sim_BivariateTrawl: Simulates a bivariate trawl process

Description Usage Arguments Details Examples

View source: R/SimulateTrawl.R

Description

Simulates a bivariate trawl process

Usage

1
2
3
4
5
6
7
8
9
sim_BivariateTrawl(t, Delta = 1, burnin = 10,
  marginal = base::c("Poi", "NegBin"),
  dependencetype = base::c("fullydep", "dep"), trawl1 = base::c("Exp",
  "DExp", "supIG", "LM"), trawl2 = base::c("Exp", "DExp", "supIG", "LM"),
  v1 = 0, v2 = 0, v12 = 0, kappa1 = 0, kappa2 = 0, kappa12 = 0,
  a1 = 0, a2 = 0, lambda11 = 0, lambda12 = 0, w1 = 0,
  delta1 = 0, gamma1 = 0, alpha1 = 0, H1 = 0, lambda21 = 0,
  lambda22 = 0, w2 = 0, delta2 = 0, gamma2 = 0, alpha2 = 0,
  H2 = 0)

Arguments

t

parameter which specifying the length of the time interval [0,t] for which a simulation of the trawl process is required.

Delta

parameter Δ specifying the length of the time step, the default is 1

burnin

parameter specifying the length of the burn-in period at the beginning of the simulation

marginal

parameter specifying the marginal distribution of the trawl

dependencetype

Parameter specifying the type of dependence

trawl1

parameter specifying the type of the first trawl function

trawl2

parameter specifying the type of the second trawl function

v1, v2, v12

parameters of the Poisson distribution

kappa1, kappa2, kappa12, a1, a2

parameters of the (possibly bivariate) negative binomial distribution

lambda11, lambda12, w1

parameters of the exponential (or double-exponential) trawl function of the first process

delta1, gamma1

parameters of the supIG trawl function of the first process

alpha1, H1

parameter of the long memory trawl of the first process

lambda21, lambda22, w2

parameters of the exponential (or double-exponential) trawl function of the second process

delta2, gamma2

parameters of the supIG trawl function of the second process

alpha2, H2

parameter of the long memory trawl of the second process

Details

This function simulates a bivariate trawl process with either Poisson or negative binomial marginal law. For the trawl function there are currently four choices: exponential, double-exponential, supIG or long memory. More details on the precise simulation algorithm is available in the vignette.

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
#Simulate a bivariate negative binomial trawl process with exponential trawl
#functions
#Parameters of the exponential trawls:
lambda1 <- 1.2
lambda2 <- 1.5
#Parameters of the negative binomial marginal law:
m1 <- 2.1
theta1 <- 0.9
a1 <- 27.3
m2 <- 2.3
theta2 <- 0.9
a2 <- 35.3
kappa12 <- m1
kappa1 <- 0
kappa2 <- m2 - kappa12
#Specify the time period and grid
t <- 720
Delta <- 1
#Fix the seed
set.seed(1)
#Simulate the bivariate trawl process with common factor
#and independent components ("dep") and negative binomial
# marginal law. Both trawl functions are chosen as exponentials.
simdata <- sim_BivariateTrawl(t, Delta, burnin=10,marginal ="NegBin",
dependencetype="dep", trawl1 ="Exp", trawl2 ="Exp",
kappa1=kappa1,kappa2=kappa2,kappa12=kappa12,a1=a1,a2=a2,lambda11=lambda1,
lambda21 =lambda2)

trawl documentation built on Aug. 16, 2018, 5:04 p.m.