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#' Simulated data set
#'
#' The same simulation setup is used as in \code{\link{simul_pois1}} but considers
#' clustered observations. 10 regressors are generated, six of them continuous
#' N(0,1)-variables and four binary with \eqn{p(x_i)=0.5}.
#' The regression effects are set to \code{beta = {2,1,0.6,0,0,1.2,0,0,0.4,-0.2,0.3}}.
#' To simulate clustering, it is assumed that each of
#' C=10 clusters is formed of 30 subjects and 10 random intercepts are generated
#' from a normal distribution with zero mean and standard deviation
#' \eqn{\theta} = 0.1.
#'
#' @docType data
#' @usage data(simul_pois2)
#' @format A data frame with 300 rows and the following 12 variables:
#' \describe{
#' \item{\code{y}}{number of counts for each covariate pattern in each cluster}
#' \item{\code{cID}}{cluster ID of each count}
#' \item{\code{X.0}}{intercept}
#' \item{\code{X.1}, \code{X.2}, \code{X.3}, \code{X.4}, \code{X.5}, \code{X.6}, \code{X.7}, \code{X.8}, \code{X.9}, \code{X.10}}{covariates}
#' }
#'
#' @seealso \code{\link{simul_pois1}}, \code{\link{poissonBvs}}
#' @name simul_pois2
#' @keywords datasets
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