stUPscales (version 1.0.3.5)

MC.setup-methods: Methods for Function MC.setup

Description

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.

Usage

MC.setup(x)

Methods

signature(x = "setup")

Arguments

x

an object of class setup.

Author

J.A Torres-Matallana

References

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.

Examples

# 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)