
Declusters clustered point process data so that Poisson assumption is more tenable over a high threshold.
decluster(series, run = NA, picture = TRUE)
a numeric vector of threshold exceedances
with a times
attribute which should be a numeric
vector containing either the indices or the times/dates
of each exceedance (if times/dates, the attribute should
be an object of class "POSIXct"
or an object that
can be converted to that class; see
as.POSIXct
)
parameter to be used in the runs method; any two consecutive threshold exceedances separated by more than this number of observations/days are considered to belong to different clusters
whether or not a picture of declustering should be drawn
The declustered object.
Embrechts, P., Klueppelberg, C., Mikosch, T. (1997). Modelling Extremal Events. Springer. Chapter 8, 413--429.
# NOT RUN {
# decluster the 200 exceedances of a particular threshold in
# the negative BMW data
data(bmw)
out <- pot(-bmw, ne = 200)
decluster(out$data, 30)
# }
Run the code above in your browser using DataLab