Description Usage Arguments Methods Author(s) References Examples

Given an object of class `setup`

,
the method can be invoked for setting-up the Monte Carlo simulation.
The variables are sampled accordingly to their parameters specified in the slot `rng`

of the
`setup`

object. If `ar.model`

is defined in slot `ar.model`

, then the specified
variables are sampled from the `pdf`

`nor`

as an autorregresive (AR) model via the
function `arima.sim`

from base package `stats`

. If `var.model`

is defined in
slot `var.model`

, then the specified variables are sampled from the `pdf`

`nor`

as
an vector autorregresive (VAR) model via the function `mAr.sim`

from package `mAr`

(see Barbosa, 2015, and Luetkepohl, 2005, for details). See `setup-class`

for further details
to define the AR and VAR models.

1 2 | ```
MC.setup(x)
``` |

`x` |
an object of class |

`signature(x = "setup")`

J.A Torres-Matallana

S. M. Barbosa, Package "mAr": Multivariate AutoRegressive analysis, 1.1-2, The Comprehensive R Archive Network, CRAN, 2015.

H. Luetkepohl, New Introduction to Multiple Time Series Analysis, Springer, 2005.

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 | ```
# loading a precipitation time series as input for the setup class
library(EmiStatR)
data(P1)
# A setup with three variables to be considered in the Monte Carlo simulation:
# var1, a constant value variable; var2, a variable sampled from a uniform (uni)
# probability distribution function (pdf) with parameters min and max;
# var3, a variable sampled from a normal (nor) pdf with parameteres mu and sigma
ini <- setup(id = "MC_sim1", nsim = 500, seed = 123, mcCores = 1, ts.input = P1,
rng = list(var1 = 150, var2 = c(pdf = "uni", min = 50, max = 110),
var3 = c(pdf = "nor", mu = 90, sigma = 2.25))
)
MC_setup <- MC.setup(ini)
str(MC_setup)
## definition of AR models for variables var2 and var3 with AR coefficients 0.995 and 0.460
library(EmiStatR)
data(P1)
ini_ar <- setup(id = "MC_sim1_ar", nsim = 500, seed = 123, mcCores = 1, ts.input = P1,
rng = list(var1 = 150, var2 = c(pdf = "nor", mu = 150, sigma = 5),
var3 = c(pdf = "nor", mu = 90, sigma = 2.25)),
ar.model = ar.model <- list(var2 = 0.995, var3 = 0.460)
)
MC_setup_ar <- MC.setup(ini_ar)
str(MC_setup_ar)
## definition of a bi-variate VAR model for variables var2 and var3
ini_var <- setup(id = "MC_sim1_ar", nsim = 500, seed = 123, mcCores = 1, ts.input = P1,
rng = rng <- list(var1 = 150,
var2 = c(pdf = "nor", mu = 150, sigma = 5),
var3 = c(pdf = "nor", mu = 90, sigma = 2.25)),
var.model = var.model <- list( inp = c("var2", "var3"),
w = c(0.048, 0.021),
A = matrix(c(0.992, -8.8e-05, -31e-4, 0.995),
nrow=2, ncol=2),
C = matrix(c(0.0091, 0.0022, 0.0022, 0.0019),
nrow=2, ncol=2))
)
MC_setup_var <- MC.setup(ini_var)
str(MC_setup_var)
``` |

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