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#' C++ Sampler for Standard (Nonhierarchical) MPT Models
#'
#' Fast Gibbs sampler in C++ that is tailored to the standard fixed-effects MPT
#' model (i.e., fixed-effects, non-hierarchical MPT). Assumes independent
#' parameters per person if a matrix of frequencies per person is supplied.
#'
#' @inheritParams betaMPT
#' @inheritParams betaMPTcpp
#' @param alpha first shape parameter(s) for the beta prior-distribution of the
#' MPT parameters \eqn{\theta_s} (can be a named vector to use a different
#' prior for each MPT parameter)
#' @param beta second shape parameter(s)
#'
#' @details Beta distributions with fixed shape parameters \eqn{\alpha} and
#' \eqn{\beta} are used. The default \eqn{\alpha=1} and \eqn{\beta=1} assumes
#' uniform priors for all MPT parameters.
#' @author Daniel Heck
#'
#' @examples
#' \dontrun{
#' # fit nonhierarchical MPT model for aggregated data (see ?arnold2013):
#' EQNfile <- system.file("MPTmodels/2htsm.eqn", package = "TreeBUGS")
#' d.encoding <- subset(arnold2013, group == "encoding", select = -(1:4))
#' fit <- simpleMPT(EQNfile, colSums(d.encoding),
#' restrictions = list("D1=D2=D3", "d1=d2", "a=g")
#' )
#' # convergence
#' plot(fit)
#' summary(fit)
#' }
#' @importFrom parallel parLapply stopCluster detectCores
#' @export
simpleMPT <- function(
eqnfile,
data,
restrictions,
n.iter = 2000,
n.burnin = 500,
n.thin = 3,
n.chains = 3,
ppp = 0,
alpha = 1,
beta = 1,
parEstFile,
posteriorFile,
cores = 1
) {
hyperprior <- list(alpha = alpha, beta = beta)
if (!is.character(data) && is.null(dim(data))) {
data <- matrix(data, nrow = 1, dimnames = list(NULL, names(data)))
}
fittedModel <- fitModelCpp("simpleMPT",
eqnfile = eqnfile,
data = data, restrictions = restrictions,
hyperprior = hyperprior,
n.iter = n.iter,
n.burnin = n.burnin, n.thin = n.thin,
n.chains = n.chains, ppp = ppp,
parEstFile = parEstFile,
posteriorFile = posteriorFile,
call = match.call(),
cores = cores
)
fittedModel
}
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