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#' @title A model variable whose uncertainty follows a log Normal distribution
#' @description An R6 class representing a model variable with log Normal
#' uncertainty.
#' @details A model variable for which the uncertainty in the point estimate can
#' be modelled with a log Normal distribution. One of seven parametrizations
#' defined by Swat \emph{et al} can be used. Inherits from \code{ModVar}.
#' @references{
#' Briggs A, Claxton K and Sculpher M. Decision Modelling for Health
#' Economic Evaluation. Oxford 2006, ISBN 978-0-19-852662-9.
#'
#' Leaper DJ, Edmiston CE and Holy CE. Meta-analysis of the potential
#' economic impact following introduction of absorbable antimicrobial
#' sutures. \emph{British Journal of Surgery} 2017;\bold{104}:e134-e144.
#'
#' Swat MJ, Grenon P and Wimalaratne S. Ontology and Knowledge Base of
#' Probability Distributions. \emph{Bioinformatics} 2016;\bold{32}:2719-2721,
#' \doi{10.1093/bioinformatics/btw170}.
#' }
#' @docType class
#' @author Andrew J. Sims \email{andrew.sims@@newcastle.ac.uk}
#' @export
LogNormModVar <- R6::R6Class(
classname = "LogNormModVar",
lock_class = TRUE,
inherit = ModVar,
private = list(
),
public = list(
#' @description Create a model variable with log normal uncertainty.
#' @seealso \code{\link{LogNormDistribution}}.
#' @param description A character string describing the variable.
#' @param units Units of the quantity; character string.
#' @param p1 First hyperparameter, a measure of location.
#' See \emph{Details}.
#' @param p2 Second hyperparameter, a measure of spread.
#' See \emph{Details}.
#' @param parametrization A character string taking one of the values
#' \verb{"LN1"} (default) through \verb{"LN7"} (see \emph{Details}).
#' @return A \code{LogNormModVar} object.
initialize = function(description, units, p1, p2, parametrization = "LN1") {
# create a log normal distribution and check parameters
D <- LogNormDistribution$new(p1 = p1, p2 = p2, parametrization)
# initialize the base class
super$initialize(description, units, D = D, k = 1L)
# return new object
return(invisible(self))
},
#' @description Tests whether the model variable is probabilistic, i.e., a
#' random variable that follows a distribution, or an expression involving
#' random variables, some of which follow distributions.
#' @return \code{TRUE} if probabilistic
is_probabilistic = function() {
return(TRUE)
}
)
)
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