
This function creates a Kendall's plot (K-plot) of given bivariate copula data.
BiCopKPlot(u1, u2, PLOT = TRUE, ...)
W-statistics (x-axis).
H-statistics (y-axis).
Data vectors of equal length with values in
Logical; whether the results are plotted. If PLOT = FALSE
, the values W.in
and Hi.sort
are returned (see below;
default: PLOT = TRUE
).
Additional plot arguments.
Natalia Belgorodski, Ulf Schepsmeier
For observations
K-plots can be seen as the bivariate copula equivalent to QQ-plots. If the
points of a K-plot lie approximately on the diagonal
Genest, C. and A. C. Favre (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12 (4), 347-368.
BiCopMetaContour()
, BiCopChiPlot()
,
BiCopLambda()
, BiCopGofTest()
## Gaussian and Clayton copulas
n <- 500
tau <- 0.5
# simulate from Gaussian copula
fam <- 1
par <- BiCopTau2Par(fam, tau)
cop1 <- BiCop(fam, par)
set.seed(123)
dat1 <- BiCopSim(n, cop1)
# simulate from Clayton copula
fam <- 3
par <- BiCopTau2Par(fam, tau)
cop2 <- BiCop(fam, par)
set.seed(123)
dat2 <- BiCopSim(n, cop2)
# create K-plots
op <- par(mfrow = c(1, 2))
BiCopKPlot(dat1[,1], dat1[,2], main = "Gaussian copula")
BiCopKPlot(dat2[,1], dat2[,2], main = "Clayton copula")
par(op)
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