intervalsim {intRvals}

Simulate a set of observed intervals

Description

Simulate a set of observed intervals

Usage

intervalsim(
  n = 500,
  mu = 200,
  sigma = 40,
  p = 0.3,
  fun = "gamma",
  trunc = c(0, 600),
  fpp = 0,
  n.ind = NA,
  sigma.within = NA
)

Arguments

n

Number of simulated interval observations.

mu

Mean arrival interval.

sigma

Standard deviation of the arrival interval.

p

Probability to not observe an arrival.

fun

Assumed distribution for the intervals, one of "normal" or "gamma", corresponding to the Normal and GammaDist distributions

trunc

Observational range of intervals (intervals outside this range won't be observed)

fpp

Baseline proportion of intervals distributed as a random poisson process with mean arrival interval mu

n.ind

Number of intervals per group. Ignored without a numeric value for sigma.within.

sigma.within

The within-group standard-deviation. When a numeric value is given for sigma.within, sigma denotes the total (within+between subject) standard deviation

Details

Simulates the observations process of arrival intervals.

The default is to not differentiate between within- and between-group variance.

If both n.ind and sigma.within have numeric values, intervals are simulated with separate within-group variation (sigma.within) and between-group variation, for groups of size n.ind. Intervals belonging to the same group have:

• a within-group mean interval length that has been randomly drawn from a distribution with mean mu and between-group standard deviation \sqrt{sigma^2 - sigma.within^2}

• a within-group standard deviation in interval length equal to sigma.within

Value

This function returns a dataframe containing the following:

interval

the simulated interval data

group_id

a group identifier

Examples

# simulate observed intervals:
intervals=intervalsim(n=50,mu=200,sigma=40,trunc=c(0,600),fpp=0.1)
# check whether we retrieve the simulation parameters:
estinterval(goosedrop$interval)