get.counting.data: Extract arrival times from an object of class 'yuima.PPR'

In yuima: The YUIMA Project Package for SDEs

Extract arrival times from an object of class yuima.PPR

Description

This function extracts arrival times from an object of class yuima.PPR.

Usage

get.counting.data(yuimaPPR,type="zoo")

Arguments

yuimaPPR	An object of class yuima.PPR.
type	By default type="zoo" the function returns an object of class zoo. Other values are yuima.PPR and matrix.

Value

By default the function returns an object of class zoo. The arrival times can be extracted by applying the method index to the output

Examples

## Not run: 
##################
# Hawkes Process #
##################

# Values of parameters.
mu <- 2
alpha <- 4
beta <-5

# Law definition

my.rHawkes <- function(n){
  res <- t(t(rep(1,n)))
  return(res)
}

Law.Hawkes <- setLaw(rng = my.rHawkes)

# Point Process Definition

gFun <- "mu"
Kernel <- "alpha*exp(-beta*(t-s))"

modHawkes <- setModel(drift = c("0"), diffusion = matrix("0",1,1),
  jump.coeff = matrix(c("1"),1,1), measure = list(df = Law.Hawkes),
  measure.type = "code", solve.variable = c("N"),
  xinit=c("0"))

prvHawkes <- setPPR(yuima = modHawkes, counting.var="N", gFun=gFun,
  Kernel = as.matrix(Kernel), lambda.var = "lambda", 
  var.dx = "N", lower.var="0", upper.var = "t")

true.par <- list(mu=mu, alpha=alpha,  beta=beta)

set.seed(1)

Term<-70
n<-7000

# Simulation trajectory

time.Hawkes <-system.time(
  simHawkes <- simulate(object = prvHawkes, true.parameter = true.par,
     sampling = setSampling(Terminal =Term, n=n))
)

# Arrival times of the Counting process.

DataHawkes <- get.counting.data(simHawkes)
TimeArr <- index(DataHawkes)

##################################
# Point Process Regression Model #
##################################

# Values of parameters.
mu <- 2
alpha <- 4
beta <-5

# Law definition
my.rKern <- function(n,t){
  res0 <- t(t(rgamma(n, 0.1*t)))
  res1 <- t(t(rep(1,n)))
  res <- cbind(res0,res1)
  return(res)
}

Law.PPRKern <- setLaw(rng = my.rKern)

# Point Process definition
modKern <- setModel(drift = c("0.4*(0.1-X)","0"),
                    diffusion = c("0","0"),
                    jump.coeff = matrix(c("1","0","0","1"),2,2),
                    measure = list(df = Law.PPRKern),
                    measure.type = c("code","code"),
                    solve.variable = c("X","N"),
                    xinit=c("0.25","0"))

gFun <- "exp(mu*log(1+X))"
#
Kernel <- "alpha*exp(-beta*(t-s))"

prvKern <- setPPR(yuima = modKern,
                  counting.var="N", gFun=gFun,
                  Kernel = as.matrix(Kernel),
                  lambda.var = "lambda", var.dx = "N",
                  lower.var="0", upper.var = "t")

# Simulation

Term<-100
seed<-1
n<-10000

true.parKern <- list(mu=mu, alpha=alpha, beta=beta)


set.seed(seed)
# set.seed(1)

time.simKern <-system.time(
  simprvKern <- simulate(object = prvKern, true.parameter = true.parKern,
                         sampling = setSampling(Terminal =Term, n=n))
)


plot(simprvKern,main ="Counting Process with covariates" ,cex.main=0.9)

# Arrival Times
CountVar <- get.counting.data(simprvKern)
TimeArr <- index(CountVar)



## End(Not run)

yuima documentation built on Nov. 14, 2022, 3:02 p.m.