mcstoc: Creates Stochastic mcnode Objects
In mc2d: Tools for Two-Dimensional Monte-Carlo Simulations

Description Usage Arguments Details Value See Also Examples

Creates a mcnode object using a random generating function.

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mcstoc(func=runif, type=c("V", "U", "VU", "0"), ..., nsv=ndvar(),
	  nsu=ndunc(), nvariates=1, outm="each", nsample="n",
	  seed=NULL, rtrunc=FALSE, linf=-Inf, lsup=Inf, lhs=FALSE)

`func`	A function providing random data or its name as character.
`type`	The type of mcnode to be built. By default, a "V" node. see `mcnode` for details.
`...`	All other arguments but the size of the sample to be passed to func. These arguments should be vectors or mcnodes (arrays prohibited).
`nsv`	The number of simulations in the variability dimension.
`nsu`	The number of simulations in the uncertainty dimension.
`nvariates`	The number of variates of the output.
`outm`	The output of the mcnode for multivariates nodes. May be "each" (default) if an output should be provided for each variates considered independently, "none" for no output or a vector of functions (as a character string) that will be applied on the variates dimension before any output (ex: "mean", "median", c("min","max")). Each function should return 1 value when applied to 1 value (ex. do not use "range"). Note that the outm attribute may be changed further using the `outm` function.
`nsample`	The name of the parameter of the function giving the size of the vector. By default, n, as in most of the random sampling distributions of the stats library (with the exceptions of rhyper and rwilcox where nsample="nn" should be used).
`seed`	The random seed used for the evaluation. If NULL the seed is unchanged.
`rtrunc`	Should the distribution be truncated? See `rtrunc`.
`linf`	If truncated: lower limit. May be a scalar, an array or a mcnode.
`lsup`	If truncated: upper limit. May be a scalar, an array or a mcnode. lsup should be pairwise strictly greater then linf
`lhs`	Should a Random Latin Hypercube Sampling be used? see `lhs`

Note that arguments after ... must match exactly.

Any function who accepts vectors/matrix as arguments may be used (notably: all current random generator of the stats package). The arguments may be sent classically but it is STRONGLY recommended to use consistant mcnodes if arguments should be recycled, since a very complex recycling is handled for mcnode and not for vectors. The rules for compliance of mcnode arguments are as following (see below for special functions):

type="V": accepts "0" mcnode of dimension (1 x 1 x nvariates) or of dimension (1 x 1 x 1) (recycled) and "V" mcnode of dimension (nsv x 1 x nvariates) or (nsv x 1 x 1) (recycled).
type="U": accepts "0" mcnode of dimension (1 x 1 x nvariates) or of dimension (1 x 1 x 1) (recycled) and "U" mcnode of dimension (1 x nsu x nvariates) or of dimension (1 x nsu x 1) (recycled).
type="VU": accepts "0" mcnode of dimension (1 x 1 x nvariates) or of dimension (1 x 1 x 1) (recycled), "V" mcnode of dimension (nsv x 1 x nvariates) (recycled classicaly) or (nsv x 1 x 1) (recycled classically), "U" mcnode of dimension (1 x nsu x nvariates) (recycled by rows) or (1 x nsu x 1) (recycled by row on the uncertainty dimension and classicaly on variates), "VU" mcnode of dimension (nsv x nsu x nvariates) or of dimension (nsv x nsu x 1) (recycled).
type="0": accepts "0" mcnode of dimension (1 x 1 x nvariates) or (1 x 1 x 1) (recycled).

Multivariate nodes and multivariate distributions:

The number of variates should be provided (not guesses by the function). A multivariates node may be built using a univariate distribution and nvariates!=1. See examples.

rdirichlet needs for alpha a vector or a multivariates nodes and returns a multivariate node. rmultinomial needs for size and prob vectors and/or multivariate nodes and return a univariate or a multivariate node. rmultinormal needs for mean and sigma vectors and/or multivariate nodes and return a multivariate node. rempiricalD needs for values and prob vectors and/or multivariate nodes and return a a univariate or a multivariate node. See examples.

trunc=TRUE is valid for univariates distributions only. The distribution will be truncated on (linf, lsup]. The function 'func' should have a 'q' form (with first argument 'p') and a 'p' form, as all current random generator of the stats library. Example : 'rnorm' (has a 'qnorm' and a 'pnorm' form), 'rbeta', 'rbinom', 'rgamma', ...

If lhs=TRUE, a Random Hypercube Sampling will be used on nsv and nsu The function 'func' should have a 'q' form (with argument 'p'). lhs=TRUE is thus not allowed on multivariates distributions.

An mcnode object.

mcnode for a description of mcnode object, methods and functions on mcnode objects.

Ops.mcnode for operations on mcnode objects. rtrunc for important warnings on the use of the trunc option.

Oldnvar <- ndvar()
Oldnunc <- ndunc()
ndvar(5)
ndunc(4)

## compatibility with mcdata as arguments
x0 <- mcstoc(runif, type="0")
xV <- mcstoc(runif, type="V")
xU <- mcstoc(runif, type="U")
xVU <- mcstoc(runif, type="VU")

## "0" accepts mcdata "0"
mcstoc(runif, type="0", min=-10, max=x0)

## "V" accepts "0" mcdata and "V" mcdata
mcstoc(rnorm, type="V", mean=x0, sd=xV)

## "U" accepts "0" mcdata and "U" mcdata
mcstoc(rnorm, type="U", mean=x0, sd=xU)

## "VU" accepts "0" mcdata, "U" mcdata
## "V" mcdata and "U" mcdata with correct recycling
mcstoc(rnorm, type="VU", mean=x0, sd=xVU)
mcstoc(rnorm, type="VU", mean=xV, sd=xU)

## any function giving a set (vector/matrix) of value of length 'size' works
f <- function(popi) 1:popi
mcstoc(f, type="V", nsample="popi")

##Multivariates

ndvar(2)
ndunc(5)
##Build a multivariate node with univariate distribution
mcstoc(rnorm, "0", nvariates=3)
mcstoc(rnorm, "V", nvariates=3)
mcstoc(rnorm, "U", nvariates=3)
mcstoc(rnorm, "VU", nvariates=3)

##Build a multivariate node with multivariates distribution
alpha <- mcdata(c(1, 1000, 10, 100, 100, 10, 1000, 1), "V", nvariates=4)
(p <- mcstoc(rdirichlet, "V", alpha=alpha, nvariates=4))
mcstoc(rmultinomial, "VU", size=10, p, nvariates=4)

##Build a univariates node with "multivariates" distribution
size <- mcdata(c(1:5), "U")
mcstoc(rmultinomial, "VU", size, p, nvariates=1) #since a multinomial return one value

##Build a multivariates node with "multivariates" distribution
mcstoc(rmultinomial, "VU", size, p, nvariates=4) #sent 4 times to fill the array

##Use of rempiricalD with nodes
##A bootstrap
ndunc(5)
ndvar(5)
dataset <- c(1:9)
(b <- mcstoc(rempiricalD, "U", nvariates=9, values=dataset))
unclass(b)
##Then we build a VU node by sampling in each set of bootstrap
(node <- mcstoc(rempiricalD, "VU", values=b))
unclass(node)

## truncated
ndvar(2)
ndunc(5)
linf <- mcdata(-1:3, "U")
x <- mcstoc(rnorm, "VU", rtrunc=TRUE, linf=linf)
unclass(round(x))
linf <- mcdata(1:5, "U")
mcstoc(rnorm, "VU", nsv=100, rtrunc=TRUE, linf=linf, lhs=TRUE)

ndvar(Oldnvar)
ndunc(Oldnunc)