new_hawkes: Create a new hawkes model with given arguments

In behavioral-ds/evently: Fit Hawkes and HawkesN Processes with AMPL and Ipopt

Create a new hawkes model with given arguments

Description

Create a new hawkes model with given arguments

Usage

new_hawkes(
  model_type,
  par = NULL,
  data = NULL,
  init_par = NULL,
  observation_time = NULL,
  lower_bound = NULL,
  upper_bound = NULL,
  model_vars = NULL,
  limit_event = NULL
)

Arguments

model_type	A string indicates the model tyep, e.g. EXP for a Hawkes process with an exponential kernel
par	A named vector denotes the model parameters where the names are model parameters and the values are the corresponding parameter values
data	A list of data.frame(s) where each data.frame is an event cascade with event tims and event magnitudes (optional)
init_par	Initial parameter values used in fitting
observation_time	The event cascades observation time. It is assumed that all cascades in data are observed until a common time.
lower_bound	Model parameter lower bounds. A named vector where names are model parameters and values are the lowest possible values.
upper_bound	Model parameter upper bounds. A named vector where names are model parameters and values are the largest possible values.
model_vars	A named list of extra variables provided to hawkes objects
limit_event	choose how to optimize the computation by reducing the number of events added in log-likelihood functions.

Value

A model object with class [hawkes] and [hawkes_'model_type'] where 'model_type' is replaced by the given model_type

Examples

data <- list(data.frame(time = c(0, 0.5, 1)))
new_hawkes(model_type = 'EXP', par = c(K = 0.9, theta = 1),
           data = data, observation_time = Inf)

behavioral-ds/evently documentation built on Feb. 3, 2023, 9:42 a.m.