View source: R/AmbitSimulation.R
sim_weighted_trawl_gen | R Documentation |
This function simulates a weighted trawl process for a generic trawl function and various choices the marginal distribution. The specific trawl function to be used can be supplied directly by the user.
sim_weighted_trawl_gen( n, Delta, trawlfct_gen, distr, distr_par, kernelfct = NULL )
n |
number of grid points to be simulated (excluding the starting value) |
Delta |
grid-width |
trawlfct_gen |
the trawl function a used in the simulation |
distr |
marginal distribution. Choose from "Gamma" (Gamma), "Gauss" (Gaussian), "Cauchy" (Cauchy), "NIG" (Normal Inverse Gaussian), Poi" (Poisson), "NegBin" (Negative Binomial) |
distr_par |
parameters of the marginal distribution: (Gamma: shape, scale; Gauss: mu, sigma (i.e. the second parameter is the standard deviation, not the variance); Cauchy: l, s; NIG: alpha, beta, delta, mu; Poi: v, NegBin: m, theta) |
kernelfct |
the kernel function p used in the ambit process |
This functions simulates a sample path from a weighted trawl process given by
Y_t =\int_{(-∞,t]\times (-∞, ∞)} p(t-s)I_{(0,a(t-s))}(x)L(dx,ds),
for t ≥ 0, and returns Y_0, Y_{Δ}, …, Y_{nΔ}. The user needs to ensure that trawlfct_gen is a monotonic function.
path Simulated path
slice_sizes slice sizes used
S_matrix Matrix of all slices
kernelweights kernel weights used
#Simulation of a Gaussian trawl process with exponential trawl function n<-2000 Delta<-0.1 trawlfct_par <-0.5 distr<-"Gauss" distr_par<-c(0,1) #mean 0, std 1 set.seed(233) a <- function(x){exp(-trawlfct_par*x)} path <- sim_weighted_trawl_gen(n, Delta, a, distr, distr_par)$path #Plot the path library(ggplot2) df <- data.frame(time = seq(0,n,1), value=path) p <- ggplot(df, aes(x=time, y=path))+ geom_line()+ xlab("l")+ ylab("Trawl process") p
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