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
residual_process(
component,
inter_arrival,
type,
rambda_component,
mu,
beta,
dimens = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
...
)Arguments
- component
The component of type to get the residual process.
- inter_arrival
Inter-arrival times of events. This includes inter-arrival for events that occur in all dimensions. Start with zero.
- type
A vector of types distinguished by numbers, 1, 2, 3, and so on. 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.- mark
A vector of realized mark (jump) sizes. Start with zero.
- N
A matrix of counting processes.
- Nc
A matrix of counting processes weighted by mark.
- lambda_component0
The initial values of lambda component. Must have the same dimensional matrix with
hspec.- N0
The initial value of N
- ...
Further arguments passed to or from other methods.
Examples
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(component = 1, res$inter_arrival, res$type, res$rambda_component, mu, beta)
#> Warning: The 'residual_process' function is deprecated. Please use 'logLik' with 'infer = TRUE' instead.