mc2d (version 0.1-17)

mcnode: Build mcnode Objects from Data or other mcnode Objects

Description

Creates a mcnode object from a vector, an array or a mcnode.

Usage

mcdata(data, type=c("V", "U", "VU", "0"), nsv=ndvar(), nsu=ndunc(),
	  nvariates=1, outm="each")
mcdatanocontrol(data, type=c("V", "U", "VU", "0"), nsv=ndvar(), nsu=ndunc(),
	  nvariates=1, outm="each")

Arguments

data

The numeric/logical vector/matrix/array of data or the mcnode object.

type

The type of node to be built. By default, a "V" node.

nsv

The variability dimension (type="V" or type="VU") of the node. By default: the current value in mc.control

nsu

The uncertainty dimension (type="U" or type="VU") of the node. By default: the current value in mc.control

nvariates

The number of variates. By default: 1

outm

The output of the mcnode for multivariates nodes. May be "each" (default) if output should be provided for each variates considered independently, "none" for no output or a vector of name of function(s) (as a character string) that will be applied on the variates dimension before any output (ex: "mean", "median", c("min", "max")). The function should have no other arguments and send one value per vector of values (ex. do not use "range"). Note that the outm attribute may be changed at any time using the outm function.

Value

An mcnode object.

Details

A mcnode object is the basic element of a mc object. It is an array of dimension (nsv x nsu x nvariates). Four types of mcnode exists:

• "V" mcnode, for "Variability", are arrays of dimension (nsv x 1 x nvariates). The alea in the data should reflect variability of the parameter.
• "U" mcnode, for "Uncertainty", are arrays of dimension c(1 x nsu x nvariates). The alea in the data should reflect uncertainty of the parameter.
• "VU" mcnode, for "Variability and Uncertainty", are arrays of dimension (nsv x nsu x nvariates). The alea in the data reflects separated variability (in rows) and uncertainty (in columns) of the parameter.
• "0" mcnode, for "Neither Variability or Uncertainty", are arrays of dimension (1 x 1 x nvariates). No alea is considered for these nodes. "0" mcnode are not necessary in the univariate context (use scalar instead) but may be useful for operations on multivariate nodes.

Multivariate nodes (i.e. nvariates != 1) should be used for multivariate distributions implemented in mc2d (rmultinomial, rmultinormal, rempiricalD and rdirichlet).

For security, recycling rules are limited to fill the array using data. The general rules is that recycling is only permitted to fill a dimension from 1 to the final size of the dimension.

If the final dimension of the node is (nsv x nsu x nvariates) (with nsv = 1 and nsu = 1 for "0" nodes, nsu = 1 for "V" nodes and nsv = 1 for "U" nodes), mcdata accepts :

• Vectors of length 1 (recycled on all dimensions), vectors of length (nsv * nsu) (filling first the dimension of variability, then the dimension of uncertainty then recycling on nvariates), or vectors of length (nsv * nsu * nvariates) (filling first the dimension of variability, then the uncertainty, then the variates).
• Matrixes of dimensions (nsv x nsu), recycling on variates.
• Arrays of dimensions (nsv x nsu x nvariates) or (nsv x nsu x 1), recycling on variates.
• For data as mcnode, recycling is dealt to proper fill the array:
  1. a "V" node accepts a "0" node of dimension (1 x 1 x nvariates) (recycling on variability) or of dimension (1 x 1 x 1) (recycling on variability and variates), or a "V" node of dimension (nsv x 1 x nvariates) or (nsv x 1 x 1) (recycling on variates),
  2. a "U" node accepts a "0" node of dimension (1 x 1 x nvariates) (recycling on uncertainty) or of dimension (1 x 1 x 1) (recycling on uncertainty and variates), or a "U" node of dimension (1 x nsu x nvariates), or (1 x nsu x 1) (recycling on variates),
  3. a "VU" node accepts a "0" node of dimension (1 x 1 x nvariates) (recycling on varaiability and uncertainty) or of dimension (1 x 1 x 1) (recycling on variability, uncertainty and variates), a "U" node of dimension (1 x nsu x nvariates)(recycling "by row" on the variability dimension), or of dimension (1 x nsu x 1)(recycled "by row" on the variability dimension then on variates), a "V" node of dimension (nsv x 1 x nvariates)(recycling on the uncertainty dimension) or of dimension (nsv x 1 x 1)(recycled on the uncertainty dimension then on variates), and a "VU" node of dimension (nsv x nsu x nvariates) or of dimension (nsv x nsu x 1) (recycling on variates).
  4. a "0" node accepts a "0" node of dimension (1 x 1 x nvariates) or (1 x 1 x 1) (recycling on variates).

mcdatanocontrol is a dangerous version of mcnode which forces the dimension of data to be (nsv x nsu x nvariates) and gives the atributes and the class without any control. This function is useful when your model is tested since it is much more quicker.

