Description Usage Arguments Examples
Using random time change, this function compute the residual process, which is the inter-arrival time of a standard Poisson process. Therefore, the return values should follow the exponential distribution with rate 1, if model and rambda are correctly specified.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
component |
the component of type to get the residual process |
type |
a vector of types. Distinguished by numbers, 1, 2, 3, and so on. Start with zero. |
inter_arrival |
inter-arrival times of events. Includes inter-arrival for events that occur in all dimensions. Start with zero. |
rambda_component |
right continuous version of lambda process |
mu |
numeric value or matrix or function, if numeric, automatically converted to matrix |
beta |
numeric value or matrix or function, if numeric, automatically converted to matrix, exponential decay |
dimens |
dimension of the model. if omitted, set to be the length of |
mark |
a vector of realized mark (jump) sizes. Start with zero. |
N |
a matrix of counting processes |
Nc |
a matrix of cumulated counting processes |
lambda0 |
the initial values of lambda component. Must have the same dimensional matrix with |
N0 |
the initial value of N |
1 2 3 4 5 6 7 8 9 10 | mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=1000)
rp <- residual_process(1, res$type, res$inter_arrival, res$rambda_component, mu, beta)
p <- ppoints(100)
q <- quantile(rp,p=p)
plot(qexp(p), q, xlab="Theoretical Quantiles",ylab="Sample Quantiles")
qqline(q, distribution=qexp,col="blue", lty=2)
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