Author: Anestis Touloumis
Date: 2023-07-10

# Simulating Correlated Binary and Multinomial Responses with SimCorMultRes

#### Anestis Touloumis

#### 10 July 2023

# 1 Introduction

The R package `SimCorMultRes` is suitable for simulation of correlated binary responses (exactly two response categories) and of correlated nominal or ordinal multinomial responses (three or more response categories) conditional on a regression model specification for the marginal probabilities of the response categories. A more detailed description of `SimCorMultRes` can be found in Touloumis (2016). This vignette briefly describes the simulation methods proposed by Touloumis (2016), introduces how to simulate ordinal responses under a marginal adjacent-category logit model and illustrates the use of the core functions of `SimCorMultRes`.

# 2 Areas of Applications

This package was created to facilitate the task of carrying out simulation studies and evaluating the performance of statistical methods for estimating the regression parameters in a marginal model with clustered binary and multinomial responses. Examples of such statistical methods include maximum likelihood methods, copula approaches, quasi-least squares approaches, generalized quasi-likelihood methods and generalized estimating equations (GEE) approaches among others (see references in Touloumis 2016).

In addition, `SimCorMultRes` can generate correlated binary and multinomial random variables conditional on a desired dependence structure and known marginal probabilities even if these are not determined by a regression model (see third example in Touloumis 2016) or to explore approximations of association measures for discrete variables that arise as realizations of an underlying continuum (see second example in Touloumis 2016).

# 3 Simulation Methods

Let \(Y_{it}\) be the binary or multinomial response for subject \(i\) (\(i=1,\ldots,N\)) at measurement occasion \(t\) (\(t=1,\ldots,T\)), and let \(\mathbf {x}_{it}\) be the associated covariates vector. We assume that \(Y_{it} \in \{0,1\}\) for binary responses and \(Y_{it} \in \{1,2,\ldots,J\geq 3\}\) for multinomial responses.

## 3.1 Correlated nominal responses

The function `rmult.bcl` simulates nominal responses under the marginal baseline-category logit model \[\begin{equation} \log \left[\frac{\Pr(Y_{it}=j |\mathbf {x}_{it})}{\Pr(Y_{it}=J |\mathbf {x}_{it})}\right]=(\beta_{tj0}-\beta_{tJ0})+(\boldsymbol {\beta}_{tj}-\boldsymbol{\beta}_{tJ})^{\prime} \mathbf {x}_{it}=\beta^{\ast}_{tj0}+\boldsymbol{\beta}^{\ast\prime}_{tj}\mathbf {x}_{it}, \tag{3.1} \end{equation}\] where \(\beta_{tj0}\) is the \(j\)-th category-specific intercept at measurement occasion \(t\) and \(\boldsymbol{\beta}_{tj}\) is the \(j\)-th category-specific parameter vector associated with the covariates at measurement occasion \(t\). The popular identifiability constraints \(\beta_{tJ0}=0\) and \(\boldsymbol{\beta}_{tJ}=\mathbf {0}\) for all \(t\), imply that \(\beta^{\ast}_{tj0}=\beta_{tj0}\) and \(\boldsymbol {\beta}^{\ast}_{tj}=\boldsymbol{\beta}_{tj}\) for all \(t=1,\ldots,T\) and \(j=1,\ldots,J-1\). The threshold \[Y_{it}=j \Leftrightarrow U^{NO}_{itj}=\max \{U^{NO}_{it1},\ldots,U^{NO}_{itJ}\}\] generates clustered nominal responses that satisfy the marginal baseline-category logit model (3.1), where \[U^{NO}_{itj}=\beta_{tj0}+\boldsymbol{\beta}_{tj}^{\prime} \mathbf {x}_{it}+e^{NO}_{itj},\] and where the random variables \(\{e^{NO}_{itj}:i=1,\ldots,N \text{, } t=1,\ldots,T \text{ and } j=1,\ldots,J\}\) satisfy the following conditions:

1. \(e^{NO}_{itj}\) follows the standard extreme value distribution for all \(i\), \(t\) and \(j\) (mean \(=\gamma \approx 0.5772\), where \(\gamma\) is Euler’s constant, and variance \(=\pi^2/6\)).
2. \(e^{NO}_{i_1t_1j_1}\) and \(e^{NO}_{i_2t_2j_2}\) are independent random variables provided that \(i_1 \neq i_2\).
3. \(e^{NO}_{itj_1}\) and \(e^{NO}_{itj_2}\) are independent random variables provided that \(j_1\neq j_2\).

For each subject \(i\), the association structure among the clustered nominal responses \(\{Y_{it}:t=1,\ldots,T\}\) depends on the joint distribution and correlation matrix of \(\{e^{NO}_{itj}:t=1,\ldots,T \text{ and } j=1,\ldots,J\}\). If the random variables \(\{e^{NO}_{itj}:t=1,\ldots,T \text{ and } j=1,\ldots,J\}\) are independent then so are \(\{Y_{it}:t=1,\ldots,T\}\).

