View source: R/graph-tsdepplot.R
tsdep.plot | R Documentation |
A diagnostic tool to assess for short range asymptotic dependence within a stationary time series.
tsdep.plot(data, u, ..., xlab, ylab, n.boot = 100, show.lines = TRUE, lag.max, ci = 0.95, block.size = 5 * lag.max, angle = 90, arrow.length = 0.1)
data |
The time series observations. |
u |
The threshold. |
... |
Optional arguments to be passed to the |
xlab,ylab |
The x and y-axis labels. |
n.boot |
Numeric. The number of replicates to compute the bootstrap confidence interval. |
show.lines |
Logical. If |
lag.max |
The maximum lag to be explored - may be missing. |
ci |
The level for the bootstrap confidence interval. The default is the 95% confidence interval. |
block.size |
The size for the contiguous bootstrap approach. |
angle |
The angle at the end of the error bar. If |
arrow.length |
The length to be passed in the function
|
Let X_t
be a stationary sequence of unit Frechet random
variables. By stationarity, the joint survivor function
Fbar_tau(.,.) of (X_t, X_{t+tau}) does not depend on t.
One parametric representation for Fbar_tau(.,.) is given by
Fbar_tau(s,s) = L_tau(x) s^{-1/eta_tau}
for some parameter eta_tau in (0,1] and a slowly varying function L_tau.
The Lambda_tau statistic is defined by
Lambda_tau = 2 eta_tau - 1
This statistic belongs to (-1,1] and is a measure of extremal dependence. Lambda_tau = 1 corresponds to asymptotic dependence, 0 < Lambda_tau < 1 to positive extremal association, Lambda_tau = 0 to “near” independence and Lambda_tau < 0 to negative extremal association.
This function plot the Lambda_tau statictics against the lag. Bootstrap confidence intervals are also drawn. The function returns invisibly this statistic and the confidence bounds.
Mathieu Ribatet
Ledford, A. and Tawn, J. (2003) Diagnostics for dependence within time series extremes. L. R. Statist. Soc. B. 65, Part 2, 521–543.
Ledford, A. and Tawn, J (1996) Statistics for near independence in multivariate extreme values. Biometrika 83 169–187.
chimeas
, tailind.test
##An independent case tsdep.plot(runif(5000), u = 0.95, lag.max = 5) ##Asymptotic dependence mc <- simmc(5000, alpha = 0.2) tsdep.plot(mc, u = 0.95, lag.max = 5)
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