ARPobservation (version 1.2.2)

r_WIR: Generates random whole interval recording 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, which are then coded as whole interval recording data with given interval length and rest length.

Usage

r_WIR(
  n,
  mu,
  lambda,
  stream_length,
  F_event,
  F_interim,
  interval_length,
  rest_length = 0,
  summarize = FALSE,
  equilibrium = TRUE,
  p0 = 0,
  tuning = 2
)

Value

If summarize = FALSE, a matrix with rows equal to n and a number of columns equal to the number intervals per session. If summarize = TRUE a vector of means of length n.

Arguments

n

number of behavior streams to generate

mu

mean event duration

lambda

mean interim time

stream_length

length of behavior stream

F_event

distribution of event durations. Must be of class eq_dist.

F_interim

distribution of interim times. Must be of class eq_dist.

interval_length

total interval length

rest_length

length of any recording time in each interval

summarize

logical value indicating whether the behavior streams should by summarized by taking their mean

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

Initial state probability. 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 .

Author

Daniel Swan <[e-mail]>

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. Then applies a whole interval recording filter to the generated behavior streams.

Examples

# An unsummarized set of WIR observations
r_WIR(n = 5, mu = 2, lambda = 4, stream_length = 20, 
       F_event = F_exp(), F_interim = F_exp(), 
       interval_length = 1, rest_length = 0)
      
# A summarized set of of WIR observations
r_WIR(n = 5, mu = 2, lambda = 4, stream_length = 20, 
       F_event = F_exp(), F_interim = F_exp(), 
       interval_length = 1, rest_length = 0,
       summarize = TRUE)