decluster.runs {extRemes} | R Documentation |
Performs runs/intervals declustering.
decluster.runs(z, r) decluster.intervals(z, ei)
z |
Logical vector indicating which positions correspond to extreme values. |
r |
Integer run length. |
ei |
Estimate of the extremal index. |
Runs declustering: Extremes separated by fewer than `r' non-extremes belong to the same cluster. Setting `r' < 1 causes each extreme to form a separate cluster.
Intervals declustering: Extremes separated by fewer than `r' non-extremes belong to the same cluster, where `r' is the `nc'-th largest interexceedance time and `nc', the number of clusters, is estimated from the extremal index, `ei', and the times between extremes. Setting `ei' = 1 causes each extreme to form a separate cluster.
A list containing
scheme |
Name of declustering scheme. |
par |
Value of declustering parameter (i.e., run length). |
nc |
Number of clusters. |
size |
Vector of cluster sizes. |
s |
Vector of times of extremes. |
cluster |
Vector of numbers identifying clusters to which extremes belong. |
t |
Vector of times between extremes. |
inter |
Vector of intercluster time indicators (logical). |
intra |
Vector of intracluster time indicators (logical). |
Maintained by Eric Gilleland.
Chris Ferro
Smith RL (1989) Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Statistical Science 4, 367-393.
Ferro CAT and Segers J (2003) Inference for clusters of extreme values. Journal of the Royal Statistical Society B 65, 545-556.
# Simulate a dependent series of random variables. x <- runif(1000,-1,1) x[2:1000] <- x[1:999]*0.6 # -- DON'T RUN # pacf( x) # use runs and intervals declustering using the 90th percentile as the threshold. u <- quantile(x, 0.9) z <- x > u exi.intervals(z) tmp1 <- decluster.runs(z, 1) tmp2 <- decluster.intervals( z, exi.intervals(z))