mccut: Evaluates a Two-Dimensional Monte Carlo Model in a Loop.

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

Evaluates a Two-Dimensional Monte Carlo Model in a Loop.

Description

‘⁠evalmccut⁠’ evaluates a Two-Dimensional Monte Carlo model using a loop on the uncertainty dimension. Within each loop, it calculates statistics in the variability dimension and stores them for further analysis. It allows to evaluate very high dimension models using (unlimited?) time instead of (limited) memory.

‘⁠mcmodelcut⁠’ builds a ‘⁠mcmodelcut⁠’ object that can be sent to ‘⁠evalmccut⁠’.

Usage

evalmccut(model, nsv=ndvar(), nsu=ndunc(), seed=NULL, ind="index")
## S3 method for class 'mccut'
print(x, lim=c(0.025, 0.975), digits=3, ...)
mcmodelcut(x, is.expr=FALSE)

Arguments

model	a ‘⁠mcmodelcut⁠’ object obtained using ‘⁠mcmodelcut⁠’ function or (directly) a valid call including three blocks. See Details and Examples for the structure of the call.
x	a call or an expression (if ‘⁠is.expr=TRUE⁠’) including three blocks. See Details and Examples for the structure of the call.
nsv	The number of simulations for variability used in the evaluation.
nsu	The number of simulations for uncertainty used in the evaluation.
seed	The random seed used for the evaluation. If ‘⁠NULL⁠’ the ‘⁠seed⁠’ is unchanged.
ind	The variable name used in ‘⁠model⁠’ to refers to the uncertainty. see Details and Example.
is.expr	‘⁠FALSE⁠’ to send a call, ‘⁠TRUE⁠’ to send an expression (see mcmodel examples)
lim	A vector of values used for the quantile function (uncertainty dimension).
digits	Number of digits in the print.
...	Additional arguments to be passed in the final print function.

Details

This function should be used for high dimension Two-Dimensional Monte-Carlo simulations, when the memory limits of R are attained. The use of a loop will take (lots of) time, but less memory.

‘⁠x⁠’ (or ‘⁠model⁠’ if a call is used directly in ‘⁠evalmccut⁠’) should be built as three blocks, separated by ‘⁠{⁠’.

1. The first block is evaluated once (and only once) before the first loop (step 1).

2. The second block, which should lead to an ‘⁠mc⁠’ object, is evaluated using ‘⁠nsu = 1⁠’ (step 2).

3. The third block is evaluated on the ‘⁠mc⁠’ object. All resulting statistics are stored (step 3).

4. The steps 2 and 3 are repeated ‘⁠nsu⁠’ times. At each iteration, the values of the loop index (from 1 to ‘⁠nsu⁠’) is given to the variable specified in ‘⁠ind⁠’.

5. Finally, the ‘⁠nsu⁠’ statistics are returned in an invisible object of class ‘⁠mccut⁠’.

Understanding this, the call should be built like this: ‘⁠{{block 1}{block 2}{block 3}}⁠’

1. The first block (maybe empty) is an expression that will be evaluated only once. This block should evaluate all ‘⁠"V" mcnode⁠’ and ‘⁠"0" mcnode⁠’s. It may evaluate and ‘⁠"U" mcnode⁠’ that will be sent in the second and third block by column, and, optionaly, some other codes (even ‘⁠"VU" mcnode⁠’, sent by columns) that can not be evaluated if ‘⁠ndunc=1⁠’ (e.g. sampling without replacement in the uncertainty dimension).

2. The second block is an expression that leads to the ‘⁠mc⁠’ object. It must end with an expression as ‘⁠mymc <- mc(...)⁠’. The variable specified as ‘⁠ind⁠’ may be helpful to refer to the uncertainty dimension in this step

3. The last block should build a list of statistics refering to the ‘⁠mc⁠’ object. The function ‘⁠summary⁠’ should be used if a summary, a tornado on uncertainty (tornadounc.mccut) or a convergence diagnostic converg is needed, the function plot.mc should be used if a plot is needed, the function tornado should be used if a tornado is needed. Moreover, any other function that leads to a vector, a matrix, or a list of vector/matrix of statistics evaluated from the ‘⁠mc⁠’ object may be used. list are time consuming.

IMPORTANT WARNING: do not forget to affect the results, since the print method provide only a summary of the results while all data may be stored in an ‘⁠mccut⁠’ object.

Value

An object of class ‘⁠mccut⁠’. This is a list including statistics evaluated within the third block. Each list consists of all the ‘⁠nsu⁠’ values obtained. The ‘⁠print.mccut⁠’ method print the median, the mean, the ‘⁠lim⁠’ quantiles estimated on each statistics on the uncertainty dimension.

Note

The methods and functions available on the ‘⁠mccut⁠’ object is function of the statistics evaluated within the third block:

• a print.mccut is available as soon as one statistic is evaluated within the third block;

• a summary.mccut and a tornadounc.mccut are available if a summary.mc is evaluated within the third block;

• converg may be used if a summary.mc is evaluated within the third block;

• a plot.mccut is available if a plot.mc is evaluated within the third block. (Do not forget to use the argument ‘⁠draw = FALSE⁠’ in the third block);

• a tornado is available if a tornado is evaluated within the third block.

The seed is set at the beginning of the evaluation. Thus, the complete similarity of two evaluations is not certain, depending of the structure of your model. Moreover, with a similar seed, the simulation will not be equal to the one obtained with evalmcmod since the random samples will not be obtained in the same order.

In order to avoid conflicts between the ‘⁠model⁠’ evaluation and the function, the function uses upper case variables. Do not use upper case variables in your model.

The function should be re-adapted if a new function to be applied on ‘⁠mc⁠’ objects is written.

See Also

evalmcmod

Examples

modEC3 <- mcmodelcut({

## First block:
## Evaluates all the 0, V and U nodes.
 { cook <- mcstoc(rempiricalD, type = "V", values = c(0, 1/5, 
 1/50), prob = c(0.027, 0.373, 0.6))
 serving <- mcstoc(rgamma, type = "V", shape = 3.93, rate = 0.0806)
 conc <- mcstoc(rnorm, type = "U", mean = 10, sd = 2)
 r <- mcstoc(runif, type = "U", min = 5e-04, max = 0.0015)
 }
## Second block:
## Evaluates all the VU nodes
## Leads to the mc object. 
 {
 expo <- conc * cook * serving
 dose <- mcstoc(rpois, type = "VU", lambda = expo)
 risk <- 1 - (1 - r)^dose
 res <- mc(conc, cook, serving, expo, dose, r, risk)
 }
## Third block:
## Leads to a list of statistics: summary, plot, tornado
## or any function leading to a vector (et), a list (minmax), 
## a matrix or a data.frame (summary)
 {
 list(
 sum = summary(res), 
 plot = plot(res, draw=FALSE), 
 minmax = lapply(res, range)
 )
 }
})

x <- evalmccut(modEC3, nsv = 101, nsu = 101, seed = 666)
summary(x)

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