RHawkes (version 1.0)

sim.pred1: Simulate a fitted RHawkes process model for prediction purposes

Description

Simulate a fitted RHawkes process model from the censoring time cens to a future time point cens.tilde, conditional on the observed event times until the censoring time.

Usage

sim.pred1(tms, par, re.dist = rweibull, 
    par.redist = list(shape = par[1], scale = par[2]), 
    of.dis="exp", par.ofdis = list(rate=par[3]), 
    branching.ratio = par[4], cens=tail(tms,1)+mean(diff(tms))/2, 
    cens.tilde = cens * 1.5, 
    mu.fn = function(x, p) {
        exp(dweibull(x, shape = p[1], scale = p[2], log = TRUE) - 
        pweibull(x, shape = p[1], scale = p[2], lower.tail = FALSE, log.p = TRUE))
    })

Arguments

tms

A numeric vector, with values sorted in ascending order. Event times to fit the RHawkes point process model.

par

A numeric vector, giving the parameters of the model with the immigration parameters \(\mu(.)\), offspring parameters \(h(.)\) and lastly the branching ratio \(\eta(.)\).

re.dist

A (vectorized) function. The function to simulate from the immigrant waiting times distribution.

par.redist

A (named) list, giving the parameters of the immigrant waiting time distribution.

of.dis

A character string, for the name of the offspring birth time distribution.

par.ofdis

A (named) list, giving the parameters of the offspring birth time distribution.

branching.ratio

A scalar in [0,1), the branching ratio parameter.

cens

A scalar. The censoring time.

cens.tilde

A scalar. The future time to run the simulation to.

mu.fn

A (vectorized) function. The immigration hazard function.

Value

A numeric vector that contains the simulated event times from censoring time cens up until cens.tilde

See Also

sim.pred.

Examples

# NOT RUN {
N <- 5; i <- 0;
data(quake); tms <- sort(quake$time);
# add some random noise the simultaneous occurring event times
tms[213:214] <- tms[213:214] + 
                    sort(c(runif(1, -1, 1)/(24*60), runif(1, -1, 1)/(24*60)))
# simulate future event time based on MLE fitted RHawkes model
times <- replicate(N,
                   {cat(i<<-i+1,'\n');
                   sim.pred1(tms = tms, par = c(0.314, 22.2, 1266, 0.512),
                             cens=35063)
                   })
plot(NA,NA,xlim=c(0,35063*1.5),ylim=c(0,max(lengths(times))+nrow(quake)),
     xlab="time",ylab="Sample path")
lines(c(0,quake$time),0:nrow(quake),type="s")
for(i in 1:N)
    lines(c(tail(quake$time,1),times[[i]]),nrow(quake)+0:length(times[[i]]),
          type="s",lty=2)
# }