Example 3.1 (Simulation of clustered nominal responses using the NORTA method) Suppose the aim is to simulate nominal responses from the marginal baseline-category logit model \[\begin{equation*} \log \left[\frac{\Pr(Y_{it}=j |\mathbf {x}_{it})}{\Pr(Y_{it}=4 |\mathbf {x}_{it})}\right]=\beta_{j0}+ \beta_{j1} {x}_{i1}+ \beta_{j2} {x}_{it2} \end{equation*}\] where \(N=500\), \(T=3\), \((\beta_{10},\beta_{11},\beta_{12},\beta_{20},\beta_{21},\beta_{22},\beta_{30},\beta_{31},\beta_{32})=(1, 3, 2, 1.25, 3.25, 1.75, 0.75, 2.75, 2.25)\) and \(\mathbf {x}_{it}=(x_{i1},x_{it2})^{\prime}\) for all \(i\) and \(t\), with \(x_{i1}\overset{iid}{\sim} N(0,1)\) and \(x_{it2}\overset{iid}{\sim} N(0,1)\). For the dependence structure, suppose that the correlation matrix \(\mathbf{R}\) in the NORTA method has elements \[ \mathbf{R}_{t_1j_1,t_2j_2}=\begin{cases} 1 & \text{if } t_1=t_2 \text{ and } j_1=j_2\\ 0.95 & \text{if } t_1 \neq t_2 \text{ and } j_1=j_2\\ 0 & \text{otherwise }\\ \end{cases} \] for all \(i=1,\ldots,500\).

    # parameter vector
    betas <- c(1, 3, 2, 1.25, 3.25, 1.75, 0.75, 2.75, 2.25, 0, 0, 0)
    # sample size
    sample_size <- 500
    # number of nominal response categories
    categories_no <- 4
    # cluster size
    cluster_size <- 3
    set.seed(1)
    # time-stationary covariate x_{i1}
    x1 <- rep(rnorm(sample_size), each = cluster_size)
    # time-varying covariate x_{it2}
    x2 <- rnorm(sample_size * cluster_size)
    # create covariates dataframe
    xdata <- data.frame(x1, x2)
    set.seed(321)
    library("SimCorMultRes")
    # latent correlation matrix for the NORTA method
    equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
    identity_matrix <- diag(categories_no)
    latent_correlation_matrix <- kronecker(equicorrelation_matrix, identity_matrix)
    # simulation of clustered nominal responses
    simulated_nominal_dataset <- rmult.bcl(clsize = cluster_size, ncategories = categories_no,
        betas = betas, xformula = ~x1 + x2, xdata = xdata, cor.matrix = latent_correlation_matrix)
    suppressPackageStartupMessages(library("multgee"))
    # fitting a GEE model
    nominal_gee_model <- nomLORgee(y ~ x1 + x2, data = simulated_nominal_dataset$simdata,
        id = id, repeated = time, LORstr = "time.exch")
    # checking regression coefficients
    round(coef(nominal_gee_model), 2)
    #> beta10   x1:1   x2:1 beta20   x1:2   x2:2 beta30   x1:3   x2:3
    #>   1.07   3.18   1.99   1.35   3.40   1.70   0.89   3.06   2.22

## 3.2 Correlated ordinal responses

Simulation of clustered ordinal responses is feasible under either a marginal cumulative link model or a marginal continuation-ratio model.

### 3.2.1 Marginal cumulative link model

The function `rmult.clm` simulates ordinal responses under the marginal cumulative link model \[\begin{equation} \Pr(Y_{it}\le j |\mathbf {x}_{it})=F(\beta_{tj0} +\boldsymbol {\beta}^{\prime}_t \mathbf {x}_{it}) \tag{3.2} \end{equation}\] where \(F\) is a cumulative distribution function (cdf), \(\beta_{tj0}\) is the \(j\)-th category-specific intercept at measurement occasion \(t\) and \(\boldsymbol \beta_t\) is the regression parameter vector associated with the covariates at measurement occasion \(t\). The category-specific intercepts at each measurement occasion \(t\) are assumed to be monotone increasing, that is \[-\infty=\beta_{t00} <\beta_{t10} < \beta_{t20} < \cdots < \beta_{t(J-1)0}< \beta_{tJ0}=\infty\] for all \(t\). Using the threshold \[Y_{it}=j \Leftrightarrow \beta_{t(j-1)0} < U^{O1}_{it} \leq \beta_{tj0}\] clustered ordinal responses that satisfy the marginal cumulative link model (3.2) are generated, where \[U^{O1}_{it}=-\boldsymbol{\beta}^{\prime}_t \mathbf {x}_{it}+e^{O1}_{it},\] and where \(\{e^{O1}_{it}:i=1,\ldots,N \text{ and } t=1,\ldots,T\}\) are random variables such that:

1. \(e^{O1}_{it} \sim F\) for all \(i\) and \(t\).
2. \(e^{O1}_{i_1t_1}\) and \(e^{O1}_{i_2t_2}\) are independent random variables provided that \(i_1 \neq i_2\).

