Description Usage Arguments Details Value See Also Examples
Creates a mcnode
object using a random generating
function.
1 2 3 
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 
... 
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

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

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

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):
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).
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).
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).
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73  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)

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