View source: R/DroppingInterval.R
intervalsim | R Documentation |
Simulate a set of observed intervals
intervalsim( n = 500, mu = 200, sigma = 40, p = 0.3, fun = "gamma", trunc = c(0, 600), fpp = 0, n.ind = NA, sigma.within = NA )
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
# 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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