BiCopKPlot(VineCopula)
BiCopKPlot()所属R语言包：VineCopula
Kendall's plot (K-plot) for bivariate copula data
二元Copula的数据肯德尔的图（K-图）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function creates a Kendall's plot (K-plot) of given bivariate copula data.
这个函数创建了一个肯德尔的图（K-图）给定的二元Copula的数据。
用法----------Usage----------
BiCopKPlot(u1, u2, PLOT=TRUE, ...)
参数----------Arguments----------
参数：u1,u2
Data vectors of equal length with values in [0,1].
数据向量长度相等的值在[0,1]。
参数：PLOT
Logical; whether the results are plotted. If PLOT = FALSE, the values W.in and Hi.sort are returned (see below; default: PLOT = TRUE).
逻辑;结果是否绘制。如果PLOT = FALSE，值W.in和Hi.sort返回（见下文;默认：PLOT = TRUE）。
参数：...
Additional plot arguments.
其他图参数。
Details
详细信息----------Details----------
For observations u_{i,j}, i=1,...,N, j=1,2, the K-plot considers two quantities: First, the ordered values of the empirical bivariate distribution function H_i:=\hat{F}_{U_1U_2}(u_{i,1},u_{i,2}) and, second, W_{i:N}, which are the expected values of the order statistics from a random sample of size N of the random variable W=C(U_1,U_2) under the null hypothesis of independence between U_1 and U_2. W_{i:N} can be calculated as follows
的观测u_{i,j}, i=1,...,N, j=1,2, K-图认为两个量：首先，经验的二元分布函数H_i:=\hat{F}_{U_1U_2}(u_{i,1},u_{i,2})的有序值，第二，W_{i:N}，这是预期值的次序统计量从随机抽样的大小N随机变量的W=C(U_1,U_2)下的零假设之间的独立性U_1和U_2。 W_{i:N}可以如下计算
where
哪里
and k_0() is the corresponding density.
和k_0()相应的密度。
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 y=x, then U_1 and U_2 are approximately independent. Any deviation from the diagonal line points towards dependence. In case of positive dependence, the points of the K-plot should be located above the diagonal line, and vice versa for negative dependence. The larger the deviation from the diagonal, the stronger is the degree of dependency. There is a perfect positive dependence if points ≤ft(W_{i:N},H_i\right) lie on the curve K_0(ω) located above the main diagonal. If points (W_{i:N},H_i) however lie on the x-axis, this indicates a perfect negative dependence between U_1 and U_2.
K-图可以看出，二元Copula的QQ图。如果点的K-图位于约对角线上的y=x，那么U_1和U_2大约有独立的。任何偏离对角线指向依赖。在积极的依赖的情况下，点的K-曲线应位于上方的对角线，并且为负依赖反之亦然。从对角线的偏差越大，越强的依赖程度。有积极的依赖是一个完美的，如果点≤ft(W_{i:N},H_i\right)位于曲线K_0(ω)上方的主对角线。如果点(W_{i:N},H_i)但是趴在x轴，这表明一个完美的负相关性之间U_1和U_2。
值----------Value----------
参数：W.in
W-statistics (x-axis).
W-统计量（x轴）。
参数：Hi.sort
H-statistics (y-axis).
H-统计量（y轴）。
（作者）----------Author(s)----------
Natalia Belgorodski, Ulf Schepsmeier
参考文献----------References----------
Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12 (4), 347-368.
参见----------See Also----------
BiCopMetaContour, BiCopChiPlot, BiCopLambda, BiCopGofKendall
BiCopMetaContour，BiCopChiPlot，BiCopLambda，BiCopGofKendall
实例----------Examples----------
# Gaussian and Clayton copulas[高斯和Clayton Copula函数]
n = 500
tau = 0.5
# simulate from Gaussian copula[模拟高斯系词]
fam1 = 1
theta1 = BiCopTau2Par(fam1,tau)
dat1 = BiCopSim(n,fam1,theta1)
# simulate from Clayton copula[模拟克莱顿系词]
fam2 = 3
theta2 = BiCopTau2Par(fam2,tau)
dat2 = BiCopSim(n,fam2,theta2)
# create K-plots[创建K-图]
dev.new(width=10,height=5)
par(mfrow=c(1,2))
BiCopKPlot(dat1[,1],dat1[,2],main="Gaussian copula")
BiCopKPlot(dat2[,1],dat2[,2],main="Clayton copula")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:09:44
