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)
sample_size <- 100
cluster_size <- 4
beta_intercepts <- 0
beta_coefficients <- 0.2
latent_correlation_matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
x <- rep(rnorm(sample_size), each = cluster_size)
simulated_binary_responses <- rbin(clsize = cluster_size, intercepts = beta_intercepts, 
    betas = beta_coefficients, xformula = ~x, cor.matrix = latent_correlation_matrix, 
    link = "probit")
library(gee)
binary_gee_model <- gee(y ~ x, family = binomial("probit"), id = id, data = simulated_binary_responses$simdata)
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)           x 
#>   0.1315121   0.2826005
summary(binary_gee_model)$coefficients
#>              Estimate Naive S.E.  Naive z Robust S.E. Robust z
#> (Intercept) 0.1315121 0.06399465 2.055048   0.1106696 1.188331
#> x           0.2826006 0.07191931 3.929412   0.1270285 2.224703

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.