MC.setup-methods | R Documentation |
MC.setup
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.
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.
# 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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