mcnode: Build mcnode Objects from Data or other mcnode Objects
In mc2d: Tools for Two-Dimensional Monte-Carlo Simulations
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
Creates a mcnode object from a vector, an array or a
mcnode.
Usage
1
2
3
4
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.
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:
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),
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),
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).
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.
Value
An mcnode object.
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
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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)
mc2d documentation built on July 5, 2021, 3:01 p.m.
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Description Usage Arguments Details Value See Also Examples
Description
Creates a mcnode object from a vector, an array or a mcnode.
Usage
1 2 3 4 |
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
|
nsu |
The uncertainty dimension (type="U" or
type="VU") of the node. By default: the current value in
|
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 |
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:
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),
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),
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).
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.
Value
An mcnode object.
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
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 74 75 76 77 | 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)
|
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