For each subject \(i\), the association structure among the clustered ordinal responses \(\{Y_{it}:t=1,\ldots,T\}\) depends on the pairwise bivariate distributions and correlation matrix of \(\{e^{O1}_{it}:t=1,\ldots,T\}\). If the random variables \(\{e^{O1}_{it}:t=1,\ldots,T\}\) are independent then so are \(\{Y_{it}:t=1,\ldots,T\}\).

Example 3.2 (Simulation of clustered ordinal responses conditional on a marginal cumulative probit model with time-varying regression parameters) Suppose the goal is to simulate correlated ordinal responses from the marginal cumulative probit model \[\begin{equation*} \Pr(Y_{it}\le j |\mathbf x_{it})=\Phi(\beta_{j0} + \beta_{t1} {x}_{i}) \end{equation*}\] where \(\Phi\) denotes the cdf of the standard normal distribution (mean \(=0\) and variance \(=1\)), \(N=500\), \(T=4\), \((\beta_{10},\beta_{20},\beta_{30},\beta_{40})=(-1.5,-0.5,0.5,1.5)\), \((\beta_{11},\beta_{21},\beta_{31},\beta_{41})=(1,2,3,4)\) and \(\mathbf x_{it}=x_{i}\overset{iid}{\sim} N(0,1)\) for all \(i\) and \(t\). For the dependence structure, assume that \(\mathbf{e}_i^{O1}=(e^{O1}_{i1},e^{O1}_{i2},e^{O1}_{i3},e^{O1}_{i4})^{\prime}\) are iid random vectors from a tetra-variate normal distribution with mean vector the zero vector and covariance matrix the correlation matrix \[\left( {\begin{array}{*{20}c} 1.00 & 0.85 & 0.50 & 0.15 \\ 0.85 & 1.00 & 0.85 & 0.50 \\ 0.50 & 0.15 & 1.00 & 0.85 \\ 0.15 & 0.85 & 0.50 & 1.00 \end{array} } \right).\]

    set.seed(12345)
    # sample size
    sample_size <- 500
    # cluster size
    cluster_size <- 4
    # category-specific intercepts
    beta_intercepts <- c(-1.5, -0.5, 0.5, 1.5)
    # time-varying regression parameters associated with covariates
    beta_coefficients <- matrix(c(1, 2, 3, 4), 4, 1)
    # time-stationary covariate
    x <- rep(rnorm(sample_size), each = cluster_size)
    # latent correlation matrix for the NORTA method
    latent_correlation_matrix <- toeplitz(c(1, 0.85, 0.5, 0.15))
    # simulation of ordinal responses
    simulated_ordinal_dataset <- rmult.clm(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x, cor.matrix = latent_correlation_matrix,
        link = "probit")
    # first eight rows of the simulated dataframe
    head(simulated_ordinal_dataset$simdata, n = 8)
    #>   y         x id time
    #> 1 1 0.5855288  1    1
    #> 2 2 0.5855288  1    2
    #> 3 2 0.5855288  1    3
    #> 4 1 0.5855288  1    4
    #> 5 1 0.7094660  2    1
    #> 6 1 0.7094660  2    2
    #> 7 1 0.7094660  2    3
    #> 8 1 0.7094660  2    4

### 3.2.2 Marginal continuation-ratio model

The function `rmult.crm` simulates clustered ordinal responses under the marginal continuation-ratio model \[\begin{equation} \Pr(Y_{it}=j |Y_{it} \ge j,\mathbf {x}_{it})=F(\beta_{tj0} +\boldsymbol {\beta}^{'}_t \mathbf {x}_{it}) \tag{3.3} \end{equation}\] where \(\beta_{tj0}\) is the \(j\)-th category-specific intercept at measurement occasion \(t\), \(\boldsymbol \beta_t\) is the regression parameter vector associated with the covariates at measurement occasion \(t\) and \(F\) is a cdf. This is accomplished by utilizing the threshold \[Y_{it}=j, \text{ given } Y_{it} \geq j \Leftrightarrow U^{O2}_{itj} \leq \beta_{tj0}\] where \[U^{O2}_{itj}=-\boldsymbol {\beta}^{\prime}_t \mathbf {x}_{it}+e^{O2}_{itj},\] and where \(\{e^{O2}_{itj}:i=1,\ldots,N \text{ , } t=1,\ldots,T \text{ and } j=1,\ldots,J-1\}\) satisfy the following three conditions:

1. \(e^{O2}_{itj} \sim F\) for all \(i\), \(t\) and \(j\).
2. \(e^{O2}_{i_1t_1j_1}\) and \(e^{O2}_{i_2t_2j_2}\) are independent random variables provided that \(i_1 \neq i_2\).
3. \(e^{O2}_{itj_1}\) and \(e^{O2}_{itj_2}\) are independent random variables provided that \(j_1\neq j_2\).

