mcstoc: Creates Stochastic mcnode Objects
In mc2d: Tools for Two-Dimensional Monte-Carlo Simulations
mcstoc R Documentation
Creates Stochastic mcnode Objects
Description
Creates a mcnode object using a random generating
function.
Usage
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)
Arguments
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
‘mcnode’s (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
Details
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 consistent ‘mcnode’s 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
classically) 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 classically 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.
Value
An ‘mcnode’ object.
See Also
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.
Examples
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)
mc2d documentation built on July 26, 2023, 6:07 p.m.
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| mcstoc | R Documentation |
Creates Stochastic mcnode Objects
Description
Creates a mcnode object using a random generating
function.
Usage
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)
Arguments
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 ‘mcnode’s (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
|
Details
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 consistent ‘mcnode’s 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 classically) 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 classically 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.
Value
An ‘mcnode’ object.
See Also
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.
Examples
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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