View source: R/graph-chimeas.R
chimeas | R Documentation |
Provide two measures to assess for asymptotic dependence or independence
chimeas(data, u.range, n.u = 500, xlab, ylabs, ci = 0.95, boot = FALSE, n.boot = 250, block.size = 50, show.bound = TRUE, which = 1:2, ask = nb.fig < length(which) && dev.interactive(), ..., col.ci = "grey", col.bound = "blue", lty.ci = 1, lty.bound = 1)
data |
A matrix with 2 columns with the data. |
u.range |
Numeric vection of length 2 (may be missing): the range for the probabilities. |
n.u |
The number of probabilities to be considered |
xlab,ylabs |
The x-axis and ylabs labels. ylabs must be of length 2 |
ci |
The probability level for the confidence intervals |
boot |
Logical. If |
n.boot |
The number of bootstrap replicates. |
block.size |
The size of the “contiguous” blocks. See details. |
show.bound |
Logical. If |
which |
Which plot should be plotted? |
ask |
Logical. Should user be asked before each plot is computed? |
... |
Additional options to be passed to the |
col.ci,col.bound |
The color for the confidence intervals and theoretical bounds. |
lty.ci,lty.bound |
The line type for the confidence intervals and theoretical bounds. |
These two plots help us to understand the dependence relationship between the two data set. The sign of chi(u) determines if the variables are positively or negatively correlated. Two variable are asymptotically independent if lim_{u ~ 1} chi(u) = 0. For the independent case, chi(u) = 0 for all u in (0,1). For the perfect dependence case, chi(u) = 1 for all u in (0,1). Note that for a bivariate extreme value model, chi(u) = 2(1-A(0.5)) for all u in (0,1).
The measure chibar is only useful for asymptotically independent variables. Indeed, for asymptotically dependent variable, we have lim_{u ~ 1}chibar(u) = 1. For asymptotically independent variables, lim_{u ~ 1}chibar(u) reflects the strength of the dependence between variables. For independent variables, chibar(u)=0 for all u in (0,1).
If there is (short range) dependence between observations, users may
need to use bootstrap confidence intervals. Bootstrap series are
obtained by sampling contiguous blocks, of length l
say,
uniformly with replacement from the original observations. The block
length l
should be chosen to be much greater than the
short-range dependence and much smaller than the total number of
observations.
A graphic window.
Mathieu Ribatet
Coles, S., Heffernan, J. and Tawn, J. (1999) Dependence measures for extreme value analyses. Extremes 2 339–365.
tailind.test
, specdens
,
tsdep.plot
mc <- simmc(200, alpha = 0.9) mc2 <- simmc(100, alpha = 0.2) ##An independent case par(mfrow = c(1,2)) chimeas(cbind(mc[1:100], mc2)) ##Asymptotic dependence par(mfrow = c(1,2)) chimeas(cbind(mc[seq(1,200, by = 2)], mc[seq(2,200,by = 2)])) ##The same but with bootstrap ci par(mfrow = c(1,2)) chimeas(cbind(mc[seq(1,200, by = 2)], mc[seq(2,200,by = 2)]), boot = TRUE, n.boot=50)
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