For each subject \(i\), the association structure among the clustered ordinal responses \(\{Y_{it}:t=1,\ldots,T\}\) depends on the joint distribution and correlation matrix of \(\{e^{O2}_{itj}:j=1,\ldots,J \text{ and } t=1,\ldots,T\}\). If the random variables \(\{e^{O2}_{itj}:j=1,\ldots,J \text{ and } t=1,\ldots,T\}\) are independent then so are \(\{Y_{it}:t=1,\ldots,T\}\).

Example 3.3 (Simulation of clustered ordinal responses conditional on a marginal continuation-ratio probit model) Suppose simulation of clustered ordinal responses under the marginal continuation-ratio probit model \[\begin{equation*} \Pr(Y_{it}=j |Y_{it} \ge j,\mathbf{x}_{it})=\Phi(\beta_{j0} + \beta {x}_{it}) \end{equation*}\] with \(N=500\), \(T=4\), \((\beta_{10},\beta_{20},\beta_{30},\beta_{40},\beta)=(-1.5,-0.5,0.5,1.5,1)\) and \(\mathbf{x}_{it}=x_{it}\overset{iid}{\sim} N(0,1)\) for all \(i\) and \(t\) is desired. For the dependence structure, assume that \(\left\{\mathbf{e}_i^{O2}=\left(e^{O2}_{i11},\ldots,e^{O1}_{i44}\right)^{\prime}:i=1,\ldots,N\right\}\) are iid random vectors from a multivariate normal distribution with mean vector the zero vector and covariance matrix the \(16 \times 16\) correlation matrix with elements\[ \text{corr}(e^{O2}_{it_1j_1},e^{O2}_{it_2j_2}) = \begin{cases} 1 & \text{for } j_1 = j_2 \text{ and } t_1 = t_2\\ 0.24 & \text{for } t_1 \neq t_2\\ 0 & \text{otherwise.}\\ \end{cases} \]

    set.seed(1)
    # sample size
    sample_size <- 500
    # cluster size
    cluster_size <- 4
    # category-specific intercepts
    beta_intercepts <- c(-1.5, -0.5, 0.5, 1.5)
    # regression parameters associated with covariates
    beta_coefficients <- 1
    # time-varying covariate
    x <- rnorm(sample_size * cluster_size)
    # number of ordinal response categories
    categories_no <- 5
    # correlation matrix for the NORTA method
    latent_correlation_matrix <- diag(1, (categories_no - 1) * cluster_size) + kronecker(toeplitz(c(0,
        rep(0.24, categories_no - 2))), matrix(1, cluster_size, cluster_size))
    # simulation of ordinal responses
    simulated_ordinal_dataset <- rmult.crm(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x, cor.matrix = latent_correlation_matrix,
        link = "probit")
    # first six clusters with ordinal responses
    head(simulated_ordinal_dataset$Ysim)
    #>     t=1 t=2 t=3 t=4
    #> i=1   2   1   3   1
    #> i=2   1   4   1   1
    #> i=3   2   2   1   3
    #> i=4   3   5   2   2
    #> i=5   2   1   1   1
    #> i=6   3   3   4   5

### 3.2.3 Marginal adjacent-category logit model

The function `rmult.acl` simulates clustered ordinal responses under the marginal adjacent-category logit model \[\begin{equation} \log\left[\frac{\Pr(Y_{it}=j |\mathbf {x}_{it})}{\Pr(Y_{it}=j+1 |\mathbf {x}_{it})}\right]=\beta_{tj0} +\boldsymbol {\beta}^{'}_t \mathbf {x}_{it} \tag{3.4} \end{equation}\] where \(\beta_{tj0}\) is the \(j\)-th category-specific intercept at measurement occasion \(t\), \(\boldsymbol \beta_t\) is the regression parameter vector associated with the covariates at measurement occasion \(t\).

Generation of clustered ordinal responses relies upon utilizing the connection between baseline-category logit models and adjacent-category logit models. In particular, the threshold \[Y_{it}=j \Leftrightarrow U^{O3}_{itj}=\max \{U^{O3}_{it1},\ldots,U^{O3}_{itJ}\}\] generates clustered nominal responses that satisfy the marginal adjacent-category logit model (3.4), where \[U^{O3}_{itj}=\sum_{k=j}^J\beta_{tk0}+(J-j)\boldsymbol{\beta}_{t}^{\prime} \mathbf {x}_{it}+e^{O3}_{itj},\] and where the random variables \(\{e^{O3}_{itj}:i=1,\ldots,N \text{, } t=1,\ldots,T \text{ and } j=1,\ldots,J\}\) satisfy the following conditions:

1. \(e^{O3}_{itj}\) follows the standard extreme value distribution for all \(i\), \(t\) and \(j\).
2. \(e^{O3}_{i_1t_1j_1}\) and \(e^{O3}_{i_2t_2j_2}\) are independent random variables provided that \(i_1 \neq i_2\).
3. \(e^{O3}_{itj_1}\) and \(e^{O3}_{itj_2}\) are independent random variables provided that \(j_1\neq j_2\).

