Compute measures of extremal dependence for 2 variables.
chi(data, nq = 100, qlim = NULL, alpha = 0.05, trunc = TRUE)# S3 method for chi
summary(object, ...)
# S3 method for summary.chi
print(x, digits=3, ...)
# S3 method for chi
print(x, ...)
# S3 method for chi
plot(x, show=c("Chi"=TRUE,"ChiBar"=TRUE), lty=1,
cilty=2, col=1, spcases=TRUE, cicol=1, xlim=c(0, 1), ylimChi =
c(-1, 1), ylimChiBar = c(-1, 1), mainChi = "Chi", mainChiBar =
"Chi Bar", xlab = "Quantile", ylabChi =
expression(chi(u)), ylabChiBar = expression(bar(chi)(u)),
ask, ...)
# S3 method for chi
ggplot(data=NULL, mapping, xlab = "Quantile",
ylab=c("ChiBar" = expression(bar(chi)(u)), "Chi" = expression(chi(u))),
main=c("ChiBar" = "Chi Bar", "Chi" = "Chi"),
xlim = c(0, 1), ylim =list("Chi" = c(-1, 1),"ChiBar" = c(-1, 1)),
ptcol="blue",fill="orange",show=c("ChiBar"=TRUE,"Chi"=TRUE),
spcases = TRUE,plot., ..., environment)
An object of class chi containing the following.
Values of chi and their estimated upper and lower confidence limits.
Values of chibar and their estimated upper and lower confidence limits.
The quantiles at which chi and chi-bar were evaluated.
Upper and lower bounds for chi and chi-bar.
A matrix containing 2 numeric columns.
The number of quantiles at which to evaluate the dependence measures.
The minimum and maximum quantiles at which to do the evaluation.
The size of the confidence interval to be used. Defaults to
alpha = 0.05.
Logical flag indicating whether the estimates should be
truncated at their theoretical bounds. Defaults to trunc = TRUE.
An object of class chi.
Number of digits for printing.
Logical, of length 2, names "Chi" and "ChiBar". Defaults to
c("Chi" = TRUE, "ChiBar" = TRUE).
Line types and colours for the the estimated quantities and their confidence intervals.
Limits for the axes.
Main titles for the plots.
Axis labels for the plots.
Arguments to ggplot methods.
Whether or not to plot special cases of perfect (positive and
negative) dependence and indpenendence. Defaults to FALSE.
whether to plot to active graphics device.
Whether or not to ask before reusing the graphics device.
Further arguments to be passed to methods.
Janet E. Heffernan, Alec Stephenson, Harry Southworth
Computes the functions chi and chi-bar described by Coles, Heffernan and Tawn (1999). The limiting values of these functions as the quantile approaches 1 give an empirical measure of the type and strength of tail dependendce exhibited by the data.
A limiting value of ChiBar equal to 1 indicates Asymptotic Dependence, in which case the limiting value of Chi gives a measure of the strength of dependence in this class. A limiting value of ChiBar of less than 1 indicates Asymptotic Independence in which case Chi is irrelevant and the limiting value of ChiBar gives a measure of the strength of dependence.
The plot and ggplot methods show the ChiBar and Chi functions. In the case of the confidence interval for ChiBar excluding the value 1 for all of the largest quantiles, the plot of the Chi function is shown in grey.
S. Coles, J. E. Heffernan and J. A. Tawn, Dependence measures for extreme values analyses, Extremes, 2, 339 -- 365, 1999.
A. G. Stephenson. evd: Extreme Value Distributions, R News, 2, 2002.
MCS, rank
D <- liver[liver$dose == "D",]
chiD <- chi(D[, 5:6])
par(mfrow=c(1,2))
ggplot(chiD)
A <- liver[liver$dose == "A",]
chiA <- chi(A[, 5:6])
# here the limiting value of chi bar(u) lies away from one so the chi plot is
# not relevant and is plotted in grey
ggplot(chiA)
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