H2ExMOParam-class | R Documentation |
CalibrationParam for the H2-exchangeable Marshall-Olkin (average) default counting process model. Extends H2ExMarkovParam and related to ExMOParam.
## S4 method for signature 'H2ExMOParam' simulate_dt(object, ..., n_sim = 10000L) ## S4 method for signature 'H2ExMOParam' simulate_adcp(object, times, ..., n_sim = 10000L)
object |
A CalibrationParam-object. |
... |
Pass-through parameters. |
n_sim |
Number of samples. |
times |
A non-negative numeric vector of timepoints. |
simulate_dt(H2ExMOParam)
: simulates the vector of default times and returns a matrix x
with
dim(x) == c(n_sim, getDimension(object))
.
simulate_adcp(H2ExMOParam)
: simulates the average default counting process and returns a
matrix x
with dim(x) == c(n_sim, length(times))
.
The default times are sampled using the stochastic representation described in details.
composition <- c(2L, 4L, 2L) d <- sum(composition) model_global <- ExMOParam(rmo::exIntensities(rmo::AlphaStableBernsteinFunction(0.4), d)) model_partition <- purrr::map(composition, ~{ ExMOParam(rmo::exIntensities(rmo::AlphaStableBernsteinFunction(0.5), .x)) }) models <- c(list(model_global), model_partition) parm <- H2ExMOParam(fraction = 0.4, models = models) simulate_dt(parm, n_sim = 1e1L) composition <- c(2L, 4L, 2L) d <- sum(composition) model_global <- ExMOParam(rmo::exIntensities(rmo::AlphaStableBernsteinFunction(0.4), d)) model_partition <- purrr::map(composition, ~{ ExMOParam(rmo::exIntensities(rmo::AlphaStableBernsteinFunction(0.5), .x)) }) models <- c(list(model_global), model_partition) parm <- H2ExMOParam(fraction = 0.4, models = models) simulate_adcp(parm, 1, n_sim = 1e1L) simulate_adcp(parm, seq(25e-2, 5, by = 25e-2), n_sim = 1e1L)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.