For each subject \(i\), the association structure among the clustered ordinal responses \(\{Y_{it}:t=1,\ldots,T\}\) depends on the joint distribution and correlation matrix of \(\{e^{O3}_{itj}:t=1,\ldots,T \text{ and } j=1,\ldots,J\}\). If the random variables \(\{e^{O3}_{itj}:t=1,\ldots,T \text{ and } j=1,\ldots,J\}\) are independent then so are \(\{Y_{it}:t=1,\ldots,T\}\).

Example 3.4 (Simulation of clustered ordinal responses conditional on a marginal adjacent-category logit model using the NORTA method) Suppose the aim is to simulate ordinal responses from the marginal adjacent-category logit model \[\begin{equation*} \log \left[\frac{\Pr(Y_{it}=j |\mathbf {x}_{it})}{\Pr(Y_{it}=j+1 |\mathbf {x}_{it})}\right]=\beta_{j0}+ \beta_{1} {x}_{i1}+ \beta_{2} {x}_{it2} \end{equation*}\] where \(N=500\), \(T=3\), \((\beta_{10},\beta_{20},\beta_{30})=(3, 2, 1)\), \((\beta_{1},\beta_{2})=(1, 1)\) and \(\mathbf {x}_{it}=(x_{i1},x_{it2})^{\prime}\) for all \(i\) and \(t\), with \(x_{i1}\overset{iid}{\sim} N(0,1)\) and \(x_{it2}\overset{iid}{\sim} N(0,1)\). For the dependence structure, suppose that the correlation matrix \(\mathbf{R}\) in the NORTA method has elements \[ \mathbf{R}_{t_1j_1,t_2j_2}=\begin{cases} 1 & \text{if } t_1=t_2 \text{ and } j_1=j_2\\ 0.95 & \text{if } t_1 \neq t_2 \text{ and } j_1=j_2\\ 0 & \text{otherwise }\\ \end{cases} \] for all \(i=1,\ldots,500\).

    # intercepts
    beta_intercepts <- c(3, 2, 1)
    # parameter vector
    beta_coefficients <- c(1, 1)
    # sample size
    sample_size <- 500
    # cluster size
    cluster_size <- 3
    set.seed(321)
    # time-stationary covariate x_{i1}
    x1 <- rep(rnorm(sample_size), each = cluster_size)
    # time-varying covariate x_{it2}
    x2 <- rnorm(sample_size * cluster_size)
    # create covariates dataframe
    xdata <- data.frame(x1, x2)
    # correlation matrix for the NORTA method
    equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
    identity_matrix <- diag(4)
    latent_correlation_matrix <- kronecker(equicorrelation_matrix, identity_matrix)
    # simulation of clustered ordinal responses
    simulated_ordinal_dataset <- rmult.acl(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x1 + x2, xdata = xdata, cor.matrix = latent_correlation_matrix)
    suppressPackageStartupMessages(library("multgee"))
    # fitting a GEE model
    ordinal_gee_model <- ordLORgee(y ~ x1 + x2, data = simulated_ordinal_dataset$simdata,
        id = id, repeated = time, LORstr = "time.exch", link = "acl")
    # checking regression coefficients
    round(coef(ordinal_gee_model), 2)
    #> beta10 beta20 beta30     x1     x2
    #>   2.95   1.97   1.67   1.14   1.00

## 3.3 Correlated binary responses

The function `rbin` simulates binary responses under the marginal model specification \[\begin{equation} \Pr(Y_{it}=1 |\mathbf {x}_{it})=F(\beta_{t0} +\boldsymbol {\beta}^{\prime}_{t} \mathbf {x}_{it}) \tag{3.5} \end{equation}\] where \(\beta_{t0}\) is the intercept at measurement occasion \(t\), \(\boldsymbol \beta_t\) is the regression parameter vector associated with the covariates at measurement occasion \(t\) and \(F\) is a cdf. The threshold \[Y_{it}=1 \Leftrightarrow U^{B}_{it} \leq \beta_{t0} + 2 \boldsymbol {\beta}^{\prime}_t \mathbf {x}_{it},\] generates clustered binary responses that satisfy the marginal model (3.5), where \[\begin{equation} U^{B}_{it}=\boldsymbol {\beta}^{\prime}_t \mathbf {x}_{it}+e^{B}_{it}, \tag{3.6} \end{equation}\] and where \(\{e^{B}_{it}:i=1,\ldots,N \text{ and } t=1,\ldots,T\}\) are random variables such that:

1. \(e^{B}_{it} \sim F\) for all \(i\) and \(t\).
2. \(e^{B}_{i_1t_1}\) and \(e^{B}_{i_2t_2}\) are independent random variables provided that \(i_1 \neq i_2\).

