R: Monte Carlo model

mcmodel {mc2d} | R Documentation

## 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)

[Package mc2d version 0.1-18 Index]