coxseisim: A function to simulate a CoxSEI process conditional on...
In coxsei: Fitting a CoxSEI Model

Description Usage Arguments Value Author(s) Examples

simulate the sample path of the CoxSEI model with given covariate process values, and excitation function and order of autodependence in the excitation term.

coxseisim(parreg, parg, lmd0 = function(tt) (1 + 0.5 * cos(2 * pi *
tt)),
          g = function(x, parg) {
                 ifelse(x <= 0, 0, parg[1] * parg[2] * exp(-parg[2] * x))
              },
          censor = 1, m = 2, trace=TRUE,
          Z = function(x) matrix(0, length(x), length(parreg))
         )

`parreg`	the regression parameter
`parg`	parameters of the excitation function
`lmd0`	the baseline intensity function
`g`	the excitation function
`censor`	the censoring time
`m`	order of autoregression in the excitation component of the intensity process
`trace`	whether to trace the data generation process; defaults to `TRUE`
`Z`	a function to calculate the covariate values at a specified event time

A data frame with provided covariate values and the censoring time, and the generated event times.

Feng Chen <feng.chen@unsw.edu.au>

    n.smp <- 100;
    z <- matrix(NA,n.smp,3)
    for(i in 1:n.smp)
    z[i,] <- round(c(runif(1,0.5,1.5),runif(1,1.5,2.5),rbinom(1,1,0.5)),2)
    dat <- coxseisim(1:3*0.2,c(0.07,10),censor=rlnorm(1,0,0.1),m=2,
    Z=function(x)matrix(z[1,],length(x),3,byrow=TRUE))
    dat$id <- 1;
    for(i in 2:n.smp){
      dattmp <- coxseisim(1:3*0.2,c(0.07,10),censor=rlnorm(1,0,0.1),m=2,
      Z=function(x)matrix(z[i,],length(x),3,byrow=TRUE))
      dattmp$id <- i;
      dat <- rbind(dat,dattmp)
    }