For each subject \(i\), the association structure among the clustered binary responses \(\{Y_{it}:t=1,\ldots,T\}\) depends on the pairwise bivariate distributions and correlation matrix of \(\{e^{B}_{it}:t=1,\ldots,T\}\). If the random variables \(\{e^{B}_{it}:t=1,\ldots,T\}\) are independent then so are \(\{Y_{it}:t=1,\ldots,T\}\).

Example 3.5 (Simulation of clustered binary responses conditional on a marginal probit model using NORTA method) Suppose the goal is to simulate clustered binary responses from the marginal probit model \[\Pr(Y_{it}=1 |\mathbf{x}_{it})=\Phi(0.2x_i)\] where \(N=100\), \(T=4\) and \(\mathbf{x}_{it}=x_i\overset{iid}{\sim} N(0,1)\) for all \(i\) and \(t\). For the association structure, assume that the random variables \(\mathbf{e}_i^{B}=(e^{B}_{i1},e^{B}_{i2},e^{B}_{i3},e^{B}_{i4})^{\prime}\) in (3.6) are iid random vectors from the tetra-variate normal distribution with mean vector the zero vector and covariance matrix the correlation matrix \(\mathbf{R}\) given by \[\begin{equation} \mathbf{R}=\left( {\begin{array}{*{20}c} 1.00 & 0.90 & 0.90 & 0.90 \\ 0.90 & 1.00 & 0.90 & 0.90 \\ 0.90 & 0.90 & 1.00 & 0.90 \\ 0.90 & 0.90 & 0.90 & 1.00 \end{array} } \right). \tag{3.7} \end{equation}\] This association configuration defines an exchangeable correlation matrix for the clustered binary responses, i.e. \(\text{corr}(Y_{it_1},Y_{it_2})=\rho_i\) for all \(i\) and \(t\). The strength of the correlation (\(\rho_i\)) is decreasing as the absolute value of the time-stationary covariate \(x_i\) increases. For example, \(\rho_i=0.7128\) when \(x_{i}=0\) and \(\rho_i=0.7\) when \(x_i=3\) or \(x_i=-3\). Therefore, a strong exchangeable correlation pattern for each subject that does not differ much across subjects is implied with this configuration.

    set.seed(123)
    # sample size
    sample_size <- 100
    # cluster size
    cluster_size <- 4
    # intercept
    beta_intercepts <- 0
    # regression parameter associated with the covariate
    beta_coefficients <- 0.2
    # correlation matrix for the NORTA method
    latent_correlation_matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
    # time-stationary covariate
    x <- rep(rnorm(sample_size), each = cluster_size)
    # simulation of clustered binary responses
    simulated_binary_dataset <- rbin(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x, cor.matrix = latent_correlation_matrix,
        link = "probit")
    library("gee")
    # fitting a GEE model
    binary_gee_model <- gee(y ~ x, family = binomial("probit"), id = id, data = simulated_binary_dataset$simdata)
    #> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
    #> running glm to get initial regression estimate
    #> (Intercept)           x
    #>   0.1315121   0.2826005
    # checking the estimated coefficients
    summary(binary_gee_model)$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

Example 3.6 (Simulation of clustered binary responses under a conditional marginal logit model without utilizing the NORTA method) Consider now simulation of correlated binary responses from the marginal logit model \[\begin{equation*} \Pr(Y_{it}=1 |\mathbf{x}_{it})=F(0.2x_i) \end{equation*}\] where \(F\) is the cdf of the standard logistic distribution (mean \(=0\) and variance \(=\pi^2/3\)), \(N=100\), \(T=4\) and \(\mathbf{x}_{it}=x_i\overset{iid}{\sim} N(0,1)\) for all \(i\) and \(t\). This is similar to the marginal model configuration in Example 3.5 except from the link function. For the dependence structure, assume that the correlation matrix of \(\mathbf{e}_i^{B}=(e^{B}_{i1},e^{B}_{i2},e^{B}_{i3},e^{B}_{i4})^{\prime}\) in (3.6) is equal to the correlation matrix \(\mathbf{R}\) defined in (3.7). To simulate \(\mathbf{e}_i^{B}\) without utilizing the NORTA method, one can employ the tetra-variate extreme value distribution (Gumbel 1958). In particular, this is accomplished by setting \(\mathbf{e}_i^{B}=\mathbf{U}_i-\mathbf{V}_i\) for all \(i\), where \(\mathbf{U}_i\) and \(\mathbf{V}_i\) are independent random vectors from the tetra-variate extreme value distribution with dependence parameter equal to \(0.9\), that is \[\Pr\left(U_{i1}\leq u_{i1},U_{i2}\leq u_{i2},U_{i3}\leq u_{i3},U_{i4}\leq u_{i4}\right)=\exp\left\{-\left[\sum_{t=1}^4 \exp{\left(-\frac{u_{it}}{0.9}\right)}\right]^{0.9}\right\}\] and \[\Pr\left(V_{i1}\leq v_{i1},V_{i2}\leq v_{i2},V_{i3}\leq v_{i3},V_{i4}\leq v_{i4}\right)=\exp\left\{-\left[\sum_{t=1}^4 \exp{\left(-\frac{v_{it}}{0.9}\right)}\right]^{0.9}\right\}.\] It follows that \(e_{it}^{B}\sim F\) for all \(i\) and \(t\) and \(\textrm{corr}(\mathbf{e}_i^{B})=\mathbf{R}\) for all \(i\).

