exi {evd} | R Documentation |
Estimates of the extremal index.
exi(data, u, r = 1, ulow = rep(-Inf, ncol(u)), rlow = rep(1, length(r)), dimnames = list(NULL, NULL), drop = TRUE)
data |
A numeric vector, which may contain missing values. |
u |
A numeric vector of thresholds, unless time varying
thresholds are used, in which case u should be a matrix,
typically with length(data) rows (or else the usual
recycling rules are applied). |
r |
A numeric vector of postive integers denoting the clustering interval lengths. |
ulow |
A numeric vector of lower thresholds with length
length(u) (if u is a vector) or ncol(u)
(if u is a matrix), unless time varying lower
thresholds are used, in which case ulow should be a
matrix with length(u) or ncol(u) columns, and
typically with length(data) rows (or else the
usual recycling rules are applied). By default there are no
lower thresholds (or equivalently, the lower thresholds are
-Inf ). |
rlow |
A numeric vector of postive integers, of length
length(r) , denoting the lower clustering interval
lengths. The lower clustering interval length is only
relevant if it is less than the clustering interval length
and if there exists a lower threshold (greater than
-Inf ). |
dimnames |
The dimnames of the result. |
drop |
Logical; return a vector if either u or
r has one element/column? |
The extremal index is estimated using the inverse of the
average cluster size, using the clusters of exceedences
derived from clusters
.
If the threshold is larger than (or equal to)
max(data)
, so that no clusters are derived, then
the corresponding estimate is NaN
.
A matrix with length(u)
(if u
is a vector) or
ncol(u)
(if u
is a matrix) rows and
length(r)
columns, such that the ij
th element
is the estimate of the extremal index using the threshold
u[i]
or u[,i]
, the clustering interval length
r[j]
, the lower threshold ulow[i]
or
ulow[,i]
, and the lower clustering interval length
rlow[j]
.
If drop
is TRUE
(the default), a numeric vector is
returned in preference to a matrix with one row/column.
data(portpirie) exi(portpirie, 4.2, 3, 3.8) us <- seq(3.9, 4.2, len = 10) exi(portpirie, us, 3) exi(portpirie, us, 1:3) tvu <- c(rep(4.2, 20), rep(4.1, 25), rep(4.2, 20)) exi(portpirie, as.matrix(tvu), 1:3) us <- seq(3.9, 4.2, len = 100) eis <- exi(portpirie, us, 1:3) matplot(us, eis, type = "l", xlab = "Threshold", ylab = "Ext. Index")