r_behavior_stream: Generates random behavior streams

In ARPobservation: Tools for Simulating Direct Behavioral Observation Recording Procedures Based on Alternating Renewal Processes

Generates random behavior streams

Description

Random generation of behavior streams (based on an alternating renewal process) of a specified length and with specified mean event durations, mean interim times, event distribution, and interim distribution.

Usage

r_behavior_stream(
  n,
  mu,
  lambda,
  F_event,
  F_interim,
  stream_length,
  equilibrium = TRUE,
  p0 = 0,
  tuning = 2
)

Arguments

n	number of behavior streams to generate
mu	vector of mean event durations
lambda	vector of mean interim time
F_event	distribution of event durations. Must be of class eq_dist.
F_interim	distribution of interim times. Must be of class eq_dist.
stream_length	length of behavior stream
equilibrium	logical; if TRUE, then equilibrium initial conditions are used; if FALSE, then p0 is used to determine initial state and normal generating distributions are used for event durations and interim times.
p0	vector of initial state probabilities. Only used if equilibrium = FALSE, in which case default is zero (i.e., behavior stream always starts with an interim time).
tuning	controls the size of the chunk of random event durations and interim times. Adjusting this may be useful in order to speed computation time .

Details

Generates behavior streams by repeatedly drawing random event durations and random interim times from the distributions as specified, until the sum of the durations and interim times exceeds the requested stream length. The vectors mu, lambda, and p0 are recycled to length n.

Value

An object of class behavior_stream containing two elements.

Examples

# default equilibrium initial conditions
r_behavior_stream(n = 5, mu = 3, lambda = 10, 
                  F_event = F_exp(), F_interim = F_exp(), 
                  stream_length = 100)
                  
# non-equilibrium initial conditions
r_behavior_stream(n = 5, mu = 3, lambda = 10,
                  F_event = F_gam(3), F_interim = F_gam(3),
                  stream_length = 100, 
                  equilibrium = FALSE, p0 = 0.5)

ARPobservation documentation built on Aug. 25, 2023, 5:19 p.m.