mcmodel: Monte Carlo model

In mc2d: Tools for Two-Dimensional Monte-Carlo Simulations

Monte Carlo model

Description

Specify a ‘⁠mcmodel⁠’, without evaluating it, for a further evaluation using evalmcmod.

Usage

mcmodel(x, is.expr=FALSE)

Arguments

x	An R call or an expression.
is.expr	‘⁠FALSE⁠’ to send a call, ‘⁠TRUE⁠’ to send an expression (see Examples)

Details

The model should be put between ‘⁠{⁠’ and the last line should be of the form ‘⁠mc(...)⁠’. Any reference to the number of simulation in the dimension of variability should be done via ‘⁠ndvar()⁠’ or (preferred) ‘⁠nsv⁠’. Any reference to the number of simulations in the dimension of uncertainty should be done via ‘⁠ndunc()⁠’ or (preferred) ‘⁠nsu⁠’.

Value

an R expression, with class ‘⁠mcmodel⁠’

See Also

expression.

evalmcmod to evaluate the model.

mcmodelcut to evaluate high Dimension Monte Carlo Model in a loop.

Examples

modEC1 <- mcmodel({
 conc <- mcdata(10, "0")
 cook <- mcstoc(rempiricalD, values=c(0, 1/5, 1/50), prob=c(0.027, 0.373, 0.600))
 serving <- mcstoc(rgamma, shape=3.93, rate=0.0806)
 expo <- conc * cook * serving
 dose <- mcstoc(rpois, lambda=expo)
 risk <- 1-(1-0.001)^dose
 mc(conc, cook, serving, expo, dose, risk)
 })
evalmcmod(modEC1, nsv=100, nsu=100)

mc2d documentation built on July 26, 2023, 6:07 p.m.