DataPpr function

From zoo data to yuima.PPR.

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

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.

Returns

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)