SimCorMultRes: Simulates Correlated Multinomial Responses

Installation

You can install the release version of SimCorMultRes:

install.packages("SimCorMultRes")

The source code for the release version of SimCorMultRes is available on CRAN at:

• https://CRAN.R-project.org/package=SimCorMultRes

Or you can install the development version of SimCorMultRes:

# install.packages('devtools')
devtools::install_github("AnestisTouloumis/SimCorMultRes")

The source code for the development version of SimCorMultRes is available on github at:

• https://github.com/AnestisTouloumis/SimCorMultRes

To use SimCorMultRes, you should load the package as follows:

library(SimCorMultRes)

Usage and functions

This package provides functions to simulate correlated binary, ordinal and nominal responses, which are drawn as realizations of a latent regression model for continuous random vectors as proposed by Touloumis (2016).

There are five core functions:

• rbin to simulate correlated binary responses,
• rmult.bcl to simulate correlated nominal multinomial responses,
• rmult.acl to simulate correlated ordinal responses under a marginal adjacent-category logit model,
• rmult.clm to simulate correlated ordinal responses under a marginal cumulative link model,
• rmult.clm to simulate correlated ordinal responses under a marginal continuation-ratio link model.

There are also two utility functions:

• rnorta for simulating continuous or discrete random vectors with prescribed marginal distributions using the NORTA method,
• rsmvnorm for simulating continuous random vectors from a multivariate normal distribution.

Example

The following R code illustrates how to use the core function rbin:

## See Example 3.5 in the Vignette.
set.seed(123)
N <- 5000
clsize <- 4
intercepts <- 0
betas <- 0.2
cor.matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
x <- rep(rnorm(N), each = clsize)
CorBinRes <- rbin(clsize = clsize, intercepts = intercepts, betas = betas, xformula = ~x, 
    cor.matrix = cor.matrix, link = "probit")
library(gee)
binGEEmod <- gee(y ~ x, family = binomial("probit"), id = id, data = CorBinRes$simdata)
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)           x 
#> 0.002636705 0.204827031
summary(binGEEmod)$coefficients
#>                Estimate  Naive S.E.    Naive z Robust S.E.   Robust z
#> (Intercept) 0.002636705 0.008929290  0.2952872  0.01572132  0.1677153
#> x           0.204827031 0.009114596 22.4724192  0.01610695 12.7166857

Additional examples can be found in Touloumis (2016) and in the vignette of SimCorMultRes.

browseVignettes("SimCorMultRes")

How to cite

To cite R package SimCorMultRes in publications, please use:

  Touloumis, A. (2016). Simulating Correlated Binary and
  Multinomial Responses under Marginal Model Specification: The
  SimCorMultRes Package. The R Journal 8:2, 79-91.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Simulating Correlated Binary and Multinomial Responses under 
         Marginal Model Specification: The SimCorMultRes Package},
    author = {Anestis Touloumis},
    year = {2016},
    journal = {The R Journal},
    volume = {8},
    number = {2},
    pages = {79-91},
    url = {https://journal.r-project.org/archive/2016/RJ-2016-034/index.html},
  }

References

Touloumis, A. (2016) Simulating Correlated Binary and Multinomial Responses under Marginal Model Specification: The SimCorMultRes Package. The R Journal, 8, 79–91.