    set.seed(8)
    # simulation of epsilon variables
    library("evd")
    simulated_latent_variables1 <- rmvevd(sample_size, dep = sqrt(1 - 0.9), model = "log",
        d = cluster_size)
    simulated_latent_variables2 <- rmvevd(sample_size, dep = sqrt(1 - 0.9), model = "log",
        d = cluster_size)
    simulated_latent_variables <- simulated_latent_variables1 - simulated_latent_variables2
    # simulation of clustered binary responses
    simulated_binary_dataset <- rbin(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x, rlatent = simulated_latent_variables)
    # fitting a GEE model
    binary_gee_model <- gee(y ~ x, family = binomial("logit"), id = id, data = simulated_binary_dataset$simdata)
    #> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
    #> running glm to get initial regression estimate
    #> (Intercept)           x
    #>  0.04146261  0.09562709
    # checking the estimated coefficients
    summary(binary_gee_model)$coefficients
    #>               Estimate Naive S.E.   Naive z Robust S.E.  Robust z
    #> (Intercept) 0.04146261  0.1008516 0.4111249   0.1790511 0.2315686
    #> x           0.09562709  0.1107159 0.8637160   0.1949327 0.4905647

## 3.4 No marginal model specification

To achieve simulation of clustered binary, ordinal and nominal responses under no marginal model specification, perform the following intercepts:

1. Based on the marginal probabilities calculate the intercept of a marginal probit model for binary responses (see Example 3.7) or the category-specific intercepts of a cumulative probit model (see third example in Touloumis 2016) or of a baseline-category logit model for multinomial responses (see Example 3.8).

2. Create a pseudo-covariate say `x` of length equal to the number of cluster size (`clsize`) times the desired number of clusters of simulated responses (say `R`), that is `x = clsize * R`. This step is required in order to identify the desired number of clustered responses.

3. Set `betas = 0` in the core functions `rbin` (see Example 3.7) or `rmult.clm`, or set 0 all values of the `beta` argument that correspond to the category-specific parameters in the core function `rmult.bcl` (see Example 3.8).

4. set `xformula = ~ x`.

5. Run the core function to obtain realizations of the simulated clustered responses.

Example 3.7 (Simulation of clustered binary responses without covariates) Suppose the goal is to simulate \(5000\) clustered binary responses with \(\Pr(Y_{t}=1)=0.8\) for all \(t=1,\ldots,4\). For simplicity, assume that the clustered binary responses are independent.

    set.seed(123)
    # sample size
    sample_size <- 5000
    # cluster size
    cluster_size <- 4
    # intercept
    beta_intercepts <- qnorm(0.8)
    # pseudo-covariate
    x <- rep(0, each = cluster_size * sample_size)
    # regression parameter associated with the covariate
    beta_coefficients <- 0
    # correlation matrix for the NORTA method
    latent_correlation_matrix <- diag(cluster_size)
    # simulation of clustered binary responses
    simulated_binary_dataset <- rbin(clsize = cluster_size, intercepts = beta_intercepts,
        betas = beta_coefficients, xformula = ~x, cor.matrix = latent_correlation_matrix,
        link = "probit")
    # simulated marginal probabilities
    colMeans(simulated_binary_dataset$Ysim)
    #>    t=1    t=2    t=3    t=4
    #> 0.8024 0.7972 0.7948 0.8088

Example 3.8 (Simulation of clustered nominal responses without covariates) Suppose the aim is to simulate \(N=5000\) clustered nominal responses with \(\Pr(Y_{t}=1)=0.1\), \(\Pr(Y_{t}=2)=0.2\), \(\Pr(Y_{t}=3)=0.3\) and \(\Pr(Y_{t}=4)=0.4\), for all \(i\) and \(t=1,\ldots,3\). For the sake of simplicity, we assume that the clustered responses are independent.

    # sample size
    sample_size <- 5000
    # cluster size
    cluster_size <- 3
    # pseudo-covariate
    x <- rep(0, each = cluster_size * sample_size)
    # parameter vector
    betas <- c(log(0.1/0.4), 0, log(0.2/0.4), 0, log(0.3/0.4), 0, 0, 0)
    # number of nominal response categories
    categories_no <- 4
    set.seed(1)
    # correlation matrix for the NORTA method
    latent_correlation_matrix <- kronecker(diag(cluster_size), diag(categories_no))
    # simulation of clustered nominal responses
    simulated_nominal_dataset <- rmult.bcl(clsize = cluster_size, ncategories = categories_no,
        betas = betas, xformula = ~x, cor.matrix = latent_correlation_matrix)
    # simulated marginal probabilities
    apply(simulated_nominal_dataset$Ysim, 2, table)/sample_size
    #>      t=1    t=2    t=3
    #> 1 0.1000 0.0996 0.1036
    #> 2 0.2034 0.2000 0.2000
    #> 3 0.2874 0.3130 0.2894
    #> 4 0.4092 0.3874 0.4070

