corr.nn4pn
and corr.nn4pp
.
The engine function genPBONdata
computes the final correlation matrix and generates mixed data in accordance with the specified marginal and correlational quantities.
Demirtas, H. & Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.
Demirtas, H. & Doganay, B. (2012). Simultaneous generation of binary and normal data with specified marginal and association structures. Journal of Biopharmaceutical Statistics, 22(2), 223-236.
Demirtas, H., Hedeker, D. & Mermelstein, R. J. (2012). Simulation of massive public health data by power polynomials. Statistics in Medicine, 31(27), 3337-3346.
Demirtas, H. & Yavuz, Y. (2015). Concurrent generation of ordinal and normal data. Journal of Biopharmaceutical Statistics, 25(4), 635-650.
Demirtas, H. & Hedeker, D. (2016). Computing the point-biserial correlation under any underlying continuous distribution. Forthcoming in Communications in Statistics--Simulation and Computation.
Ferrari, P.A. and Barberio, A. (2012). Simulating ordinal data. Multivariate Behavioral Research, 47(4), 566-589.
Yahav, I. & Shmueli, G. (2012). On generating multivariate Poisson data in management science applications. Applied Stochastic Models in Business and Industry, 28(1), 91-102.