Description Usage Arguments Details Value Examples

Simulate a set of observed intervals

1 2 |

`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 " |

`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 |

`n.ind` |
Number of intervals per group. Ignored without a numeric value for |

`sigma.within` |
The within-group standard-deviation. When a numeric value is given for |

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*√{sigma^2 - sigma.within^2}*a within-group standard deviation in interval length equal to

`sigma.within`

This function returns a dataframe containing the following:

`interval`

the simulated interval data

`group_id`

a group identifier

1 2 3 4 | ```
# 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)
``` |

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