See Also

mcstoc to build a stochastic mcnode object, mcprobtree to build a stochastic node fro a probability tree.

Ops.mcnode for operations on mcnode objects.

mc to build a Monte-Carlo object.

Informations about an mcnode: is.mcnode, dimmcnode, typemcnode.

To build a correlation structure between mcnode: cornode.

To study mcnode objects: print.mcnode, summary.mcnode, plot.mcnode, converg, hist.mcnode

To modify mcnode objects: NA.mcnode

Examples

oldvar <- ndvar()
oldunc <- ndunc()
ndvar(3)
ndunc(5)

(x0 <- mcdata(100, type="0"))
mcdata(matrix(100), type="0")

(xV <- mcdata(1:ndvar(), type="V"))
mcdata(matrix(1:ndvar(), ncol=1), type="V")

(xU <- mcdata(10*1:ndunc(), type="U"))
mcdata(matrix(10*1:ndunc(), nrow=1), type="U")

(xVU <- mcdata(1:(ndvar()*ndunc()), type="VU"))
mcdata(matrix(1:(ndvar()*ndunc()), ncol=5, nrow=3), type="VU")

##Do not use
## Not run: 
# mcdata(matrix(1:5, nrow=1), type="VU")
# ## End(Not run)
##use instead
mcdata(mcdata(matrix(1:ndunc(), nrow=1), type="U"), "VU")
##or
mcdata(matrix(1:ndunc(), nrow=1), type="U") + mcdata(0, "VU")

mcdata(x0, type="0")

mcdata(x0, type="V")
mcdata(xV, type="V")

mcdata(x0, type="U")
mcdata(xU, type="U")

mcdata(x0, type="VU")
mcdata(xU, type="VU")
mcdata(xV, type="VU")

##Multivariates
(x0M <- mcdata(1:2, type="0", nvariates=2))
mcdata(1, type="0", nvariates=2)

(xVM <- mcdata(1:(2*ndvar()), type="V", nvariates=2))
mcdata(1:ndvar(), type="V", nvariates=2)
mcdata(array(1:(2*ndvar()), dim=c(3, 1, 2)), type="V", nvariates=2)

mcdata(1, type="V", nvariates=2)
mcdata(x0, type="V", nvariates=2)
mcdata(x0M, type="V", nvariates=2)
mcdata(xV, type="V", nvariates=2)
mcdata(xVM, type="V", nvariates=2)

(xUM <- mcdata(10*(1:(2*ndunc())), type="U", nvariates=2))
mcdata(array(10*(1:(2*ndunc())), dim=c(1, 5, 2)), type="U", nvariates=2)

mcdata(1, type="U", nvariates=2)
mcdata(x0, type="U", nvariates=2)
mcdata(x0M, type="U", nvariates=2)
mcdata(xU, type="U", nvariates=2)
mcdata(xUM, type="U", nvariates=2)

(xVUM <- mcdata(1:(ndvar()*ndunc()), type="VU", nvariates=2))
mcdata(array(1:(ndvar()*ndunc()), dim=c(3, 5, 2)), type="VU", nvariates=2)

mcdata(1, type="VU", nvariates=2)
mcdata(x0, type="VU", nvariates=2)
mcdata(x0M, type="VU", nvariates=2)
mcdata(xV, type="VU", nvariates=2)
mcdata(xVM, type="VU", nvariates=2)
mcdata(xU, type="VU", nvariates=2)
mcdata(xUM, type="VU", nvariates=2)
mcdata(xVU, type="VU", nvariates=2)
mcdata(xVUM, type="VU", nvariates=2)

ndvar(oldvar)
ndunc(oldunc)