DataPpr: From 'zoo' data to 'yuima.PPR'.

In yuima: The YUIMA Project Package for SDEs

From zoo data to yuima.PPR.

Description

The function converts an object of class zoo to an object of class yuima.PPR.

Usage

DataPPR(CountVar, yuimaPPR, samp)

Arguments

CountVar	An object of class zoo that contains counting variables and covariates. index(CountVar) returns the arrival times.
yuimaPPR	An object of class yuima.PPR that contains a mathematical description of the point process regression model assumed to be the generator of the observed data.
samp	An object of class yuima.sampling.

Value

The function returns an object of class yuima.PPR where the slot model contains the Point process described in yuimaPPR@model, the slot data contains the counting variables and the covariates observed on the grid in samp.

Examples

## Not run: 
# In this example we generate a dataset contains the Counting Variable N
# and the Covariate X.
# The covariate X is an OU driven by a Gamma process.

# 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<-200
seed<-1
n<-20000

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)

# Using the function get.counting.data we extract from an object of class
# yuima.PPR the counting process N and the covariate X at the arrival times.

CountVar <- get.counting.data(simprvKern)

plot(CountVar)

# We convert the zoo object in the yuima.PPR object.

sim2 <- DataPPR(CountVar, yuimaPPR=simprvKern, samp=simprvKern@sampling)


## End(Not run)

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