R/simul1.R

In airbornemint/pogit: Bayesian Variable Selection for a Poisson-Logistic Model

#' Simulated data set
#' 
#' The simulated data set \code{simul1} considers a situation with four
#' binary covariates in both sub-models of the Pogit model, 
#' i.e. \code{X} = \code{W}.
#' The respective design matrix is built by computing all 2^4 possible 0/1 
#' combinations and one observation is generated for each covariate pattern. 
#' The regression effects are set to \code{beta = {0.75,0.5,-2,0,0}} in the 
#' Poisson and to \code{alpha = {2.2,-1.9,0,0,0}} in the logit model.
#' Additionally to the main study sample, validation data are available for
#' each covariate pattern. For details concerning the simulation setup, see 
#' Dvorzak and Wagner (2016). 
#' 
#' @docType data
#' @usage data(simul1)
#' @format A data frame with 16 rows and the following 9 variables: 
#' \describe{
#'  \item{\code{y}}{number of observed counts for each covariate pattern}
#'  \item{\code{E}}{total exposure time}
#'  \item{\code{X.0}}{intercept}
#'  \item{\code{X.1}, \code{X.2}, \code{X.3}, \code{X.4}}{binary covariates}
#'  \item{\code{v}}{number of reported cases for each covariate pattern in 
#'    the validation sample}
#'  \item{\code{m}}{number of true cases subject to the fallible reporting
#'    process (sample size of validation data)}
#' }
#' 
#' @source Dvorzak, M. and Wagner, H. (2016). Sparse Bayesian modelling
#'  of underreported count data. \emph{Statistical Modelling}, \strong{16}(1),
#'  24 - 46, \url{http://dx.doi.org/10.1177/1471082x15588398}.  
#'  
#' @seealso \code{\link{pogitBvs}}
#' @name simul1
#' @keywords datasets
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