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:
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:
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.