## 3.5 A note on the correlation matrix

In `SimCorMultRes`, the user provides the correlation matrix (denoted as \(\mathbf{R}\)) for the multivariate normal distribution used in the intermediate step of the NORTA method, rather than the correlation matrix of the latent responses used in the corresponding threshold approach. This choice is motivated by the observation that when all the marginal distributions of the correlated latent responses follow a logistic distribution, the correlation matrix \(\mathbf{R}\) and the correlation matrix of the latent responses are expected to be similar, as noted by Touloumis (2016). Therefore, in `SimCorMultRes`, this approximation is employed irrespective of the marginal distribution of the latent `responses`.

To evaluate the validity of this approximation for the threshold approaches employed in `SimCorMultRes`, a simulation study was conducted. For a fixed sample size \(N\) and a correlation parameter \(\rho\), \(N\) independent bivariate random vectors \(\{\mathbf y_{i}: i = 1, \ldots, N \}\) from a bivariate normal distribution with mean vector the zero vector and covariance matrix the correlation matrix \[ \mathbf R = \begin{bmatrix} 1 & \rho\\ \rho & 1 \end{bmatrix} \] were drawn. The sample correlation was used to estimate \(\rho\). Next, the NORTA method was applied to obtain bivariate random vectors \(\{\mathbf z_{i}: i = 1, \ldots, N \}\) so that their marginal distribution is the logistic distribution. Their correlation parameter, say \(\rho_{z}\), was estimated using the corresponding sample correlation. Then, the NORTA method was applied to obtain bivariate random vectors \(\{\mathbf w_{i}: i = 1, \ldots, N \}\) so that their marginal distribution is the Gumbel distribution. Their correlation parameter, say \(\rho_{w}\), was estimated using their sample correlation.

For a fixed value of \(\rho\), this procedure was replicated \(10,000,000\) times to reduce the simulation error and with \(N=10,000\) to reduce the sampling error. The three correlation parameters \(\rho\), \(\rho_z\) and \(\rho_w\) were estimated using their corresponding Monte Carlo counterparts, denoted by \(\widehat{\rho}\), \(\widehat{\rho}_z\) and \(\widehat{\rho}_w\), respectively. We let \(\rho \in \{ 0, 0.01,0.02,\ldots, 0.99\}\).

The dataframe `simulation` contains the true correlation parameter \(\rho\) (`rho`) and the Monte Carlo estimates \(\widehat{\rho}\) (`normal`), \(\widehat{\rho}_z\) (`logistic`) and \(\widehat{\rho}_w\) (`gumbel`) from the simulation study described above. As expected, \(\widehat{\rho} \approx \rho\) regardless of the strength of the correlation parameter. For the case of logistic marginal distributions, the average difference between \(\rho\) and \(\widehat{\rho}_z\) is 0.002, taking the maximum value of 0.0031 at \(\rho = 0.58\). Therefore \(\rho\) appears to approximate \(\rho_{z}\) to 2 decimal points. For the case of Gumbel marginal distributions, the average difference between \(\rho\) and \(\widehat{\rho}_w\) is 0.0103, taking the maximum value of 0.0154 at \(\rho = 0.51\). Although \(\rho\) appears to approximate well \(\rho_{w}\), there is some accuracy loss compared to \(\rho_{z}\). The plot below shows the differences between the true correlation coefficient of the bivariate normal distribution and the simulated correlations for \(\rho\), \(\rho_z\) or \(\rho_w\).

Figure 3.1: Difference between the correlation parameters of the bivariate normal distribution and of the latent variables for three different marginal distributions.

Overall, there is little accuracy loss by specifying the correlation matrix of the multivariate normal distribution in the intermediate step of the NORTA distribution instead of the correlation matrix of correlated latent responses regardless of whether their marginal distributions are either logistic or Gumbel distributions. Users can treat the correlation matrix passed on to the core functions of `SimCorMultRes` as the correlation matrix of the latent variables.

# 4 How to Cite

    citation("SimCorMultRes")
    To cite '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. R package version 1.9.0,
      <https://journal.r-project.org/archive/2016/RJ-2016-034/index.html>.

    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},
        note = {R package version 1.9.0},
        pages = {79-91},
        url = {https://journal.r-project.org/archive/2016/RJ-2016-034/index.html},
      }

# References

Gumbel, E. J. 1958. Statistics of Extremes. Columbia University Press, New York.
Touloumis, A. 2016. “Simulating Correlated Binary and Multinomial Responses under Marginal Model Specification: The SimCorMultRes Package.” The R Journal 8 (2): 79–91. https://journal.r-project.org/archive/2016/RJ-2016-034/index.html.