The SimCorrMix package generates correlated continuous (normal, non-normal, and mix ture), binary, ordinal, and count (regular and zero-inflated, Poisson and Negative Binomial) variables that mimic real-world data sets. Continuous variables are simulated using either Fleishman’s third order or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture distributions, and the target correlation matrix is specified in terms of correlations with components. However, the package contains functions to approximate expected correlations with continuous mixture variables. There are two simulation pathways which calculate intermediate correlations involving count variables differently, increasing accuracy under a wide range of parameters. The package also provides functions to calculate cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. SimCorrMix is an important addition to existing R simulation packages because it is the first to include continuous mixture and zero-inflated count variables in correlated data sets.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-022.zip
AdaptGauss, DPP, bgmm, ClusterR, mclust, mixture, AdMit, bimixt, bmixture, CAMAN, flexmix, mixdist, mixtools, nspmix, MixtureInf, Rmixmod, hurdlr, zic, mixpack, distr, stats, rebmix, SimCorrMix, SimMultiCorrData, GenOrd, VGAM, Matrix, ggplot2, mvtnorm
Cluster, Distributions, Multivariate, Bayesian, Environmetrics, Econometrics, Psychometrics, ExtremeValue, Finance, Graphics, MetaAnalysis, NumericalMathematics, Phylogenetics, Robust, SocialSciences, Survival, TeachingStatistics
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Fialkowski & Tiwari, "SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions", The R Journal, 2019
BibTeX citation
@article{RJ-2019-022,
author = {Fialkowski, Allison and Tiwari, Hemant},
title = {SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-022},
doi = {10.32614/RJ-2019-022},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {250-286}
}