residual_process: Compute residual process

Description Usage Arguments Examples

View source: R/hgfit.R

Description

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.

Usage

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residual_process(component, type, inter_arrival, rambda_component, mu, beta,
  dimens = NULL)

Arguments

component

the component of type to get the residual process

type

a vector of dimensions. 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 mu.

Examples

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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)

ksublee/emhawkes documentation built on April 18, 2019, 9:46 a.m.